News

  • 01/15/2024: Dr. Fang and Skylar Stolte's contributed publication, with collaborators, entitled "Artificial Intelligence-Optimized Non-Invasive Brain Stimulation and Treatment Response Prediction for Major Depression" has been published in Bioelectronic Medicine.
  • 09/24/2024: Congrats to SMILE Lab Ph.D. student Seowung Leem on winning the Best Poster Award at Graduate Pruitt Research and Alumni Engagement Day!
  • 09/06/2024: Dr. Fang's Insights Featured in National Academy of Science, Engineering, and Medicine Publication on AI and Neuroscience! [Link]
  • 09/06/2024: Congratulations to SMILE Lab Ph.D. Student Skylar Stolte for Winning the $1,000 CAM (Center for Cognitive Aging and Memory) Student Travel Award!
  • 08/26/2024: Congratulations to SMILE Lab Ph.D. Alumna Yao Xiao on starting a new position as an Assistant Professor at the Mayo Clinic.
  • 08/08/2024: Congrats to SMILE Lab Ph.D. student Joseph Cox on the acceptance of his first-author paper, "BrainSegFounder: Towards 3D Foundation Models for Neuroimage Segmentation," Link to the Medical Image Analysis Journal! [Link]
  • 07/26/2024: Congrats to SMILE Lab Ph.D. student Skylar Stolte on receiving the prestigious NIH F31 Ruth L. Kirschstein Predoctoral Individual National Research Service Award!
  • 06/17/2024: Dr. Fang has been selected to participate in the 2024-2025 class of the University of Florida Leadership Academy.
  • 06/08/2024: Congratulations to SMILE Lab Alumna Gianna Sweeting getting accepted in Meharry Medical College's MD program!
  • 05/14/2024: Dr. Fang gave an invited talk at the University of Tokyo and RIKEN, Japan on neuroscience-inspired AI and AI for brain health, hosted by Dr. Tatsuya Harada and Dr. Lin Gu.
  • 04/29/2024: Kudos to SMILE Lab PhD candidate Seowung Leem on his paper being accepted by IEEE EMBC as an oral presentation!
  • 04/18/2024: Congratulations to SMILE Lab PhD student Seowung Leem on receiving the IEEE EMBC NextGen Scholar Award from IEEE Engineering in Medicine and Biology Society (EMBS), 2024!
  • 04/08/2024: Congratulations to Skylar Stolte on being selected as 2023-2024 Attributes of a Gator Engineer Award Winners in Creativity!
  • 04/03/2024: Congratulations to SMILE Lab PhD student Joseph Cox on being selected as NIAAA T32 Fellow! [News]
  • 04/03/2024: Dr. Fang has been awarded Inaugural "AI Course Award". This award was created by the AI2 Center, the Center for Teaching Excellence (CTE), and the Center for Instructional Technology & Training (CITT) to honor UF instructors who have developed high-quality AI courses. She will be recognized at the Interface Conference on April 17, 2024. [News]
  • 04/02/2024: New paper entitled "DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction" has been accepted by Plos Computational Biology! Congrats to Chaoyue! [Paper]
  • 03/27/2024: Congratulations to SMILE Lab PhD student Skylar Stolte on winning the Cluff Aging Research Award!
  • 03/26/2024: New paper entitled "Neuron-Level Explainable AI for Alzheimer’s Disease Assessment from Fundus Images" is accepted by Nature Scientific Reports! [Link]
  • 03/21/2024: Congratulations to SMILE Lab undergraduates Amy Lazarte and Jason Chen on being selected as AI Scholars at the University Scholar Program (USP)!
  • 03/04/2024: Dr. Fang is invited to give a talk at the National Academy of Science (NAS)'s workshop titled Exploring the Bidirectional Relationship between Artificial Intelligence and Neuroscience, March 25-26, 2024 in Washington DC. [News]
  • 02/28/2024: Dr. Fang is named to the Class of 2024 Senior Members by National Academy of Inventors (NAI)! [News]
  • 02/27/2024: Congrats to SMILE Lab PhD student Chaoyue Sun on winning the Best Paper Award at HEALTHINF 2024 Conference!
  • 02/24/2024: SMILE Lab has a third paper accepted in 2024 in just two months entitled "Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects" by PLOS Computational Biology! Kudo to SMILE Lab postdoc associate Dr. Peng Liu! [Link]
  • 02/10/2024: SMILE Lab has a new paper entitled "Deep Learning Predicts Prevalent and Incident Parkinson’s Disease From UK Biobank Fundus Imaging" accepted by Nature Scientific Reports!
  • 01/08/2024: Congrats to Skylar Stolte on her first author journal paper entitled "Precise and Rapid Whole-Head Segmentation from Magnetic Resonance Images of Older Adults using Deep Learning" being accepted by Imaging Neuroscience!
  • 12/20/2023: A new paper is accepted by Journal of Visual Communication and Image Representation!
  • 12/01/2023: Welcome Diandra Ojo to join SMILE Lab as a Postdoc Associate!
  • 11/17/2023: A new published in Nature Portfolio Journal npj Digital Medicine on bias and diaparity of AI in diagosing women's health issue. [Paper] [News]
  • 10/24/2023: Dr. Fang gave an invited talk at the 3rd Annual AI Symposium at UF Health Cancer Center: Insights in Cancer Imaging on "AI for Medical Image Analysis 101". [Link]
  • 10/12/2023: Dr. Fang co-host with Dr. Parisa Rashidi a Special Session on "AI in Biomedical Engineering" at BMES 2023. Department chairs from University of Washington, Cornell, University of Florida, Boston University, Clemson University were invited to share thoughts on this panel discussion.
  • 10/11/2023: Congratulations to SMILE Lab PhD Candidate Skylar Stolte on placing 2nd in the Women in MICCAI (WiM) Inspirational Leadership Legacy (WILL) Initiative competition! [Link]
  • 10/09/2023: Dr. Fang is selected as Rising Stars (Engineering) to be recognized at the Academy of Science, Engineering, and Medicine of Florida (ASEMFL) Annual Meeting of November 3 & 4, 2023. [News][College News]
  • 10/09/2023: Dr. Fang becomes a MICCAI 2023 Mentor!
  • 10/08/2023: Dr. Fang presented at the Pre-MICCAI conference as Keynote Speaker in the University of British Columbia (UBC), Vancouver, Canada.
  • 09/01/2023: Welcome Peng Liu to join SMILE Lab as a Postdoc Associate!
  • 09/01/2023: Congratulations to our SMILE Lab Ph.D. student Daniel Rodriguez being selected to receive NIH T1D&BME T32 Fellowship! [News]
  • 08/18/2023: A new collaborative grant (DRPD-ROF2023) entitled "Learning optimal treatment strategies for hypotension in critical care patients with acute kidney injury using artificial intelligence" has been funded by the University of Florida!
  • 08/09/2023: Dr. Fang receives a new NSF award on brain-inspired AI entitled "NCS-FO: Brain-Informed Goal-Oriented and Bidirectional Deep Emotion Inference" as the PI, with Co-PI Dr. git gzhou and Andreas Keil. [News] [NSF Link]
  • 08/07/2023: Dr. Fang gave a talk at the MRI Research Institute (MRIRI), Weill Cornell Medical College, Cornell University on "Generative, Trustworthy, and Precision AI in Radiology".
  • 08/01/2023: Dr. Fang will serve as the President of Women in MICCAI, International Society of Medical Image Computing and Computer Assisted Intervention (MICCAI). [News]
  • 07/31/2023: Two papers from SMILE Lab PhD students Joseph Cox and Seowung Leem will be presented at BMES 2023 in Seattle, WA!
  • 06/26/2023: One paper entitled "DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability" is accepted by MICCAI 2023 in Vancouver, Canada. Kudos to Skylar!
  • 05/23/2023: New paper entitled "Machine-Learning Defined Precision tDCS for Improving Cognitive Function" is accepted by Brain Stimulation!
  • 05/17/2023: Editorial on "Frontiers of Women in Brain Imaging and Brain Stimulation" authored by Dr. Fang is published in the Frontiers of Human Neuroscience.
  • 05/16/2023: Dr. Fang gave an invited talk at the Nvidia Artificial Intelligence Technology Center (NVAITC) on "Trustworthy AI and Large Vision Models for Neuroimages".
  • 04/25/2023: Dr. Fang will serve as a mentor for Dr. Joseph Gullett who is awarded an NIH/NIA K23 on "Using Artificial Intelligence to Predict Cognitive Training Response in Amnestic Mild Cognitive Impairment".
  • 04/25/2023: A new collaborative R01 award entitled "Cognitively engaging walking exercise and neuromodulation to enhance brain function in older adults" is funded by NIH/NIA!
  • 04/19/2023: SMILE Lab PhD student Joseph Cox taught the AI Bootcamp of AI4Health Conference in Orlando.
  • 04/10/2023: Congratulations to SMILE Lab PhD Student Hong Huang on his work "Distributed Pruning Towards Tiny Neural Networks in Federated Learning" getting accepted by the 43rd IEEE International Conference on Distributed Computing Systems (ICDCS 2023) (rate=18.9%)!
  • 04/08/2023: Congrats to SMILE Lab alumna and Dream Engineering Team vice president Neeva Sethi on being one of the five inductees to the UF CLAS Hall of Fame!
  • 04/07/2023: Congrats to SMILE Lab PhD student Tianqi Liu on passing his PhD Proposal Defense!
  • 04/06/2023: Dr. Fang appears in the National Geographic story, "Your eyes may be a window into early Alzheimer's detection"! [News]
  • 04/06/2023: SMILE Lab received an Oracle for Research Cloud Starter Award!
  • 04/05/2023: SMILE Lab undergraduate researchers Grace Cheng and Akshay Ashok have been selected to receive UF AI Scholarship on its first offering! Congrats to Grace and Akshay! [News]
  • 03/31/2023: SMILE Lab is featured on the Forward & Up video by the College of Engineering. [Video]
  • 03/31/2023: Congratulations to SMILE Lab Alumnus Garrett Fullerton on receiving the prestigious National Science Foundation Graduate Research Fellowship Program (NSF-GRFP) grant!
  • 03/23/2023: Congratulations to SMILE Lab Alumna Neeva Sethi on receiving the prestigious Presidential Service Award at the University of Florida! [News]
  • 03/06/2023: Dr. Fang gave a talk at the Intelligence Critical Care Center (IC3) on "Artificial Intelligence for Cognitive Aging: Novel Diagnosis and Personalized Intervention".
  • 02/14/2023: Dr. Fang and SMILE Lab's research using HiperGator supercomputer for healthcare and humanity is featured by ABC Action News in the story "Supercomputer at the University of Florida is harnessing the awesome power of AI". [News]
  • 02/11/2023: Our journal paper entitled "DOMINO: Domain-aware loss for deep learning calibration" has been accepted by Software Impact. Kudos to Skylar! Code and pretrained models are available on GitHub and CodeOcean [Paper]
  • 01/10/2023: Dr. Fang and SMILE Lab Ph.D. student Skylar Stolte are featured by the Medical Imaging section of Computer Vision News on medical AI research. [Link]
  • 01/06/2023: Welcome two new Ph.D. students Zhuobiao Qiao and Yu Feng to join SMILE Lab!
  • 12/09/2022: Dr. Fang recieves Pruitt Family Endowed Faculty Fellow for 2023-2026! [News]
  • 12/02/2022: Dr. Fang delivered a keynote speech entitled "A Tale of Two Frontiers - When Brain Meets AI" at the Neural Information Processing System (NeurIPS) 2022 workshop on "Medical Imaging Meets NeurIPS" in New Orleans, Louisiana.
  • 11/11/2022: Dr. Fang gave an invited talk at College of Medicine, Stanford University on "Artificial Intelligence in Cognitive Aging and Brain-Inspired AI".
  • 11/01/2022: SMILE Lab PhD Candidate Skylar Stolte presented her work, DOMINO: Domain-aware Calibration in Medical Image Segmentation, at the [Fall 2022 HiPerGator Symposium] [News].
  • 10/25/2022: New collaborative paper entitled "Association of Longitudinal Cognitive Decline with Diffusion MRI in Gray Matter, Amyloid, and Tau Deposition" is accepted by Neurobiology of Aging!
  • 09/20/2022: Kudos to SMILE Lab PhD student Skylar Stolte for her Women in MICCAI's Best Paper Presentation Award Runner up! [News]
  • 08/24/2022: Congrats to SMILE Lab PhD Candidate Skylar Stolte on passing her dissertation qualification!
  • 08/20/2022: Welcome two new Masters students Everett Schwieg and Fan Yang to join SMILE Lab!
  • 01/06/2023: Welcome two new Ph.D. students Chaoyue Sun and Hong Huang to join SMILE Lab!
  • 08/10/2022: Congrats to SMILE Lab PhD student Skylar Stolte on her paper selected to be oral presentation (Oral rate=2.3%) at MICCAI 2022!
  • 08/09/2022: Kudos to SMILE Lab PhD student Seowung Leem's paper being accepted by the Annual Meeting of Society for Neuroscience (SfN) 2022!
  • 06/28/2022: Dr. Fang is awarded Tenure and Promotion to Associate Professor! [News]
  • 06/20/2022: Congratulations to SMILE Lab PhD student Skylar Stolte on receiving MICCAI Student Travel Award!
  • 06/07/2022: Dr. Fang gave an invited talk at the Gordan Research Conference (GRC) Image Science, Emerging Imaging Techniques at the Intersection of Physics and Data Science on the topic "From Zero to One: Physiology-Informed Deep Learning for Contrast-Free CT Perfusion in Stroke Care" in Newry, ME, United States.
  • 06/01/2022: Dr. Fang gave an invited talk at the Gordan Research Conference (GRC) System Aging, Systemic Processes, Omics Approaches and Biomarkers in Aging on the topic "Artificial Intelligence and Machine Learning for Cognitive Aging: Novel Diagnosis and Precision Intervention" in Newry, ME, United States.
  • 05/25/2022: New paper published in Frontiers in Radiology entitled "PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration". Congrats to recent SMILE Lab PhD graduate Peng Liu and undergraduate graduate Garrett Fullerton! [Link]
  • 05/23/2022: Welcome new Masters student Ayesha Naikodi to join SMILE Lab!
  • 05/12/2022: Welcome new Ph.D. student Joseph Cox to join SMILE Lab!
  • 05/05/2022: Our paper entitiled "DOMINO: Domain-aware Model Calibration in Medical Image Segmentation" is early accepted by MICCAI in Singapore Sep. 18-22, 2022 (early acceptance rate=13%)! Congrats to Skylar Stolte!
  • 05/03/2022: Dr. Fang was interview by ABC WCJB TV Tech Tuesday on her research using artificial intelligence (AI) to detect Alzheimer's Disesase. [Interview]
  • 04/14/2022: Congratulations to SMILE Lab member Garrett Fullerton on winning the Outstanding Research Award at the BME Undergraduate Research Day! [News]
  • 03/20/2022: Welcome new Ph.D. student Ziqian Huang to join SMILE Lab!
  • 03/11/2022: A new collaborative $10.7M P01 entitled "Multi-Scale Evaluation and Mitigation of Toxicities Following Internal Radionuclide Contamination" (PI: Gayle E. Woloschak) is funded by NIH NIAID!
  • 03/04/2022: Dr. Fang is selected to receive the Herbert Wertheim College of Engineering 2022 Faculty Award for Excellence in Innovation! [News]
  • 03/01/2022: Dr. Fang is appointed as the Associate Director in the UF Intelligent Critical Care Center (IC3).
  • 02/16/2022: Dr. Fang will serve as Topic Editor on "Women In Brain Imaging and Stimulation" with Frontiers in Human Neuroscience Journal. Welcome to submit to this exciting topic!
  • 02/12/2022: Dr. Fang will serve as Area Chair of the 25th International Conference of Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 in Singapore. Call for papers for this top conference in MIA!
  • 02/02/2022: Dr. Fang and collaborators joined forces with Nvidia scientists and OpenACCorg to accelerate brain science at the Georgia Tech GPU Hackathon. The StimulatedBrain AI@UF team drastically improved their processing time for evaluating datapoints collected from an individual brain. [News] [Twitter]
  • 01/18/2022: Dr. Fang delivers an invited talk entitled "Modular machine learning for Alzheimer's disease classification from retinal vasculature" at University of Oxford "Artificial Intelligence for Mental Health" seminar series.
  • 01/09/2022: Welcome new Ph.D. student Tianqi Liu to join SMILE Lab!
  • 01/07/2022: Dr. Fang is interviewed by Ivanhoe on her collaborative work on Aritifial Intelligence to prevent dementia. [News]
  • 01/04/2022: Dr. Fang gives an invited talk entitled "Artificial Intelligence for Cognitive Aging - Novel Diagnosis and Precision Intervention" at the University of Florida Institute of Aging.
  • 12/15/2021: Congrats to SMILE Lab PhD Dr. Peng Liu graduated!
  • 12/14/2021: Dr. Fang receives the BME Departmental Faculty Research Excellence Award! [News]
  • 12/07/2021: A new paper on Artificial Intelligence to predict dementia entitled "Baseline neuroimaging predicts decline to dementia from amnestic mild cognitive impairment" is published in Frontiers in Aging Neuroscience! [Paper] [News]
  • 11/30/2021: Kudos to SMILE PhD Peng Liu on winning the UNIQUE-IVADO Award for Best Abstract (Undergraduate and Graduate Level) at Montreal AI& Neuroscience (MAIN) Conference! [Video] [News]
  • 11/03/2021: Our lab's new AI paper entitled "Unraveling Somatotopic Organization in the Human Brain using Machine Learning and Adaptive Supervoxel-based Parcellations" is accepted by NeuroImage today! Kudos to Kyle See as a leading first author! [Link]
  • 10/29/2021: Our lab's new paper on AI in domain adaptation entitled "CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation" is published in NeuroComputing today! Kudos to Peng and Charlie! [Link]
