IIS-1908299 III: Small: Collaborative Research:
Modeling Multi-Level Connectivity of Brain Dynamics
PIs: Prof. Ruogu Fang and Prof. Mingzhou Ding
University of Florida
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.
The approach consists of three thrusts: (1) multi-scale structural connectivity modeling to quantify brain dynamics beyond a single voxel; (2) multimodal dynamic dictionary learning for mining hidden complementary information; and (3) multicenter evaluation to assess the efficacy of the proposed models at three nationally renowned healthcare systems. Successful project completion would potentially transform the rapidly evolving field of brain dynamics modeling, facilitate basic neuroscience discovery and enable comprehensive identification of neurovascular diseases. Aiming to broaden its impact this project will also implement educational initiatives to expose students, middle school teachers, and medical professionals to 'CS for All,' to foster interests in STEM and cross-disciplinary careers, and to promote research on the convergence of computer science and computational thinking for brain health and neuromedicine.
[Medical Imaging Denoising'24] Yao Xiao*, Kai Huang, Hely Lin*, Ruogu Fang: Medical Image Synthesis: Methods and Clinical Applications, Chapter 7 Medical Imaging Denoising, Taylor & Francis Group, 2024, accepted.
[SI'23] Skylar E. Stolte*, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam J. Woods, Kevin Brink, Matthew Hale, and Ruogu Fang. DOMINO: Domain-aware loss for deep learning calibration, in Software Impacts, vol.15. 2023. https://doi.org/10.1016/j.simpa.2023.100478
[Frontiers'23] Ruogu Fang, Lijun Bai, and Wen Li. Editorial: Frontiers of women in brain imaging and brain stimulation, in Frontiers in Human Neuroscience, 17:1208253. 2023. http://doi.org/10.3389/fnhum.2023.1208253
[BS'23] Alejandro Albizu, Aprinda Indahlastari, Ziqian Huang*, Jori Waner, Skylar E. Stolte*, Ruogu Fang, Adam J. Woods: Machine-Learning Defined Precision tDCS for Improving Cognitive Function. Brain Stimulation, June 4, 2023. DOI: https://doi.org/10.1016/j.brs.2023.05.020 PMID: 37279860
[Neurobiology of Aging'23] Wei-en Wang, Rob Chen, Robin Perry Mayrand, Malek Adjouadi, Ruogu Fang, Steven T. DeKosky, Ranjan Duara, Stephen A. Coombes, David E. Vaillancourt. “Association of Longitudinal Cognitive Decline with Diffusion MRI in Gray Matter, Amyloid, and Tau Deposition”. Neurobiology of Aging, pp.166-178, 2023.
[MICCAI'23] 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. International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2023. Conference Proceedings, Springer.
[MDS'23] Joshua Wong, Sina Aghili-Mehrizi, Reza Forghani, Abbas Babajani, Michael Okun, Christopher Butson, Ruogu Fang. AI-guided microelectrode recording for GPi DBS in Parkinson’s Disease, International Congress of Parkinson’s Disease and Movement Disorders, 2023.
[AAPM'23] Zhuobiao Qiao*, Zeyu Zhang, Zhuoran Jiang, Youfang Lai, Jessica Lee, Dapeng Oliver Wu, Chris Beltran, Ruogu Fang, Lei Ren, Mi Huang. Attention Module Embedded Generative Adversarial Network for Enhancing 3D CBCT Image Quality for Radiomics Analysis.
[BMES'23] Seowung Leem*, Andreas Keil, Peng Liu*, Ruogu Fang, Mingzhou Ding. Can Deep Convolutional Neural Network Associate Emotions with Gabor Patches? Biomedical Engineering Society Annual Meeting, 2023
[BMES'23] Joseph Cox*, Yunchao Yang, Huiwen Ju, Justin Broce*, Ruogu Fang, Large Brain Models: Large-scale 3D Pretrained Deep Learning Models for Neuroimages, Biomedical Engineering Society Annual Meeting, 2023
[SPR'23] Faith E Gilbert, Hannah M Engle, Caitlin M Traiser, Arash Mirifar, Christian Panitz, Jourdan Pouliot, Laura C Ahumada Hernandez, Ethan Smith*, Ruogu Fang, Mingzhou Ding, Andreas Keil: Building and Evaluating an AI Generated, Standardized Affective Picture Dataset, Annual meeting of the Society for Psychophysiological Research, New Orleans LA, September 27 to October 1, 2023.
[ICDCS'23] Hong Huang*, Lan Zhang, Chaoyue Sun*, Ruogu Fang, Xiaoyong Yuan and Dapeng Wu: Distributed Pruning Towards Tiny Neural Networks in Federated Learning, the 43rd IEEE International Conference on Distributed Computing Systems (ICDCS 2023), July 18-21, 2023, Hong Kong (acceptance ratio: 18.9%)
[BMC'22] Zehao Yu; Xi Yang; Gianna L Sweeting*; Yinghan Ma; Skylar E. Stolte*; Ruogu Fang; Yonghui Wu. Identify Diabetic Retinopathy-related Clinical Concepts and Their Attributes Using Transformer-based Natural Language Processing Methods. BMC Medical Informatics and Decision Making. Published Sep 27, 2022.
[MICCAI'22] Skylar Stolte*, Kyle Volle, Aprinda Indahlastari, Alejandro Albizu, Adam Woods, Kevin Brink, Matthew Hale, Ruogu Fang: DOMINO: Domain-aware Model Calibration in Medical Image Segmentation, 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)), Singapore, Sep. 18-22, 2022. (Oral Presentation rate=2.3%, Early acceptance rate=13%) (9 pages) Best Paper Presentation Award Runner Up, Women in MICCAI
[RSNA'22] Garrett Fullerton*, Simon Kato*, Dhanashree Rajderkar, John Rees, Pina Sanelli, Ruogu Fang: MAGIC: Multitask synthesis of contrast-free CT perfusion maps via generative adversarial network, in Annual Meeting of Radiology Society of North America (RSNA), Chicago, Nov.27-Dec.1, 