  • 10/28/2021: Congratulations to SMILE Lab member Peng Liu on successfully defending his Ph.D. dissertation entitled "Biology and Neuroscience-Inspired Deep Learning"! Congrats, Dr. Liu!
  • 10/28/2021: New collaborative paper on "Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks" published in Theoretical and Computational Fluid Dynamics! [Link]
  • 10/22/2021: New publication on AI in computational fluid dynamics entitled "Rotational and Reflectional Equivariant Convolutional Neural Network for data-limited applications: Multiphase Flow demonstration" in the Journal of Physics of Fluids is now online! [Link]
  • 10/09/2021: Dr. Fang will serve as session chair for Optical Imaging Session of the Annual Meeting of Biomedical Engineering Society (BMES) on Oct. 6-9, 2021 at Orlando, FL.
  • 09/30/2021: A new collaborative grant entitled "Creation of an intelligent alert to improve efficacy & patient safety in real time during fluoroscopic guided lumbar transforaminal epidural steroid injection" is funded by the I. Heermann Anesthesia Foundation.
  • 09/30/2021: Dr. Fang has received an Oracle Research Project Award on "Explainable artificial intelligence for Alzheimer’s Disease Assessment from Retinal Imaging".
  • 09/29/2021: Dr. Fang serves as the session chair of Image Reconstruction Session at MICCAI 2021.
  • 09/21/2021: A new 5-year U24 grant ($2.5M) entitled "Southern HIV and Alcohol Research Consortium Biomedical Data Repository" is funded by NIH NIAAA!
  • 09/10/2021: A new 5-year P01 grant ($6.6M) entitled "Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection" is funded by NIH NIAAA! [News]
  • 09/09/2021: A new 4-year SCH grant ($1.2M) entitled "Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases" is funded by National Science Foundation! Dr. Fang is Co-PI on this Trustworthy and Explanable Artificial Intelligence (AI) award. [NSF] [News]
  • 09/09/2021: A new 5-year P01 grant ($1.5M to UF) entitled "SHARE Program: Innovations in Translational Behavioral Science to Improve Self-management of HIV and Alcohol Reaching Emerging adults" is funded by NIH NIAAA!
  • 08/27/2021: Welcome two new Ph.D. students Charlie Tran and Seowung Leem to join SMILE Lab!
  • 08/26/2021: Three abstracts are accepted by Society of Neuroscience (SfN) Annual Meeting to be held November 8-11 virtual and November 13-16, 2021 in Chicago:
    • Emergence of emotion selectivity in deep neural networks trained to recognize visual objects
    • A deep neural network model for emotion perception
    • Machine learning defined precision tES for improving cognitive function in older adults
    Congrats to Peng and Alejandro!
  • 08/24/2021: A new paper entitled "Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks" is accepted by Theoretical and Computational Fluid Dynamics!
  • 08/23/2021: Welcome two new Ph.D. students Charlie Tran and Seowung Leem to join SMILE Lab!
  • 08/17/2021: Kudos to SMILE Lab PhD student Charlie Tran who has been selected into the highly competitive NextProf Pathfinder 2021 to be held during October 17-19, 2021 on the campus of University of Michigan, Ann Arbor, co-sponsored by the University of California, San Diego. Congrats Charlie! [News]
  • 08/11/2021: Congratulations to SMILE lab undergraduate student Gianna Sweeting on being selected to receive the John & Mittie Collins Engineering Scholarship from Herbert Wertheim College of Engineering (HWCOE)! [News]
  • 07/30/2021: A new collaborative 5-Year R01 grant ($2.3M) entitled "Acquisition, extinction, and recall of attention biases to threat: Computational modeling and multimodal brain imaging" has been funded by NIH NIMH!
  • 07/15/2021: Our AI research on precision dosing for preventing dementia has been reported by UF News, WPLG Local 10 News (ABC-affiliated local TV), WCJB TV (ABC/CW+ affiliated local), and The Alligator.
  • 06/14/2021: Dr. Fang presented as an invited speaker at the Cleveland Clinic-National Science Foundation Workshop "The Present and the Future of Artificial Intelligence in Biomedical Research".
  • 06/04/2021: NeuroAI T32 Machine Learning Workshop taught by Dr. Fang has successfully completed! [News]
  • 05/14/2021: New 4-Year MPI (Fang & Woods) RF1 grant ($2.9M) on Artificial Intelligence for transcranial direct current stimulation (tDCS) in remediating cognitive aging has been funded by NIH! [News]
  • 05/02/2021: Congrats to SMILE PhD student Skylar Stolte on passing her Doctoral Comprehensive Exam!
  • 04/07/2021: Congratulations to SMILE undergraduate student and incoming Ph.D. student Charlie Tran on receiving the McNaire Graduate Assistantship!
  • 03/18/2021: New 5-year ($5M) NIH U01 grant on New AI tool to improve diagnosis of Parkinson’s and related disorders. [News]
  • 03/01/2021: Congratulations to SMILer undergraduate Gianna Sweeting for being selected by the Univerity Scholar Program!
  • 02/26/2021: Dr. Fang's work AI in Parkinson's Disease dignosis via eye scans has been reported by The Washington Post.
  • 02/25/2021: Congratulations to SMILer undergraduate Gianna Sweeting for being selected as a Fernandez Family Scholar by the Herbert Wertheim College of Engineering for her excellent progress and interest in research and graduate studies. [News][Twitter]
  • 02/18/2021: Dr. Fang has been selected as Aspiring PI to attend 2021 NSF SCH PI Workshop: Smart Health in the AI and COVID Era.
  • 02/18/2021: SMILE lab PhD student Peng Liu has been selected by NSF to attend the 2021 NSF SCH PI Workshop: Smart Health in the AI and COVID Era.
  • 02/2021: Dr. Fang will serve as Track Chair for BMES 2021
  • 02/2021: Dr. Fang will serve as Program Committee for MICCAI 2021.
  • 01/25/2021: Congrats to Kyle See and Rachel Ho whose joint paper on "TL1 Team Approach to Predicting Response to Spinal Cord Stimulation for Chronic Low Back Pain" has been accepted by Translational Science 2021!
  • 12/2020: Dr. Fang, together with Dr. Mingzhou Ding and Dr. Andreas Keil have been awarded UF Research Artificial Intelligence Research Catalyst Fund on the project "VCA-DNN: Neuroscience-Inspired Artificial Intelligence for Visual Emotion Recognition"! [Link]
  • 12/15/2020: Our paper on "Modular machine learning for Alzheimer's disease classification from retinal vasculature" has been accepted by Nature Scientific Reports! [Link]
  • 12/2020: Dr. Fang has been interviewed by Forbes, RSNA, Diagnostic Imaging on her research about AI for Parkinson's Disease Diagnosis via Eye Exam.
  • 11/2020: Dr. Fang is interviewed by UFII on AI for brain functions. [Link]
  • 10/30/2020: Paper published in Brain Simulation on Machine learning and individual variability in electric field characteristics predict tDCS treatment response". [PDF]
  • 10/14/2020: Congratulations to SMILer Gianna Sweeting on receving the Engineering College Scholarship from the Herbert Wertheim College of Engineering (HWCOE)! [News]
  • 09/04/2020: Congratulations to BME Alumna Yao Xiao Ph.D on receiving the prestigious 2020 BMES Career Development Award! [News]
  • 08/20/2020: Welcome new Ph.D. student Skylar Stolte to join SMILE Lab!
  • 08/05/2020: Congrats on Skylar Stolte on her abstract entitled "Artificial Intelligence For Characterizing Heart Failure In Cardiac Magnetic Resonance Images" being accepted by American Heart Association Scientific Sessions 2020!
  • 07/16/2020: Our paper on "Physiological wound assessment from coregistered and segmented tissue hemoglobin maps" is published at Journal of the Optical Society of America A. It is a collaborative work with Dr. Anuradha Godavarty at Florida International University. [Paper]
  • 07/06/2020: Congratulaions to SMILE Member PhD student Peng Liu on receving UFII Graduate Student Fellowship on his project on Neuroscience-Inspired Artificial Intelligence![News]
  • 06/25/2020: Yao Xiao presented at the 2020 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM) on 'Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network'! [Link]
  • 06/12/2020: SMILer Kyle B. See is highlighted in BME Student Spotlight! [News]
  • 05/30/2020: Congratulations to Skylar Stolte for her paper, 'A Survey on Medical Image Analysis in Diabetic Retinopathy', being accepted for publication in Medical Image Analysis! [Link]
  • 05/21/2020: Congratulations to Kyle See for passing the PhD Departmental Comprehensive Exam!
  • 05/04/2020: Garrett Fullerton and Simon Kato have been selected for NSF REU.
  • 04/05/2020: Yao Xiao presented at the IEEE International Symposium on Biomedical Imaging (ISBI'20) on 'Transfer-GAN: Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Networks'! [Paper]
  • 04/30/2020: Yao Xiao has been selected as a Graduate Student Commencement Speaker at the College of Engineering Graduation Ceremony. [News] [Speech beings at 16:28]
  • 04/07/2020: SMILer Charlie Tran has been selected to participate in the University of Florida's Ronald E. McNair Post-Baccalaureate Achievement Program. [News]
  • 04/01/2020: SMILE Lab Alumnus Daniel El Basha has been awarded the NSF Graduate Research Fellowship! [News]
  • 03/25/2020: Yao Xiao on successfully defending her PhD dissertation!
  • 02/25/2020: SMILer Garret Fullerton has been accepted into UF University Scholars Program to work on machine learning for medical image optimization. [News]
  • 02/17/2020: Yao Xiao presented at the SPIE Medical Imaging (SPIE MI'20) on 'Transfer generative adversarial network for multimodal CT image super-resolution'! [Talk]
  • 02/07/2020: Congrats to Yao Xiao for her abstract, 'Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network', being accepted for presenting at the 2020 Annual Meeting of the Society for Imaging Informatics in Medicine (SIIM)! [Paper]
  • 01/06/2020: Congrats to Yao Xiao for her paper, 'Transfer-GAN: Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Networks', being accepted for publication in the IEEE International Symposium on Biomedical Imaging (ISBI'20), and awarded the ISBI (NIH, NIBIB, NCI-funded) Student Travel Award, UF GSC Student Travel Award! [Paper]
  • 12/04/2019: Dr. Fang presented at the Annual Conference of Radiological Society of North America (RSNA) on “Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Network” with Ph.D. student Yao Xiao as the leading author. [Link]
  • 10/16/2019: Congrats to Yao Xiao for her abstract, 'Transfer generative adversarial network for multimodal CT image super-resolution', being accepted for presenting at the SPIE Medical Imaging (SPIE MI'20)! [Paper]
  • 09/09/2019: Dr. Fang receives an NSF award entitled "III:Small: Modeling Multi-Level Connectivity of Brain Dynamics". [News]
  • 08/30/2019: Congrats to Peng Liu on passing his dissertation qualification!
  • 08/26/2019: Kudos to Peng Liu and Dr. Fang for their first patent at UF. A Software Using a Genetic Algorithm to Automatically Build Convolutional Neural Networks for Medical Image Denoising! [News]
  • 08/20/2019: Welcome new Ph.D. student Kyle See to join SMILE Lab!
  • 08/20/2019: Congrats to our STTP students on their fantastic project presentations!
  • 07/26/2019: 'Development and Validation of Automated Imaging Differentiation in Parkinsonism: A Multi-Site Machine Learning Study' accepted for publication in The Lancet Digital Health. Congrats to SMiLE Lab and Dr. David Vaillancourt!
  • 08/20/2019: Two SSTP high school students Imaan Randhawa and Aarushi Walia successfully completed their summer research in SMiLE Lab under the supervision of Dr. Fang and PhD student Yao Xiao.
  • 06/28/2019: Dr. Fang recieves UF Informatics Institute Seed Fund Grant for research in smartphone based Diabetic Retinopath detection. [News]
  • 06/13/2019: Kyle See is awarded an NIH CTSI TL1 Predoctoral Fellowship to study methods and mechanisms of cognition and motor control through data science. [News]
  • 05/10/2019: Dr. Fang receives collaborative CTSI Pilot Award to study cancer therapy-induced cardiotoxicity. [News]
  • 05/07/2019: Congrats to Yao Xiao for successfully defending her proposal
  • 04/16/2019: Congrats to Peng Liu for his paper ,'Deep Evolutionary Networks with Expedited Genetic Algorithms for Medical Image Denoising', being accepted for publication in Medical Image Analysis! [News]
  • 04/16/2019: Kudo to Yao Xiao for her paper, 'STIR-Net: Spatial-Temporal Image Restoration Net for CTPerfusion Radiation Reduction', being accepted for publication in Frontiers in Neurology, section Stroke. [News]
  • 04/16/2019: SMiLE Lab's Senior Design group presents their final project for Smartphone Based Diabetic Retinopathy Diagnosis.
  • 03/07/2019: Maximillian Diaz accepted to UF University Scholars Program to work on retina based Parkinson's diagnosis. [News]
  • 01/10/2019: Dr. Fang named a senior member of IEEE. [News]
  • 05/17/2018: Dr. Fang awarded University of Florida Informatics Institute and the Clinical and Translational Science Institute (CTSI) pilot funding for precision medicine. [News]
  • 04/24/2018: SMiLE Lab recieves two first place awards at the Diabetic Retinopathy Segmentation and Grading Challenge. [News]
  • 03/26/2018: Akash and Akshay Mathavan accepted to UF University Scholars Program to work on environmental risk factors for ALS. [News]
  • 10/30/2017: Two master students, Yangjunyi Li and Yun Liang, join SMILE Group. Welcome!
  • 10/01/2017: Two UF BME senior students Kyle See and Daniel El Basha join SMILE lab to work on big biomedical data anlytics research supported by NSF REU. Welcome! [News]
  • 08/2017: Yao is awarded MICCAI 2017 Student Travel Award. Congrats to Yao! [News]
  • 08/2017: Four papers from SMILE Lab will be presented at BMES 2017 in Phoenix, Arizona.
  • 08/2017: SMILE Lab will move to J. Crayton Pruitt Family Department of Biomedical Engineering at University of Florida.
  • 07/2017: One paper is accepted by Machine Learning and Medical Imaging (MLMI) Workshop at MICCAI 2017. Congrats to Yao!
  • 07/16/2017: Dr. Fang is invited to give a talk at the 4th Medical Image Computing Seminar (MICS) held at Shanghai Jiaotong University, Shanghai, China.
  • 06/2017: Yao won the NSF Travel Award to attend CHASE 2017 in Philadelphia, PA. Congrats to Yao! [News]
  • 05/2017: Dr. Fang is invited by NSF as a panelist of Smart and Connect Health.
  • 05/2017: One paper is accepted by CHASE 2017. Congrats to Yao!
  • 04/2017: One paper is accepted by MICCAI 2017. Congrats to Ling!
  • 04/2017: Dr. Fang is invited to give a talk at the Society for Brain Mapping and Therapeutics Annual Meeting in Los Angeles, CA.
  • 03/2017: Dr. Fang attended NSF Smart and Connected Health PI Meeting held in Boston, MA.
  • 10/2016: PhD student Maryamossadat Aghili has been awarded travel grants from NIPS WiML 2016 Program Committee and FIU GPSC to attend WiML of NIPS 2016 in Barcelona.
  • 09/2016: Dr. Fang is invited by NIH to review in BDMA study section.
  • 09/2016: One paper is accepted by Pattern Recognition!
  • 08/2016: Dr. Fang is selected as an Early Career Grant Reviewer by NIH.
  • 08/2016: SMILE REU and RET students and teachers won the 2016 Best REU and RET Poster Awards at SCIS of FIU.
  • 07/2016: Our paper "Abdominal Adipose Tissues Extraction Using Multi-Scale Deep Neural Network" is accepted by NeuroComputing!
  • 04/2016: PhD student Maryamossadat Aghili has been awarded travel grants to attend Grad Cohort Conference (CRAW).
  • 05/2016: Dr. Fang's research appeared on FIU News: Professor uses computer science to reduce patients' exposure to radiation from CT scans!
  • 05/2016: Dr. Fang has been selected to receive the 2016 Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities!
  • 05/2016: Dr. Fang received funding from NSF on CRII (Pre-CAREER) Award on "Characterizing, Modeling and Evaluating Brain Dynamics"!
  • 04/2016: Our paper "TENDER: TEnsor Non-local Deconvolution Enabled Radiation Reduction in CT Perfusion" is accepted by NeuroComputing!
  • 04/09/2016: Dr. Fang is awarded the first Robin Sidhi Memorial Young Scientist Award from the Annual Congress of Society of Brain Mapping and Therapeutics for recognizing her continuous devotion to bridge information technology and health informatics.
  • 03/2016: Our paper ""Computational Health Informatics in the Big Data Age: A Survey" is accepted by the prestigous ACM Computing Survey!
  • 01/2016: Dr. Fang is invited by NSF as a panel reviewer of Smart and Connect Health.
  • 12/2015: Dr. Fang serves as the Publicity Chair of the 14th IEEE International Conference on Machine Learning and Applications 2015.
  • 04/2015: Dr. Fang is invited by NSF as a panel reviewer of Smart and Connect Health.

Honors & Distinctions

  • 2024
    University of Florida Research Computing
  • 2024
    University of Florida
  • 2024
    National Academy of Inventors (NAI)
  • 2024
    The 17th International Conference on Health Informatics (HEALTHINF/BIOSTEC), Italy, Rome
  • 2023
    Academy of Science, Engineering and Medicine of FL (ASEMFL)
  • 2022
  • 2022
  • 2022
    Herbert Wertheim College of Engineering, University of Florida
  • 2021
    J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida
  • 2020
    NVIDIA-University of Florida
  • 2020
    University of Florida Center for Teaching Excellence
  • 2020
    For the highest-scoring abstract, Co-Authored Paper
    American Society for Clinical Pharmacology and Therapeutics Annual Meeting in Houston, TX, March 18-21, 2020.
  • 2018
  • 2018
    Top-ranked (#1) in “Fovea Detection” System, First International Diabetic Retinopathy Grading and Segmentation Challenge
    IEEE International Symposium of Biomedical Imaging
  • 2018
    Top-ranked (#1) in “Optical Disc Segmentation” System, First International Diabetic Retinopathy Grading and Segmentation Challenge
    IEEE International Symposium of Biomedical Imaging
  • 2017
    Association for Computing Machinery (ACM)
  • 2017
    Robin Sidhu Memorial Young Scientist Award
    Society of Brain Mapping and Theurapeutics
    Covered by PR Newswire, Yahoo, NEWS
  • 2016
    NSF Best REU and RET Poster Awards
    image
    Dr. Fang's mentee, Paul Naghshineh from George Washington University, won the Best REU Poster Award at the annual REU Symposium at FIU. Christian McDonald with Edda I. Rivera, teachers from Miami Jackson Senior High School and John A. Ferguson Senior High School, under Dr. Fang's mentorship, won the Best RET Poster Award. Congratulations to Paul, Christian and Edda!
  • 2016
    Ralph E. Powe Junior Faculty Enhancement Award
    Oak Ridge Associated Universities
    image
    Dr. Fang has been selected to receive the 2016 Ralph E. Powe Junior Faculty Enhancement Award from Oak Ridge Associated Universities (two nominations from each institute, 35 awardees out of 132 applicants).
  • 2016
    NSF IIS CRII Award on "Characterizing, Modeling and Evaluating Brain Dynamics"
    image
  • 2015
    CISE CAREER Workshop Travel Award
    National Science Foundation
  • 2014
    Hsien Wu and Daisy Yen Wu Memorial Award
    Cornell University
    image
    In recognition of the excellent progress in the academic program and high potential for a successful academic career (5 awardees out of all graduate students at Cornell University).
  • 2012
    ECE Women's Conference Travel Grant
    IBM-Cornell University
  • 2010
    Best Paper Award
    IEEE International Conference on Image Processing (ICIP)
    image
    Best Paper Award at the 17th International Conference on Image Processing, 2010. (Top 1 out of 1190 accepted papers, first author publication)
  • 2010
    Best PhD Poster Award, Cornell Engineering
    Cornell Engineering Research Conference
  • 2009
    Irwin and Joan Jacobs Fellowship
    Cornell University
    image
    Awarded to students who exemplify strength and potential in academics, service, and leadership, 2009-2010

Grants & Awards

  • 2023
    DRPD-ROF2023: Learning optimal treatment strategies for hypotension in critical care patients with acute kidney injury using artificial intelligence
    Funding: University of Florida
    Amount: $86,500
    Role: Co-I (PI: Tezcan Ozrazgat Baslanti, Co-I: Jessica Ray)
  • 2023
    NCS-FO-2318984: Brain-Informed Goal-Oriented and Bidirectional Deep Emotion Inference
    Funding: National Science Foundation, Integrative Strategies for Understanding Neural and Cognitive Systems (NCS)
    Amount: $920,000
    Role: Principal Investigator
  • 2023
    R01: Cognitively engaging walking exercise and neuromodulation to enhance brain function in older adults
    Funding: National Institute of Health, National Institute of Aging (NIA)
    Amount: $4.8M
    Role: Co-I (PI: David Clark, University of Florida)
  • 2022
    P01AI165380: Multi-Scale Evaluation and Mitigation of Toxicities Following Internal Radionuclide Contamination
    Funding: National Institute of Health, National Institute of Allergy and Infectious Diseases (NIAID)
    Amount: $10.7M ($1.95M to UF)
    Role: Co-I (PI: Gayle E. Woloschak, Northwestern University)
  • 2021
    RF1: Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study (R01-equivalent)
    Funding: National Institute of Health, National Institute of Aging (NIA)
    Amount: $2.9M
    Role: Principal Investigator (MPI: Ruogu Fang & Adam Woods)
  • 2021
    Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases
    Funding: National Science Foundation, Smart and Connected Health [Link]
    Amount: $1.2M ($840K to UF)
    Duration: 2021-2025
    Role: Co-Principal Investigator (PI: My Thai)
  • 2021
    U24AA029959: Southern HIV and Alcohol Research Consortium Biomedical Data Repository
    Funding: National Institute of Health, National Institute on Alcohol Abuse and Alcoholism (NIAAA)
    Amount: $2.5M
    Role: Co-Investigator (PI: Samuel Wu, Robert Cook)
  • 2021
    P01AA029547: SHARE Program: Innovations in Translational Behavioral Science to Improve Self- management of HIV and Alcohol Reaching Emerging adults
    Funding: National Institute of Health, National Institute on Alcohol Abuse and Alcoholism (NIAAA)
    Amount: $3M
    Role: Co-Investigator (PI: Samuel Wu)
  • 2021
    P01AA029543: Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection
    Funding: National Institute of Health, National Institute on Alcohol Abuse and Alcoholism (NIAAA)
    Amount: $6.6M
    Role: Co-Investigator (PI: Robert Cook)
  • 2021
    R01: Acquisition, extinction, and recall of attention biases to threat: Computational modeling and multimodal brain imaging
    Funding: National Institute of Health, National Institute of Mental Health (NIMH)
    Amount: $2.3M
    Role: Co-Investigator (MPI: Mingzhou Ding & Andreas Keil)
  • 2021
    U01: Web-based Automated Imaging Differentiation of Parkinsonism
    Funding: National Institute of Health, National Institute of Neurological Disorders and Stroke (NINDS)
    Amount: $5.2M
    Role: Co-investigator (PI: David Vaillancourt)
  • 2020
    Artificial Intelligence Catalyst Award: VCA-DNN: Neuroscience-Inspired Artificial Intelligence for Visual Emotion Recognition
    Funding: NVIDIA - Universit of Florida
    Amount: $50,000
    Role: Principal Investigator (Co-PI: Mingzhou Ding)
  • 2020
    UFII: Biology and Cognition Inspired Deep Learning
    Funding: University of Florida
    Role: Mentor (Mentee: Peng Liu)
  • 2019
    III: Small: Modeling Multi-Level Connectivity of Brain Dynamics
    Funding: National Science Foundation
    Amount: $823,000
    Role: Principal Investigator (Co-PI: Mingzhou Ding)
  • 2019
    UFII Junior SEED Award: Multimodal Visual-Text Learning from Clinical Narrative and Image for Early Detection of Diabetic Retinopathy
    Funding: University of Florida
    Amount: $40,000
    Role: Co-Principal Investigator (PI: Yonghui Wu)
  • 2019
    UFII-CTSI Pilot Award: Toward prevention of cardiotoxicity in cancer: a multimodal approach leveraging genomics, images and clinical data
    Funding: University of Florida
    Amount: $60,000
    Role: Co-Principal Investigator (PI: Yan Gong)
  • 2019
    NIH CTSI TL1: Predicting short-term and long-term effects of spinal cord stimulation: implications for clinical practice
    Funding: University of Florida
    Role: Mentor (Mentee: Kyle B. See)
  • 2018
    UFII-CTSI Pilot Award: PrecisionDose:Personalized Radiation Dose Optimization for Multimodal Imaging
    Funding: University of Florida
    Amount: $75,000
    Role: Principal Investigator
  • 2018
    Phase I IUCRC University of Florida: Center for Big Learning
    Funding: National Science Foundation (NSF Abstract)
    Amount: $1,050,478
    Role: Senior Personnel (PI: Dapeng Wu)
  • 2016
    Ralph E. Powe Junior Faculty Enhancement Award
    Funding: Oak Ridge Associated Universities (ORAU)
    Amoung: $10,000
    Role: Principal Investigator
  • 2016
    CRII: Characterizing, Modeling and Evaluating Brain Dynamics
    Funding: National Science Foundation
    Amount: $190,991
    Role: Principal Investigator
    image

    Brain dynamics, which reflects the healthy or pathological states of the brain with quantifiable, reproducible, and indicative dynamics values, remains the least understood and studied area of brain science despite its intrinsic and critical importance to the brain. Unlike other brain information such as the structural and sequential dimensions that have all been extensively studied with models and methods successfully developed, the 5th dimension, dynamics, has only very recently started receiving systematic analysis from the research community. The state-of-the-art models suffer from several fundamental limitations that have critically inhibited the accuracy and reliability of the dynamic parameters' computation. First, dynamic parameters are derived from each voxel of the brain spatially independently, and thus miss the fundamental spatial information since the brain is connected? Second, current models rely solely on single-patient data to estimate the dynamic parameters without exploiting the big medical data consisting of billions of patients with similar diseases.

    This project aims to develop a framework for data-driven brain dynamics characterization, modeling and evaluation that includes the new concept of a 5th dimension – brain dynamics – to complement the structural 4-D brain for a complete picture. The project studies how dynamic computing of the brain as a distinct problem from the image reconstruction and de-noising of convention models, and analyzes the impact of different models for the dynamics analysis. A data-driven, scalable framework will be developed to depict the functionality and dynamics of the brain. This framework enables full utilization of 4-D brain spatio-temporal data and big medical data, resulting in accurate estimations of the dynamics of the brain that are not reflected in the voxel-independent models and the single patient models. The model and framework will be evaluated on both simulated and real dual-dose computed tomography perfusion image data and then compared with the state-of-the-art methods for brain dynamics computation by leveraging collaborations with Florida International University Herbert Wertheim College of Medicine, NewYork-Presbyterian Hospital / Weill Cornell Medical College (WCMC) and Northwell School of Medicine at Hofstra University. The proposed research will significantly advance the state-of-the-art in quantifying and analyzing brain structure and dynamics, and the interplay between the two for brain disease diagnosis, including both the acute and chronic diseases. This unified approach brings together fields of Computer Science, Bioengineering, Cognitive Neuroscience and Neuroradiology to create a framework for precisely measuring and analyzing the 5th dimension – brain dynamics – integrated with the 4-D brain with three dimensions from spatial data and one dimension from temporal data. Results from the project will be incorporated into graduate-level multi-disciplinary courses in machine learning, computational neuroscience and medical image analysis. This project will open up several new research directions in the domain of brain analysis, and will educate and nurture young researchers, advance the involvement of underrepresented minorities in computer science research, and equip them with new insights, models and tools for developing future research in brain dynamics in a minority serving university.

Education

  • Ph.D.

    Ph.D. in Electrical and Computer Engineering

    Cornell University, Ithaca, NY.

  • B.E.

    B.E. in Information Engineering

    Zhejiang University, Hangzhou, China

Professional Service (Selected)

  • President 2023-Present

    Women in MICCAI (WiM)

    Medical Image Computing and Computer Assisted Intervention (MICCAI) Society

  • Associate Editor

    Medical Image Analysis

  • Area Chair 2022-Present

    MICCAI Conference

    The International Conference on Medical Image Computing and Computer Assited Intervention (MICCAI)

  • Reviewer

    The Lancet, Nature Machine Intelligence, Science Advances, IEEE TPAMI, IEEE TNNLS, MIA, IEEE TMI, IEEE TIP

Academic Positions

  • BME

    Tenured Associate Professor

    University of Florida J. Crayton Pruitt Family Department of Biomedical Engineering

  • IC3

    Associate Director

    University of Florida UF Intelligent Critical Care Center (IC3)

  • ECE

    Affliated Faculty

    University of Florida Department of Electrical and Computer Engineering

  • COM

    Affiliated Faculty

    University of Florida Department of Radiology, College of Medicine

  • CISE

    Affiliated Faculty

    University of Florida Department of Computer & Information Science & Engineering

  • CAM

    Affiliated Faculty

    University of Florida The Center for Cognitive Aging and Memory Clinical Translational Research (CAM)

  • UFGI

    Affiliated Faculty

    University of Florida Genetics Institute (UFGI)

  • UFCC

    Affiliated Faculty

    University of Florida UF Health Cancer Center

  • EPI

    Affiliated Faculty

    University of Florida Emerging Pathogens Institute

Acknowledgements

  • Our work is supported by:

    image image image image image image image image image image image image

Current Members

Postdoc Researchers

Peng Liu

Postdoc, 2023-Now

Ph.D. in Biomedical Engineering, University of Florida, 2021
Postdoc, Psychology and Brain Science, DartMouth College, 2022-2023
Postdoc, University of Florida, 2023-Now

Wasif Khan

Postdoc, Incoming 2024

Postdoc, University of Florida, Incoming 2024

Ph.D. Students

Skylar Stolte

Ph.D. Candidate, 2020-Now

Biomedical Engineering

NIH F31 Fellow, UF Graduate Student Preeminence Award, Attributes of a Gator Engineer Award

Seowung Leem

Ph.D. Candidate, 2021-Now

Biomedical Engineering

Joseph Cox

Ph.D. Student, 2022-Now

Biomedical Engineering

NIH NIAAA T32 Fellow

Chaoyue Sun

Ph.D. Candidate, 2020-Now

Electrical and Computer Engineering

Zhuobiao Qiao

Ph.D. Student, 2021-Now

Electrical Engineering

Daniel Rodriguez

Ph.D. Student, 2022-Now

Electrical and Computer Engineering

NIH T1D T32 Fellow

Junfu Cheng

Ph.D. Student, 2024-Now

Electrical and Computer Engineering


Co-advise or Collaborating PhD Students


Boxiao Yu

Ph.D. Student, 2022-Now

Biomedical Engineering

Ziqian Huang

Ph.D. Student, 2022-Now

Biomedical Engineering


M.S. Students


Zeyun Zhao

M.S. Student, 2024-Now

Biomedical Engineering

Travis Koenig

M.S. Student, 2023-Now

Biomedical Engineering

Pratyush Shukla

M.S. Student, 2023-Now


PostBac Researchers

Gavin Hart

Post-Baccalaureate

Neuroscience

GATORAADE Fellow

Undergraduate Students

Minhyeok Wi

Undergraduate Student

Electrical Engineering

Samuel Zheng

Undergraduate Student

Biomedical Engineering

NSF REU Awardee

Ritika Samanta

Undergraduate Student

Computer Science and Psychology BCN

Sanandan Ojha

Undergraduate Student

Biomedical Engineering

Akshay Ashok

Undergraduate Student

Computer Science

UF AI Scholar

Veronica Ramos

Undergraduate Student

Biomedical Engineering

Thrisha Acharya

Undergraduate Student

Microbiology and Cell Sciences

Grace Cheng

Undergraduate Student

Biomedical Engineering

UF AI Scholar

Jason Chen

Undergraduate Student

Computer Science

NSF REU Awardee, UF AI Scholar

Amy Lazarte

Undergraduate Student

Biomedical Engineering

NSF REU Awardee, UF AI Scholar

Surya Karthikeyan Vijayalakshmi

Undergraduate Student

Computer Science

Collaborators

Mingzhou Ding

University of Florida
Department of Biomedical Engineering

Andreas Keil

University of Florida
Department of Psychology

Adam Woods

University of Texas, Dallas
Department of Neuroscience

Aprinda I Queen

University of Florida
Department of Clinical and Health Psychology

Samuel Wu

University of South Florida
Department of Biostatistics

My Thai

University of Florida
Department of Computer Science and Engineering

Leonid L. Moroz

University of Florida
Department of Neuroscience, Genetics, Chemistry & Biology

Marco Salemi

University of Florida
Department of Pathology, Immunology, and Laboratory Medicine

Ivana Parker

University of Florida
Department of Biomedical Engineering

Yonghui Wu

University of Florida
Department of Health Outcomes and Biomedical Informatics

Alumni

M.D. Alumni

Simeng Zhu

Doctor of Medicine, 2018-2022
Medical Resident at Henry Ford Hospital Radiation Oncology, 2019-2023

Assistant Professor at Ohio State University from Fall 2023

PostDoc Alumni

Diandra Ojo

Postdoc, 2023-2024

Co-mentor with Dr. Ivana Parker

PostDoc, University of Florida, ECE

Ph.D. Alumni

Yao Xiao

Doctor of Philosophy, 2017-2020

Biomedical Engineering

BMES 2020 Career Development Award
Graduate Student Speaker at College of Engineering Commencement 2020

Assistant Professor, Mayo Clinic

Peng Liu

Doctor of Philosophy, 2016-2021

Biomedical Engineering

Best Abstract Award at Montreal AI & Neuroscience (MAIN) Conference
UF Informatics Institute Graduate Fellowship

Postdoc Associate, University of Florida

Kyle See

Ph.D. Candidate, 2019-2024

Biomedical Engineering

UF Graduate Student Preeminence Award
NIH CTSI TL1 Predoctoral Fellowship

Postdoc Associate, University of Florida

Tianqi Liu

Ph.D. Student, 2019-2024

Electrical and Computer Engineering

Senior Researcher, Tencent Americas


M.S. Alumni

Everett J. Schwieg

M.S. Student, 2022-2024

Biomedical Engineering

Fan Yang

M.S. Student, 2022-2024

Ph.D. student, NJIT

Ayesha Naikodi

M.S. Computer Information Science and Engineering, 2022-2023

Ph.D. student, University of Florida

Huang Hong

Master of Engineering, 2021-2023

Electrical and Computer Engineering

Jimmy Ossa

Master of Engineering, 2020-2022

Software Engineer at Infotech

Shen Kai

Master of Engineering, 2021-2022

PhD Student at University of Florida

Jiaqing Zhang

Master of Engineering, ECE, UF 2020-2022

PhD student at University of Florida

Shreya Verma

Master of Engineering, BME, UF 2019-2021

Ph.D. student at Penn State University

Bhavin Soni

Master of Engineering, BME, 2019-2021

Neuroimaging Engineer, Washington University at St. Louis

Yangjunyi Li

Masters Student, UF BME, 2017-2018

Senior Scientist, Computational Biologist, Eli Lilly and Company

Yun Liang

UF BME, 2018-2019

PhD student, UF BME

Micheal Adeyosoye

MS-PhD, FIU Bridge to PhD Fellowship, 2016-2017

PhD Student, FIU

Jingan Qu

Masters Student, 2015-2017

Data Scientist, Applied Research Center FIU

Daniel Parra

Masters Student, 2015-2016

Software Engineer, ProActive Technologies, LLC

Haodi Jiang

Masters Student, 2014-2015

PhD student at NJIT

Sherman Ng

M.Eng. 2010-2011 @ Cornell ECE

Software Engineer, Microsoft


Undergraduate Alumni

Michelle Mu

Undergraduate Student

Biology

Thomas Howland

Undergraduate Student

Biomedical Engineering

Kevin Liu

Undergraduate Student

Physics

Ethan Smith

Undergraduate Student

Psychology

Justin Broce

Undergraduate Student

Computer Science

Kyle Douglas

Undergraduate Student

Biomedical Engineering

Joshua Lamb

Undergraduate Student

Computer Science

Cynthia Liu

Undergraduate Student

Biomedical Engineering

Jordi Bardia

B.E. Computer Science, UF, 2022

Benjamin Arnold

Undergraduate Student

Biomedical Engineering

Hely Lin

B.S. Biomedical Engineering, UF, 2020-2023

Machine Learning Engineer, CAE Inc.

Yiru Mu

B.S. Computer Science, UF, 2021-2023

M.S. student, Georgia Tech

Garrett Fullerton

B.S. Biomedical Engineering, UF, 2019-2022

Ph.D. Student, Medical Physics, University of Wisconsin

NSF GRFP Awardee
NSF REU Awardee
University Scholar Program

Gianna Sweeting

B.S. Biomedical Engineering, UF, 2020-2022
Fernandez Family Scholar
University Scholar Program

Simon Kato

B.S. Math and Statistics, UF, 2020-2022

Ph.D. Student, Computer Science, UIUC

NSF REU Awardee

Brian "John" Braddock

B.S. Biomedical Engineering, 2021-2023
NSF REU Awardee

Maria Cardei

Undergraduate Student, 2020-2022

Biomedical Engineering

Alvin Naiju

Undergraduate Student

Biochemistry

Michael McGaha

B.S. Computer Science and Statistics, UF, 2020-2022

Software Engineering (SDE), Amazon

Nathan Barkdull

B.S. Math, UF. 2020-2022

Ph.D. student, Dynamical Neuroscience,UCSB

Keyur Patel

B.E. Computer Science, 2021-2022

Jason Chen

B.E. Computer Science, 2019-2021.

Software Development Engineer, Facebook

NSFUF AI Scholar

Maximillian Diaz

B.E. BME, 2018-2020
University Scholar Program
NSF Graduate Research Fellowship Program

Ph.D. student at UF

Neeva Sethi

Undergraduate Student @ UF 2019-2023
Computer Science
Presidential Service Awardee
CLAS Hall of Fame Inductee

Daniel El Basha

B.E. BME, NSF REU Awardee 2017-2019
NSF Graduate Research Fellowship Program

PhD student at MD Anderson

Akshay Mathavan

Undergraduate Researcher, University Scholar Program, 2018-2019

Doctor of Medicine student, UF

Akash Mathavan

Undergraduate Researcher, University Scholar Program, 2018-2019

Doctor of Medicine student, UF


Visiting Students Alumni

Yuanyuan Zhu

Visiting Undergraduate Student, 2016 Summer

Software Engineer, Indeed.comock

Xing Pang

Visiting Graduate Student, 2015-2016

Nanjing University of Science and Technology


K-12 Teachers

Edda Rivera

NSF DoD RET Teacher, Best NSF RET Poster Award at FIU SCIS RET Presentation, 2016 Summer

High School Teacher, John A. Ferguson Senior High School

Christian McDonald

NSF DoD RET Teacher, Best NSF RET Poster Award at FIU SCIS RET Presentation, 2016 Summer

High School Teacher, Miami Jackson Senior High School


K-12 Students

Paul Naghshineh

NSF DoD REU Student, 2016 Summer

Gaumard Scientific

Srividya Vaishnavi Surampudi

UF Student Science Training Program (SSTP), 2018 Summer

Stanford University

Imaan Randhawa

UF Student Science Training Program (SSTP), 2019 Summer

University of Florida

Aarushi Walia

UF Student Science Training Program (SSTP), 2019 Summer

University of Berkeley

Funded Projects

  • image

    DRPD-ROF2023: Learning optimal treatment strategies for hypotension in critical care patients with acute kidney injury using artificial intelligence

  • image

    NCS-FO-2318984: Brain-Informed Goal-Oriented and Bidirectional Deep Emotion Inference

    NSF

    Human emotions are dynamic, multidimensional responses to challenges and opportunities that emerge from network interactions in the brain. Disruptions of these dynamics underlie emotional dysregulation in many mental disorders including anxiety and depression. To empirically study the neural basis of human emotion inference, experimenters often have observers view natural images varying in affective content, while at the same time recording their brain activity using electroencephalogram (EEG) and/or Functional magnetic resonance imaging (fMRI). Despite extensive research over the last few decades, much remains to be learned about the computational principles subserving the recognition of emotions in natural scenes. A major roadblock faced by empirical neuroscientists is the inability to carry out precisely manipulate human neural systems and test the consequences in imaging data. Deep Neural Networks (DNN), owing to their high relevance to human neural systems and extraordinary prediction capability, have become a promising tool for testing these sorts of hypotheses in swift and nearly costless computer simulations. The overarching goal of this project is to develop a neuroscience-inspired, DNN-based deep learning framework for emotion inference in real-world scenarios by synergistically integrating neuron-, circuit-, and system-level mechanisms. Recognizing that the state-of-the-art DNNs are centered on bottom-up and feedforward-only processing, which disagrees with the strong goal-oriented top-down modulation recurrence observed in the physiology, this project aims to enrich DNNs and enable closer AI-neuroscience interaction by incorporating goal-oriented top-down modulation and reciprocal interactions DNNs and test the model assumptions and predictions on neuroimaging data. To meet these goals, the project aims to develop a brain-inspired goal-oriented and bidirectional deep learning model for emotion inference. Despite the great promise shown by today?s deep learning as a framework for modeling biological vision, their architecture is limited to emulating the visual cortex for face/object/scene recognition and rarely goes beyond the inferotemporal cortex (IT), which is necessary for modeling high-level cognitive processes. In this project, we propose to build a biologically plausible deep learning architecture by integrating an in-silico amygdala module into the visual cortex architecture in DNN (the VCA model). The researchers hope to build neuron-, circuit-, and system-level modulation via goal-oriented attention priming, and multi-pathway predictive coding to 1) elucidate the mechanism of selectivity underlying preference and response to naturalistic emotions by artificial neurons; 2) differentiate fine-grained emotional responses via multi-path predictive coding, and 3) refine the neuroscientific understanding of human neuro-behavioral data by comparing attention priming and temporal generalization observed in simultaneous fMRI-EEG data to the computational observations using our brain-inspired VCA model. This project introduces two key innovations, both patterned after how brain operates, into DNN architecture and demonstrate their superior performance when applied to complex real-world tasks. Successful execution of the project can lead to the development of a new generation of AI-models that are inspired by neuroscience and that may in turn power neuroscience research.

  • image

    R01AG081477: Cognitively engaging walking exercise and neuromodulation to enhance brain function in older adults

    NIH REPORT

    There is a pressing need for effective interventions to remediate age-related cognitive decline and alter the trajectory toward Alzheimer’s disease. The NIA Alzheimer’s Disease Initiative funded Phase III Augmenting Cognitive Training in Older Adults (ACT) trial aimed to demonstrate that transcranial direct current stimulation (tDCS) paired with cognitive training could achieve this goal. The present study proposes a state of the art secondary data analysis of ACT trial data that will further this aim by 1) elucidate mechanism of action underlying response to tDCS treatment with CT, 2) address heterogeneity of response in tDCS augmented CT by determining how individual variation in the dose of electrical current delivered to the brain interacts with individual brain anatomical characteristics; and 3) refine the intervention strategy of tDCS paired with CT by evaluating methods for precision delivery targeted dosing characteristics to facilitate tDCS augmented outcomes. tDCS intervention to date, including ACT, apply a fixed dosing approach whereby a single stimulation intensity (e.g., 2mA) and set of electrode positions on the scalp (e.g., F3/F4) is applied to all participants/patients. However, our recent work has demonstrated that age-related changes in neuroanatomy as well as individual variability in head/brain structures (e.g., skull thickness) significantly impacts the distribution and intensity of electrical current induced in the brain from tDCS. This project will use person-specific MRI-derived finite element computational models of electric current characteristics (current intensity and direction of current flow) and new methods for enhancing the precision and accuracy of derived models to precisely quantify the heterogeneity of current delivery in older adults. We will leverage these individualized precision models with state-of-the-art support vector machine learning methods to determine the relationship between current characteristics and treatment response to tDCS and CT. We will leverage the inherent heterogeneity of neuroanatomy and fixed current delivery to provide insight in the not only which dosing parameters were associated with treatment response, but also brain region specific information to facilitate targeted delivery of stimulation in future trials. Further still, the current study will also pioneer new methods for calculation of precision dosing parameters for tDCS delivery to potentially optimize treatment response, as well as identify clinical and demographic characteristics that are associated with response to tDCS and CT in older adults. Leveraging a robust and comprehensive behavioral and multimodal neuroimaging data set for ACT with advanced computational methods, the proposed study will provide critical information for mechanism, heterogeneity of treatment response and a pathway to refined precision dosing approaches for remediating age- related cognitive decline and altering the trajectory of older adults toward Alzheimer’s disease.

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    RF1AG071469: Mechanisms, response heterogeneity and dosing from MRI-derived electric field models in tDCS augmented cognitive training: a secondary data analysis of the ACT study

    NIH REPORT

    There is a pressing need for effective interventions to remediate age-related cognitive decline and alter the trajectory toward Alzheimer’s disease. The NIA Alzheimer’s Disease Initiative funded Phase III Augmenting Cognitive Training in Older Adults (ACT) trial aimed to demonstrate that transcranial direct current stimulation (tDCS) paired with cognitive training could achieve this goal. The present study proposes a state of the art secondary data analysis of ACT trial data that will further this aim by 1) elucidate mechanism of action underlying response to tDCS treatment with CT, 2) address heterogeneity of response in tDCS augmented CT by determining how individual variation in the dose of electrical current delivered to the brain interacts with individual brain anatomical characteristics; and 3) refine the intervention strategy of tDCS paired with CT by evaluating methods for precision delivery targeted dosing characteristics to facilitate tDCS augmented outcomes. tDCS intervention to date, including ACT, apply a fixed dosing approach whereby a single stimulation intensity (e.g., 2mA) and set of electrode positions on the scalp (e.g., F3/F4) is applied to all participants/patients. However, our recent work has demonstrated that age-related changes in neuroanatomy as well as individual variability in head/brain structures (e.g., skull thickness) significantly impacts the distribution and intensity of electrical current induced in the brain from tDCS. This project will use person-specific MRI-derived finite element computational models of electric current characteristics (current intensity and direction of current flow) and new methods for enhancing the precision and accuracy of derived models to precisely quantify the heterogeneity of current delivery in older adults. We will leverage these individualized precision models with state-of-the-art support vector machine learning methods to determine the relationship between current characteristics and treatment response to tDCS and CT. We will leverage the inherent heterogeneity of neuroanatomy and fixed current delivery to provide insight in the not only which dosing parameters were associated with treatment response, but also brain region specific information to facilitate targeted delivery of stimulation in future trials. Further still, the current study will also pioneer new methods for calculation of precision dosing parameters for tDCS delivery to potentially optimize treatment response, as well as identify clinical and demographic characteristics that are associated with response to tDCS and CT in older adults. Leveraging a robust and comprehensive behavioral and multimodal neuroimaging data set for ACT with advanced computational methods, the proposed study will provide critical information for mechanism, heterogeneity of treatment response and a pathway to refined precision dosing approaches for remediating age- related cognitive decline and altering the trajectory of older adults toward Alzheimer’s disease.

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    NSF IIS 2123809: Collaborative Research: SCH: Trustworthy and Explainable AI for Neurodegenerative Diseases

    NSF Abstract

    Driven by its performance accuracy, machine learning (ML) has been used extensively for various applications in the healthcare domain. Despite its promising performance, researchers and the public have grown alarmed by two unsettling deficiencies of these otherwise useful and powerful models. First, there is a lack of trustworthiness - ML models are prone to interference or deception and exhibit erratic behaviors when in action dealing with unseen data, despite good practice during the training phase. Second, there is a lack of interpretability - ML models have been described as 'black-boxes' because there is little explanation for why the models make the predictions they do. This has called into question the applicability of ML to decision-making in critical scenarios such as image-based disease diagnostics or medical treatment recommendation. The ultimate goal of this project is to develop computational foundation for trustworthy and explainable Artificial Intelligence (AI), and offer a low-cost and non-invasive ML-based approach to early diagnosis of neurodegenerative diseases. In particular, the project aims to develop computational theories, ML algorithms, and prototype systems. The project includes developing principled solutions to trustworthy ML and making the ML prediction process transparent to end-users. The later will focus on explaining how and why an ML model makes such a prediction, while dissecting its underlying structure for deeper understanding. The proposed models are further extended to a multi-modal and spatial-temporal framework, an important aspect of applying ML models to healthcare. A verification framework with end-users is defined, which will further enhance the trustworthiness of the prototype systems. This project will benefit a variety of high-impact AI-based applications in terms of their explainability, trustworthy, and verifiability. It not only advances the research fronts of deep learning and AI, but also supports transformations in diagnosing neurodegenerative diseases.

    This project will develop the computational foundation for trustworthy and explainable AI with several innovations. First, the project will systematically study the trustworthiness of ML systems. This will be measured by novel metrics such as, adversarial robustness and semantic saliency, and will be carried out to establish the theoretical basis and practical limits of trustworthiness of ML algorithms. Second, the project provides a paradigm shift for explainable AI, explaining how and why a ML model makes its prediction, moving away from ad-hoc explanations (i.e. what features are important to the prediction). A proof-based approach, which probes all the hidden layers of a given model to identify critical layers and neurons involved in a prediction from a local point of view, will be devised. Third, a verification framework, where users can verify the model's performance and explanations with proofs, will be designed to further enhance the trustworthiness of the system. Finally, the project also advances the frontier of neurodegenerative diseases early diagnosis from multimodal imaging and longitudinal data by: (i) identifying retinal vasculature biomarkers using proof-based probing in biomarker graph networks; (ii) connecting biomarkers of the retina and the brain vasculature via cross- modality explainable AI model; and, (iii) recognizing the longitudinal trajectory of vasculature biomarkers via a spatio-temporal recurrent explainable model. This synergistic effort between computer science and medicine will enable a wide range of applications to trustworthy and explainable AI for healthcare. The results of this project will be assimilated into the courses and summer programs that the research team have developed with specially designed projects to train students with trustworthy and explainable AI.

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    P01AI165380: Multi-Scale Evaluation and Mitigation of Toxicities Following Internal Radionuclide Contamination

    NIH REPORT

    History has taught us that exposures to radionuclides can happen any day almost anywhere in the US and elsewhere and we have done little to prepare ourselves. Our ability to perform dosimetry modeling for such scenarios and efforts into biomarker and mitigation discovery are archaic and our tendency to rely on external beam radiation to model these is utterly misplaced. We should and we can do much better. This program centers on the hypothesis that radiation from internal emitters is very unevenly distributed within a body, amongst organs, and even within organs, tissues and cells. The half-life and decay schema of the radionuclide, its activity and concentration, particle size and morphology, and its chemical form and solubility are all critical, as are the route of uptake, tissue structure, genetic makeup, physiology, danger signaling and the crosstalk with the immune system. Conceptually this suggests that the analysis of radionuclide distribution requires measurements at the MESO, MICRO and NANO level for accurate dosimetry modeling and biokinetics analyses, that will much better align with biological endpoints, and therefore with meaningful countermeasure development. In many ways our program integrates the three main pillars of radiation science, namely radiation physics, radiation chemistry and radiation biology, taking into account pharmacokinetics and pharmacodynamics aspects of particle distribution at subcellular, cellular, and tissue levels. In other words, to understand the biological effects of internal emitters and find the best possible mitigation strategies a systematic study is called for, one that includes but is not limited to: a) radionuclide physical and chemical form and intravital migration, b) protracted exposure times, c) radiation quality parameters, d) novel virtual phantom modeling beyond few MACRO reference models ; e) novel biokinetics with sex- and age- specificity; f) MESO, MICRO and NANO scale histology and immunohistochemistry with integrated radionuclide distribution information; g) exploration of molecular biomarkers of radionuclide intake and contamination and h) countermeasures that modulate radionuclide distribution and possibly also improve DNA, cell and tissue repair. We have assembled a team with diverse scientific expertise that can tackle these challenges within an integrated program. There is an incredibly impressive technological toolbox at our disposal and our goal is to generate a meaningful blueprint for understanding and predicting biological consequences of exposure to radionuclides. The possible benefits of this program to the radiation research community and the general population are immense.

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    U24AA029959: Southern HIV and Alcohol Research Consortium Biomedical Data Repository

    NIH REPORT

    The purpose of the SHARE P01 research program project is to address HIV and alcohol use around three themes; 1) Emerging adulthood (ages 18 -29); 2) Self-management of HIV and alcohol; and 3) Translational behavioral science. Emerging adulthood is a developmental stage marked by significant change in social roles, expectations as a new adult, and increased responsibilities. It is also marked by poor HIV self- management and increased alcohol use. Emerging adults with HIV (hereafter called young people living with HIV; YPLWH) may face even more challenges given intersectional stigma. This age group continues to have very high rates of new HIV infections. Interventions designed specifically for the unique developmental challenges of emerging adults are needed, yet emerging adults are often included with older adults in intervention programs. The concept of self-management emerged concurrently within both the substance abuse and chronic illness literatures, and fits well with the developmental challenges of emerging adulthood. Self-management, a framework we have utilized in our work with YPLWH, refers to the ability to manage symptoms, treatments, lifestyle changes, and consequences of health conditions. Current research now identifies individual-level self-management skills such as self-control, decision-making, self-reinforcement, and problem solving as that protect against substance use and improve other health outcomes and can be embedded in the Information-Motivation-Behavioral Skills model. Although we have conducted multiple studies with YPLWH, only one intervention to date (Healthy Choices conducted by our team) improved both alcohol use and viral suppression in YPLWH in large trials. The goal of the SHARE P01 is to utilize advances in translational behavioral science to optimize behavioral interventions and define new developmentally- and culturally-appropriate intervention targets to improve self-management of alcohol and HIV in YPLWH. We will focus our efforts in Florida, a state hardest hit by the HIV epidemic but with a particularly strong academic- community partnership to support translation. We have assembled research teams to conduct self- management studies across the translational spectrum to address self-management and improve alcohol use and viral suppression (and thereby reduce transmission) in diverse YPLWH in Florida. The P01 will consist of three research projects (DEFINE, ENGAGE, and SUSTAIN), representing different stages on the translational spectrum and targeting different core competencies, supported by two cores (Community Engagement Core and Data Science Core). If successful, the SHARE P01 has the potential to greatly advance programs promoting self-management of HIV and alcohol use among a particularly vulnerable, but under-researched group, emerging adults living with HIV. SHARE also has a high potential for scale-up and implementation beyond Florida and across the United States.

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    P01AA029543: Interventions to improve alcohol-related comorbidities along the gut-brain axis in persons with HIV infection

    NIH REPORT

    As persons living with HIV (PLWH) live longer, approximately 50% will experience HIV-related cognitive dysfunction, which may affect daily activities, contribute to morbidity and mortality, and increase the likelihood of HIV transmission. Alcohol consumption among PLWH may further exacerbate long-term cognitive dysfunction, with the presumed mechanism involving the gut microbiome, microbial translocation, systemic inflammation, and ultimately neuroinflammation. However, there are many gaps in our understanding regarding the specific pathophysiological mechanisms, and a need to offer interventions that are effective and acceptable in helping PLWH to reduce drinking or to protect them against alcohol-related harm. The overarching goal of this P01 is to identify and ultimately implement new/improved, targeted interventions that will improve outcomes related to cognitive and brain dysfunction in persons with HIV who drink alcohol. The proposed P01 activity will extend our current line of research that forms the core of the Southern HIV & Alcohol Research Consortium (SHARC). The specific aims of this P01 are to: 1) improve our understanding of the specific mechanisms that connect the gut microbiome to cognitive and brain health outcomes in persons with HIV; 2) evaluate interventions that are intended to reduce the impact of alcohol on brain and cognitive health in persons with HIV; and 3) connect and extend the research activity from this P01 with the training programs and community engagement activity in the SHARC. Our P01 will utilize two cores that provide infrastructure to two Research Components (RC1, RC2). The two RC will together enroll 200 PLWH with at-risk drinking into clinical trials that share common timepoints and outcome assessments. RC1 will compare two strategies to extend contingency management to 60 days, using breathalyzers and wrist-worn biosensors to monitor drinking. RC2 uses a hybrid trial design to evaluate two biomedical interventions targeting the gut-brain axis. One intervention is a wearable, transcutaneous vagus nerve stimulator that is hypothesized to stimulate the autonomic nervous system, resulting in decreased inflammation and improved cognition. The other intervention is a probiotic supplement intended to improve the gut microbiome in persons with HIV and alcohol consumption. All participants in RC2, and a subset of those in RC1 will have neuroimaging at two timepoints. The Data Science Core will provide data management and analytical support, and will analyze existing data and the data collected from this P01 using a machine learning and AI approach to identify factors associated with intervention success or failure. The Administrative Core will provide scientific leadership, clinical research and recruitment infrastructure, and connection to the outstanding training programs, development opportunities, and community engagement provided by the SHARC. Our community engagement with diverse populations, and collection of acceptability data from clinical trial participants, will facilitate our readiness to scale up the most promising interventions and move towards implementation in the next phase of our research.

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    P01AA029547: SHARE Program: Innovations in Translational Behavioral Science to Improve Self- management of HIV and Alcohol Reaching Emerging adults

    NIH REPORT

    The number of persons living with HIV (PLWH) continues to increase in the United States. Alcohol consumption is a significant barrier to both achieving the goal of ending the HIV epidemic and preventing comorbidities among PLWH, as it contributes to both HIV transmission and HIV-related complications. Recent advances in data capture systems such as mHealth devices, medical imaging, and high-throughput biotechnologies make large/complex research and clinical datasets available, including survey data, multi-omics data, electronic medical records, and/or other sources of reliable information related to engagement in care. This offers tremendous potential of applying “big” data to extract knowledge and insights regarding fundamental physiology, understand the mechanisms by which the pathogenic effects of biotic and abiotic factors are realized, and identify potential intervention targets. We propose to integrate the disparate data sources maintained by our partners and then utilize the big data to address research questions in treating HIV and alcohol-related morbidity and mortality. Specifically, we will pursue the following three aims: 1) Integrate the disparate data sources through standardization, harmonization, and merging; 2) Develop a web-based data sharing platform including virtual data sharing communities, data privacy protection, streamlined data approval and access, and tracking of ongoing research activities; 3) Provide statistical support to junior investigators to use the data repository for exploratory data analysis and proposal development. The proposed study will tap into disparate data sources, unleash the potential of data and information, accelerate knowledge discovery, advance data-powered health, and transform discovery to improve health outcomes for PLWH.

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    R01MH125615: Acquisition, extinction, and recall of attention biases to threat: Computational modeling and multimodal brain imaging

    NIH REPORT

    Classical aversive conditioning is a well-established laboratory model for studying acquisition and extinction of defensive responses. In experimental animals, as well as in humans, research to date has been mainly focused on the role of limbic structures (e.g., the amygdala) in these responses. Recent evidence has begun to stress the important contribution by the brain’s sensory and attention control systems in maintaining the neural representations of conditioned responses and in facilitating their extinction. The proposed research breaks new ground by combining novel neuroimaging techniques with advanced computational methods to examine the brain’s visual and attention processes underlying fear acquisition and extinction in humans. Major advances will be made along three specific aims. In Aim 1, we characterize the brain network dynamics of visuocortical threat bias formation, extinction, and recall in a two-day learning paradigm. In Aim 2, we establish and test a computational model of threat bias generalization. In Aim 3, we examine the relation between individual differences in generalization and recall of conditioned visuocortical threat biases and individual differences in heightened autonomic reactivity to conditioned threat, a potential biomarker for assessing the predisposition to developing the disorders of fear and anxiety. It is expected that accomplishing these research aims will address two NIMH strategic priorities: defining the circuitry and brain networks underlying complex behaviors (Objective 1) and identifying and validating new targets for treatment that are derived from the understanding of disease mechanisms (Objective 3). It is further expected that this project will enable a paradigm shift in research on dysfunctional attention to threat from one that focuses primarily on limbic-prefrontal circuits to one that emphasizes the interactions among sensory, attention, executive control and limbic systems.

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    U01NS119562: Web-based Automated Imaging Differentiation of Parkinsonism

    NIH REPORT

    Across the globe, there has been a considerable growth in the number of people diagnosed with Parkinsonism. Estimates indicate that from 1990 to 2015 the number of Parkinsonism diagnoses doubled, with more than 6 million people currently carrying the diagnosis, and by year 2040, 12 and 14.2 million people will be diagnosed with Parkinsonism. Parkinson’s disease (PD), multiple system atrophy Parkinsonian variant (MSAp), and progressive supranuclear palsy (PSP) are neurodegenerative forms of Parkinsonism, which can be difficult to diagnose as they share similar motor and non-motor features, and they each have an increased chance of developing dementia. In the first five years of a PD diagnosis, about 58% of PD are misdiagnosed, and of these misdiagnoses about half have either MSA or PSP. Since PD, MSAp, and PSP require unique treatment plans and different medications, and clinical trials testing new medications require the correct diagnosis, there is an urgent need for both clinic ready and clinical-trial ready markers for differential diagnosis of PD, MSAp, and PSP. Over the past decade, we have developed diffusion imaging as an innovative biomarker for differentiating PD, MSAp, and PSP. In this proposal, we will leverage our extensive experience to create a web-based software tool that can process diffusion imaging data from anywhere in the world. We will disseminate and test the tool in the largest prospective cohort of participants with Parkinsonism (PD, MSAp, PSP), working closely with the Parkinson Study Group. The reason to test this in the Parkinson Study Group network, is because they are the community that evaluates Phase II and Phase III clinical trials in Parkinsonism. This web-based software tool will be capable of reading raw diffusion imaging data, performing quality assurance procedures, analyzing the data using a validated pipeline, and providing imaging metrics and diagnostic probability. We will test the performance of the wAID-P by enrolling 315 total subjects (105 PD, 105 MSAp, 105 PSP) across 21 sites in the Parkinson Study Group. Each site will perform imaging, clinical scales, diagnosis, and will upload the data to the web-based software tool. The clinical diagnosis will be blinded to the diagnostic algorithm and the imaging diagnosis will be compared to the movement disorders trained neurologist diagnosis. We will also enroll a portion of the cohort into a brain bank to ascertain pathological confirmation and to test the algorithm against cases with post-mortem diagnoses. The final outcome will be to disseminate a validated diagnostic algorithm to the Parkinson neurological and radiological community and to make it available to all on a website.

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    IIS-1908299 III: Small: Collaborative Research: Modeling Multi-Level Connectivity of Brain Dynamics

    Project Page

    The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer's disease. Existing efforts at characterizing brain dynamics have predominantly centered on 'isolated' models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.

  • IIS-1564892 CRII: SCH: Characterizing, Modeling and Evaluating Brain Dynamics

    The temporal dynamics of blood flows through the network of cerebral arteries and veins provides a window into the health of the human brain. Since the brain is vulnerable to disrupted blood supply, brain dynamics serves as a crucial indicator for many kinds of neurological diseases such as stroke, brain cancer, and Alzheimer's disease. Existing efforts at characterizing brain dynamics have predominantly centered on 'isolated' models in which data from single-voxel, single-modality, and single-subject are characterized. However, the brain is a vast network, naturally connected on structural and functional levels, and multimodal imaging provides complementary information on this natural connectivity. Thus, the current isolated models are deemed not capable of offering the platform necessary to enable many of the potential advancements in understanding, diagnosing, and treating neurological and cognitive diseases, leaving a critical gap between the current computational modeling capabilities and the needs in brain dynamics analysis. This project aims to bridge this gap by exploiting multi-scale structural (voxel, vasculature, tissue) connectivity and multi-modal (anatomical, angiography, perfusion) connectivity to develop an integrated connective computational paradigm for characterizing and understanding brain dynamics.

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    CNS-1747783: Phase I IUCRC University of Florida: Center for Big Learning

    This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL). The vision is to create intelligence towards intelligence-driven society. Through catalyzing the fusion of diverse expertise from the consortium of faculty members, students, industry partners, and federal agencies, CBL seeks to create state-of-the-art deep learning methodologies and technologies and enable intelligent applications, transforming broad domains, such as business, healthcare, Internet-of-Things, and cybersecurity. This timely initiative creates a unique platform for empowering our next-generation talents with cutting-edge technologies of societal relevance and significance. This project establishes the NSF Industry/University Collaborative Research Center for Big Learning (CBL) at University of Florida (UF). With substantial breakthroughs in multiple modalities of challenges, such as computer vision, speech recognitions, and natural language understanding, the renaissance of machine intelligence is dawning. The CBL vision is to create intelligence towards intelligence-driven society. The mission is to pioneer novel deep learning algorithms, systems, and applications through unified and coordinated efforts in the CBL consortium. The UF Site will focus on intelligent platforms and applications and closely collaborate with other sites on deep learning algorithms, systems, and applications. The CBL will have broad transformative impacts in technologies, education, and society. CBL aims to create pioneering research and applications to address a broad spectrum of real-world challenges, making significant contributions and impacts to the deep learning community. The discoveries from CBL will make significant contributions to promote products and services of industry in general and CBL industry partners in particular. As the magnet of deep learning research and applications, CBL offers an ideal platform to nurture next-generation talents through world-class mentors from both academia and industry, disseminates the cutting-edge technologies, and facilitates industry/university collaboration and technology transfer. The center repository will be hosted at http://nsfcbl.org. The data, code, documents will be well organized and maintained on the CBL servers for the duration of the center for more than five years and beyond. The internal code repository will be managed by GitLab. After the software packages are well documented and tested, they will be released and managed by popular public code hosting services, such as GitHub and Bitbucket.

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    Artificial Intelligence Catalyst Award: VCA-DNN: Neuroscience-Inspired Artificial Intelligence for Visual Emotion Recognition

    Human emotions are dynamic, multidimensional responses to challenges and opportunities, which emerge from network interactions in the brain. Disruptions of these network interactions underlie emotional dysregulation in many mental disorders, including anxiety and depression. Creating an AI-based model system that is informed and validated by known biological findings and can be used to carry out causal manipulations and test the consequences against human imaging data will thus be a highly significant development in the short term. The long-term goal is to understand how the human brain processes emotional information and how the process breaks down in mental disorders. NIH currently funds the team to record and analyze fMRI data from humans viewing natural images of varying emotional content. In the process of their research, they recognize that empirical studies such as theirs have significant limitations. Chief among them is the lack of ability to manipulate the system to establish the causal basis for the observed relationship between brain and behavior. The advent of AI, especially deep neural networks (DNNs), opens a new avenue to address this problem. Creating an AI-based model system that is informed and validated by known biological findings and that can be used to carry out causal manipulations and allow the testing of the consequences against human imaging data will thus be a significant step toward achieving our long-term goal.

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    UFII: Biology and Cognition Inspired Deep Learning

    Human emotions are dynamic, multidimensional responses to challenges and opportunities, which emerge from network interactions in the brain. Disruptions of these network interactions underlie emotional dysregulation in many mental disorders, including anxiety and depression. Creating an AI-based model system that is informed and validated by known biological findings and can be used to carry out causal manipulations and test the consequences against human imaging data will thus be a highly significant development in the short term. The long-term goal is to understand how the human brain processes emotional information and how the process breaks down in mental disorders. NIH currently funds the team to record and analyze fMRI data from humans viewing natural images of varying emotional content. In the process of their research, they recognize that empirical studies such as theirs have significant limitations. Chief among them is the lack of ability to manipulate the system to establish the causal basis for the observed relationship between brain and behavior. The advent of AI, especially deep neural networks (DNNs), opens a new avenue to address this problem. Creating an AI-based model system that is informed and validated by known biological findings and that can be used to carry out causal manipulations and allow the testing of the consequences against human imaging data will thus be a significant step toward achieving our long-term goal.

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    UFII Junior SEED Award: Multimodal Visual-Text Learning from Clinical Narrative and Image for Early Detection of Diabetic Retinopathy

    Vision-threatening diseases are one of the leading causes of blindness. DR, a common complication of diabetes, is the leading cause of blindness in American adults and the fastest growing disease threatening nearly 415 million diabetic patients worldwide. With professional eye imaging devices such as fundus cameras or Optical Coherence Tomography (OCT) scanners, most of the vision-threatening diseases can be curable if detected early. However, these diseases are still damaging people’s vision and leading to irreversible blindness, especially in rural areas and low-income communities where professional imaging devices and medical specialists are not available or not even affordable. There is an urgent need for early detection of vision-threatening diseases before vision loss in these areas. The current practice of DR screening relies on human experts to manually examine and diagnose DR in stereoscopic color fundus photographs at hospitals using professional fundus camera, which is time-consuming and infeasible for large-scale screening. It also puts an enormous burden on ophthalmologists and increases waiting lists and may undermine the standards of health care. Therein, automatic DR diagnosis systems with ophthalmologist-level performance are a critical and unmet need for DR screening. Electronic Health Records (EHR) have been increasingly implemented in US hospitals. Vast amounts of longitudinal patient data have been accumulated and are available electronically in structured tables, narrative text, and images. There is an increasing need for multimodal synergistic learning methods to link different data sources for clinical and translational studies. Recent emerging of AI technologies, especially deep learning (DL) algorithms, have greatly improved the performance of automated vision-disease diagnosis systems based on EHR data. However, the current systems are unable to detect early stage of vision-diseases. On the other hand, the clinical text provides detailed diagnosis, symptoms, and other critical observations documented by physicians, which could be a valuable resource to help lesion detection from medical images. Multimodal synergistic learning is the key to linking clinical text to medical images for lesion detection. This study proposes to leverage the narrative clinical text to improve lesion level detection from medical images via clinical Natural Language Processing (NLP). The team hypothesizes that early stage vision-threatening diseases can be detected using smartphone-based fundus camera via multimodal learning integrating clinical text and images with limited lesion-level labels via clinical NLP. The ultimate goal is to improve the early detection and prevention of vision-threatening diseases among rural and low-income areas by developing a low-cost, highly efficient system that can leverage both clinical narratives and images.

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    UFII-CTSI Pilot Award: Toward prevention of cardiotoxicity in cancer: a multimodal approach leveraging genomics, images and clinical data

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    NIH CTSI TL1: Predicting short-term and long-term effects of spinal cord stimulation: implications for clinical practice

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    Pilot Award: Precision Dose:Personalized Radiation Dose Optimization for Multimodal Imaging

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    Modeling, Estimating and Reasoning in Limited Data Brain

    In the current era of invigorating brain research, there is emerging attention in leveraging machine learning in understanding the brain, particularly exploring the brain dynamics. With the ever-increasing amount of neuroscience data, new challenges and opportunities arise for brain dynamics analysis, such as data-driven reconstruction and computer-aided diagnosis. However, there are few attempts to bridge the semantic gaps between the raw brain imaging data and the diagnosis. We will develop robust and data-driven techniques for the purpose of modeling, estimating functional parameters from the limited data brain images, and making decision support practical based on efficient direct estimation of the brain dynamics. This is an interdisciplinary research combining medical image analysis, machine learning, neuroscience, and the domain expertise.

Current Teaching

  • 2023 Fall

    BME4931/6938 Medical Artificial Intelligence

    1. Understand the basic concepts and techniques of machine learning.
    2. Formulate machine learning problems corresponding to different applications.
    3. Understand a range of machine learning algorithms along with their strengths and weaknesses.
    4. Acquire skills of using recent machine learning software for solving practical problems.
    5. Apply machine learning algorithms to solve problems of moderate complexity.
    6. Apply machine learning algorithms to a real-world problem, optimize the model learned and report the expected performance that can be achieved by applying the models.

    Instructor Evaluation: 5.00/5.00, Course Evaluation: 4.75/5.00

Teaching History

    • 2023 Spring

      BME6938/CIS6930 Multimodal Data Mining

      Course Objectives: 1. Understand multimodal data mining in the biomedical domain; 2. Understand the concept, approaches, and limitations in analyzing different modalities of biomedical data. 3. Learn to use biomedical data programming libraries and skills to analyze multimodal biomedical data.

    • 2022 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

    • 2021 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

      Instructor Evaluation: 4.63/5.00, Course Evaluation: 4.38/5.00

    • 2021 Spring

      BME6938 Multimodal Data Mining

      Course Objectives: 1. Understand multimodal data mining in the biomedical domain; 2. Understand the concept, approaches, and limitations in analyzing different modalities of biomedical data. 3. Learn to use biomedical data programming libraries and skills to analyze multimodal biomedical data.

      Instructor Evaluation: 4.92/5.00, Course Evaluation: 4.70/5.00

    • 2020 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

      Instructor Evaluation: 4.83/5.00, Course Evaluation: 4.53/5.00 (Historical highest for this course)

    • 2019 Spring

      BME6938 Multimodal Data Mining

      Course Objectives: 1. Understand multimodal data mining in the biomedical domain; 2. Understand the concept, approaches, and limitations in analyzing different modalities of biomedical data. 3. Learn to use biomedical data programming libraries and skills to analyze multimodal biomedical data.

      Instructor Evaluation: 4.87/5.00

    • 2019 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

    • 2018 Fall

      BME3053C Computer Applications for BME

    • 2017 Fall

      BME3053C Computer Applications for BME

      The objectives this course are: 1) Develop a proficiency in the use of computer programming (specifically, MATLAB) to analyze biomedical measurements. 2) Develop an understanding of biomedical engineering problems that require quantitative analysis and visualization.

    2017 Spring

    CAP 5771 Principles of Data Mining

  • 2017 Spring

    CAP 4770 Introduction to Data Mining

  • 2016 Fall

    CAP 4770 Introduction to Data Mining

  • 2016 Spring

    CAP 5610 Machine Learning

  • 2015 Fall

    CAP 4770 Introduction to Data Mining

    Data Mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It has gradually matured as a discipline merging ideas from statistics, machine learning, database and etc. This is an introductory course for junior/senior computer science undergraduate students on the topic of Data Mining. Topics include data mining applications, data preparation, data reduction and various data mining techniques (such as association, clustering, classification, anomaly detection).

  • 2015 Spring

    CAP 5610 Machine Learning

    Machine learning is concerned with the question of how to make computers learn from experience. The ability to learn is not only central to most aspects of intelligent behavior, but machine learning techniques have become key components of many software systems. For examples, machine learning techniques are used to create spam filters, to analyze customer purchase data, to understand natural language, or to detect fraudulent credit card transactions.

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LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images

CodeLAVA is an XAI framework that aims to exploit neuron-level explanation as auxiliary information during learning process to make a high-resolution AD continuum prediction.

Citation

Nooshin Yousefzadeh, Charlie Tran, Adolfo Ramirez-Zamora, Jinghua Chen, Ruogu Fang, My T. Thai. "LAVA: Granular Neuron-Level Explainable AI for Alzheimer's Disease Assessment from Fundus Images" In Nature Scientific Reports, April 2024.

RetinaPD

CodeThe purpose of this project is for the binary classification of Parkinson's Disease from UK Biobank Fundus Imaging.

Citation

Charlie Tran, Kai Shen, Kang Liu, Akshay Ashok, Adolfo Ramirez-Zamora Jinghua Chen, Yulin Li, Ruogu Fang. "Deep Learning Predicts Prevalent and Incident Parkinson’s Disease From UK Biobank Fundus Imaging" In Nature Scientific Reports, 2024.

DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability

CodeIn this work, we propose DOMINO++, a dual-guidance and dynamic domain-aware loss regularization focused on OOD generalizability.

Citation

Skylar E. Stolte, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam J. Woods, Kevin Brink, Matthew Hale, and Ruogu Fang. "DOMINO++: Domain-aware Loss Regularization for Deep Learning Generalizability" In MICCAI, 2023

DOMINO: Domain-aware Model Calibration in Medical Image Segmentation

CodeDOMINO is an open-source package for domain-aware model calibration that leverages the semantic confusability and hierarchical similarity between class labels in multi-class classification/segmentation to improve model calibration while not sacrificing or even improving model accuracy.

Citation

Stolte, Skylar E., Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam J. Woods, Kevin Brink, Matthew Hale, and Ruogu Fang. "DOMINO: Domain-Aware Model Calibration in Medical Image Segmentation." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 454-463. Springer, Cham, 2022.

EEGAILab

CodeToolbox for analyzing and visualizing dipole sources from resting electroencephalography data.

Citation

  • Kyle B. See, Rachel Ho, Stephen Coombes, Ruogu Fang, “TL1 Team Approach to Predicting Response to Spinal Cord Stimulation for Chronic Low Back Pain”, Journal of Clinical and Translational Science, Mar 2021.
  • Kyle B. See, Rachel Louise Mahealani Judy, Stephen Coombes, Ruogu Fang, “TL1 Team Approach to Predicting Short-term and Long-term Effects of Spinal Cord Stimulation” Journal of Clinical and Translational Science, Jul 2020.

CADA

CodeMulti-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation

Citation

  • Liu, Peng, Charlie T. Tran, Bin Kong, and Ruogu Fang. "CADA: Multi-scale Collaborative Adversarial Domain Adaptation for unsupervised optic disc and cup segmentation." Neurocomputing 469 (2022): 209-220.

Domain Shift

CodeAdversarial Discriminative Adaptation and Ensembling Collaborative Learning for Domain Shift in Unsupervised Optic Disc and Cup Segmentation

Citation

  • Liu, Peng, Bin Kong, Zhongyu Li, Shaoting Zhang, and Ruogu Fang. "CFEA: collaborative feature ensembling adaptation for domain adaptation in unsupervised optic disc and cup segmentation." In International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 521-529. Springer, Cham, 2019.

EvoNet

CodeDeep evolutionary networks with expedited genetic algorithms for medical image denoising

Citation

  • Liu, Peng, Mohammad D. El Basha, Yangjunyi Li, Yao Xiao, Pina C. Sanelli, and Ruogu Fang. "Deep evolutionary networks with expedited genetic algorithms for medical image denoising." Medical image analysis 54 (2019): 306-315.

CT Perfusion Toolbox

CodeToolbox for CT perfusion image processing and computation, including image loading, preprocessing, reconstruction, quantification, and example code.

Citation

  • Fang, R., Chen, T. and Sanelli, P.C., 2013. Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning. Medical image analysis, 17(4), pp.417-428.
  • Fang, R., Zhang, S., Chen, T. and Sanelli, P.C., 2015. Robust low-dose CT perfusion deconvolution via tensor total-variation regularization. IEEE transactions on medical imaging, 34(7), pp.1533-1548.

Tensor Total Variation

CodeToolbox for Tensor Total Variation quantfication of CT perfusion parameters in low-dose CT perfusion.

Citation

  • Fang, R., Zhang, S., Chen, T. and Sanelli, P.C., 2015. Robust low-dose CT perfusion deconvolution via tensor total-variation regularization. IEEE transactions on medical imaging, 34(7), pp.1533-1548.
  • Fang, R., Sanelli, P.C., Zhang, S. and Chen, T., 2014, September. Tensor total-variation regularized deconvolution for efficient low-dose CT perfusion. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 154-161). Springer, Cham.

Sparse High-Dose Induced Patlak Model for Permeability

CodeSparse high-dose induced Patlak model for Permeability quantification in low-dose CT perfusion.

Citation

  • Fang, R., Karlsson, K., Chen, T. and Sanelli, P.C., 2014. Improving low-dose blood–brain barrier permeability quantification using sparse high-dose induced prior for Patlak model. Medical image analysis, 18(6), pp.866-880.

Sparse Perfusion Deconvolution Toolbox

CodeSparse perfusion deconvolution toolbox using dictionary learning and sparse coding for low-dose CT perfusion parameters quantification.

Citation

  • Fang, R., Chen, T. and Sanelli, P.C., 2012, October. Sparsity-based deconvolution of low-dose perfusion CT using learned dictionaries. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 272-280). Springer Berlin Heidelberg.
  • Fang, R., Chen, T. and Sanelli, P.C., 2013. Towards robust deconvolution of low-dose perfusion CT: Sparse perfusion deconvolution using online dictionary learning. Medical image analysis, 17(4), pp.417-428.

Kinship Classification Dataset

DataFamily101 Dataset: first large-scale dataset of families across several generations. It contains 101 different family with distinct family names, including 206 nuclear families, 607 individuals, with 14,816 images.

Citation

  • Ruogu Fang, Andrew C. Gallagher, Tsuhan Chen, Alexander Loui. Kinship Classification by Modeling Facial Feature Heredity. IEEE International Conference on Image Processing, Australia, 2013 (ICIP '13)

Kinship Verification Dataset

DataCornell Kinship Verification Dataset (143 pairs of parents and children)

Citation

  • Ruogu Fang, Kevin D. Tang, Noah Snavely, Tsuhan Chen. Towards Computational Models of Kinship Verification. IEEE International Conference on Image Processing, Hong Kong, September 2010 (ICIP '10)

AI across the BME curriculum at the University of Florida

Dr. Ruogu Fang prepares students in a Medical AI course for a world where AI literacy is an essential part of the medical field.

Leveraging artificial intelligence to improve healthcare

Dr. Fang with a team of students and faculty members leverage deep neural networks to predict HIV.

National Academy of Science, Engineering, and Medicine Workshop

Dr. Fang presents a workshop presentation on the impact of AI in the medical & clinical environment.

UF Engineering - Forward & Up

Our SMILE Lab is featured on the Forward & Up video by the College of Engineering!

ABC Action News - Supercomputing AI

Dr. Fang is interviewed by ABC Action News in a news segment on the University of Florida's supercomputer, HiPerGator.

July 31, 2024

AI Across the curriculum at UF.

Dr. Ruogu Fang prepares students in a Medical AI course for a world where AI literacy is an essential part of the medical field.

July 16, 2024

Computer Vision News & Medical Imaging News.

Dr. Fang with a team of students and faculty members leverage deep neural networks to predict HIV.

May 2024

Computer Vision News & Medical Imaging News.

Dr. Ruogu Fang receives the inaugural AI Course Award for her exceptional leadership in developing and teaching the Medical Artificial Intelligence course at UF!

March 25-26, 2024

A Workshop on Exploring the Bidirectional Relationship Between Artificial Intelligence and Neuroscience.

A 1.5-day public workshop hosted by the National Academies of Sciences, Engineering, and Medicine explores the application of artificial intelligence in neuroscience research.

November 20, 2023

Study reveal bias in AI tools when diagnosing women's health issue

Machine learning algorithms designed to diagnose a common infection that affects women showed a diagnostic bias among ethnic groups, University of Florida researchers found.

April 6, 2023

Your eyes may be a window into early Alzheimer's detection

Scientists have linked certain changes in the retina to mild cognitive impairment-which may someday help identify the early signs of dementia.

March 30, 2023

UF Herbet Wertheim College of Engineering - Forward & Up

See how our college is creating societal values in the far future and dissolving walls between the university and industry research and development in the latest college video.

February 14, 2023

Supercomputer at UF harnesses the awesome power of AI

At the University of Florida, inside a brick building with fake windows, lives a supercomputer called HiPerGator, one of the fastest in the world to harness the power of artificial intelligence.

January 10, 2023

Computer Vision News

The Medical Imaging section of Computer Vision News featured SMILE Lab, Dr. Fang, and Ph.D. student Skylar E. Stolte on our artificial intelligence research in brain health and aging.

April 21, 2022

UF Innovate - Tech Tuesday

In today's Tech Tuesday, UF Innovate host Lauren Asmus introduces us to biomedical engineer, Dr. Ruogu Fang. Dr. Fang is an assistant professor at the University of Florida who made an important discovery when studying the brain and the eye.

March 8, 2022

Fang recieves HWCOE 2022 Faculty Award for Excellence in Innovation.

As a key faculty member of HWCOE in the space of data science and AI, Dr. Fang’s innovative research output and impact is fundamental to the latest HWCOE initiative to embrace AI-driven innovation.

February 2, 2022

UF, NVIDIA partner to speed brain research using AI

University of Florida researchers joined forces with scientists at NVIDIA, UF’s partner in its artificial intelligence initiative, and the OpenACC organization to significantly accelerate brain science as part of the Georgia Tech GPU Hackathon held last month.

January 10, 2022

Artificial Intelligence Prevents Dementia?

A team at the University of Florida is using targeted transcranial direct current stimulation to save memories. “It’s a weak form of electrical stimulation applied to the scalp. And this weak electric current actually has the ability to alter how the neurons behave,” continued Woods.

December 6, 2021

UF study shows artificial intelligence’s potential to predict dementia

New research published today shows that a form of artificial intelligence combined with MRI scans of the brain has the potential to predict whether people with a specific type of early memory loss will develop Alzheimer’s disease or other form of dementia.

July 19, 2021

UF researchers fight dementia using AI technology to develop new treatment

If presented with current models of electrical brain stimulation a decade ago, Dr. Adam J. Woods would have thought of a science-fiction movie plot. Artificial intelligence now pulls the fantasy out of screens and into reality.

July 16, 2021

Staving off dementia is focus of University of Florida study

Researchers at the University of Florida have found a therapy that holds promise for preventing dementia by combining non-invasive brain stimulation with brain games.

July 14, 2021

UF researchers using artificial intelligence to develop treatment to prevent dementia

UF researchers are developing a method they hope will prevent Alzheimer’s and Dementia. Dr. Ruogu Fang and Dr. Adam Woods are working to personalize brain stimulation treatments to make them as effective as possible.

July 13, 2021

UF researchers use AI to develop precision dosing for treatment aimed at preventing dementia

UF researchers studying the use of a noninvasive brain stimulation treatment paired with cognitive training have found the therapy holds promise as an effective, drug-free approach for warding off Alzheimer’s disease and other dementias.

February 2021

Our eyes may provide early warning signs of Alzheimer’s and Parkinson’s

Forget the soul — it turns out the eyes may be the best window to the brain. Changes to the retina may foreshadow Alzheimer’s and Parkinson’s diseases, and researchers say a picture of your eye could assess your future risk of neurodegenerative disease.

December 4, 2020

Eye Blood Vessels May Diagnose Parkinson's Disease

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson's disease, according to research being presented at the annual meeting of the Radiological Society of North America.

December 1, 2020

UF researchers are looking into the eyes of patients to diagnose Parkinson's Disease

With artificial intelligence (AI), researchers have moved toward diagnosing Parkinson’s disease with, essentially, an eye exam. This relatively cheap and non-invasive method could eventually lead to earlier and more accessible diagnoses.

November 29, 2020

Blood Vessels in the Eye May Diagnose Parkinson's Disease

Using an advanced machine-learning algorithm and fundus eye images, which depict the small blood vessels and more at the back of the eye, investigators are able to classify patients with Parkinson's disease compared against a control group.

November 29, 2020

Eye exam possible test to determine Parkinson's disease

Scientists have determined a simple eye exam combined with powerful artificial intelligence (AI) machine learning technology that could provide early detection of Parkinson's disease.

November 25, 2020

Scientists Are Looking Into The Eyes Of Patients To Diagnose Parkinson’s Disease

With artificial intelligence (AI), researchers have moved toward diagnosing Parkinson's disease with, essentially, an eye exam. This relatively cheap and non-invasive method could eventually lead to earlier and more accessible diagnoses.

November 23, 2020

Eye Exam Could Lead to Early Parkinson’s Disease Diagnosis

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson’s disease, according to research being presented at the annual meeting of the Radiological Society of North America.

November 23, 2020

Simple Eye Exam With Powerful Artificial Intelligence Could Lead to Early Parkinson’s Disease Diagnosis

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson’s disease, according to research being presented at the annual meeting of RSNA.

November 23, 2020

RSNA 20: AI-Based Eye Exam Could Aid Early Parkinson’s Disease Diagnosis

A simple eye exam combined with powerful artificial intelligence (AI) machine learning technology could provide early detection of Parkinson’s disease, according to research being presented at the annual meeting of the Radiological Society of North America.

May 31, 2017

Fang selected to ACM's Future of Computer Academy

Dr. Ruogu Fang, incoming assistant professor in the J. Crayton Pruitt Family Department of Biomedical Engineering, has been selected as a member of the Association for Computing Machinery’s (ACM) inaugural Future Computing Academy (FCA).

May 31, 2016

Researcher says head CT radiation dose can be reduced by 90 percent

Working with radiologists at Weill Cornell Medicine, Dr. Ruogu Fang, Ph.D., has applied machine learning and mathematical algorithms to manipulate low-dose CT perfusion images on stroke patients.

May 25, 2016

Professor uses computer science to reduce patients' exposure to radiation from CT scans

When a doctor orders a CT perfusion scan, most people don’t give it a second thought as it’s necessary to evaluate serious medical conditions such as stroke.

December 7, 2011

Facial recognition software spots family resemblance

FACIAL recognition software that’s as good as people at spotting family resemblances could help to reunite lost family members – or help the likes of Facebook work out which of your friends are blood relatives.

For Prospective Students

Postdoc

We are hiring a Postdoc Researcher. Proficiency with Python and deep learning is required. Experience with Pytorch/Tensorflow, FSL/FreeSurfer/SPM are preferred. Please check out the job HERE.

Please send me an email at ruogu.fang@bme.ufl.edu with the following:

  • Subject line: "Postdoc Applicant: YOUR_NAME - YOUR_UNIVERSITY"
  • CV
  • Transcript
  • Cover letter
  • Contact information of three references

Ph.D. Applicants:

For Ph.D. applicants interested in working in my lab, you can apply for the Ph.D. program in Biomedical Engineering, Electrical and Computer Engineering, Computer Science, or other related programs at UF.

Please send me an email at ruogu.fang@bme.ufl.edu with the following before you apply:

  • Subject line: "PhD Applicant: YOUR_NAME - YOUR_UNIVERSITY"
  • CV
  • Transcript
  • Overview of research experiences

Current or Incoming Master Students:

If you are already a master student or have received admission to BME/ECE/CISE or related programs at the University of Florida, first complete the application form below.

Please complete the application form

Please send me an email at ruogu.fang@bme.ufl.edu with the intention for voluntary research along with the following:

  • Subject line: "MS Applicant: YOUR_NAME - YOUR_UNIVERSITY"
  • CV
  • Transcript

Undergraduate Students:

We take undergraduates from all years and prefer students who have a good math and coding background, or at least the strong motivation and interest to build a solid math and coding background.

Please complete the application form

This form is always open, but the form will only be reviewed during the recruitment period. The official recruitment period begins at the end of Fall and Spring semester. More details are listed below.

  • The recruitment consists of 1 review process and 1 (or additional) interview process.
  • The Fall recruitment review begins June 15th (application should be submitted no later than June 14 23:59pm).
  • The Spring recruitment review begins December 15th (application should be submitted no later than December 14 23:59pm).
  • There are no exceptions for the late submission of the application.

Prerequisite: Complete Coursera Machine Learning course by Andrew Ng before you can officially join the lab. (Free)

Co-requisite: Students who have joined our lab (including PhD, MS, and UG) are strongly encouraged to complete the following courses online during their time in the lab:


NSF Research Experience for Undergraduate (REU)

  • The Research Experiences for Undergraduates (REU) site at University of Florida (UF) provides research experience to undergraduate students a six-month/1-year program.
  • Students will work on cutting-edge research in the area of AI for brain health and brain-inspired AI under the supervision of Dr. Fang and her graduate students.
  • Improve oral and written comprehension and communication of technical knowledge.
  • Spark an interest in continuing STEM research in graduate schools.

Requirements

  • Applicant must be a U.S. citizen or permanent resident of the United States.
  • Applicant must be and remain an undergraduate student in good standing.

All students that meet these requirements are welcome to apply! We are particularly interested in broadening participation of underrepresented groups in STEM, and women and minorities are especially encouraged to apply. We also aim to provide research experiences for students who have limited exposure and opportunities to participate in research.

How to apply

  • Send an email to Dr. Fang entitled "REU Application_YourName" with your (i) a CV; (ii) information describing your research skills and experience; (iii) your goals and research interests for your REU research; (iv) contact details for two references. Applicants are considered on a rolling basis until filled. The deadline for 2022 application is April 1, 2022.
  • We consider applications on a rolling basis until all positions are filled.

We look forward to your smile at SMILE Lab to make your day!

At My Office

J287 Biomedical Science Building
University of Florida
1275 Center Dr.
Gainesville, FL, 32611.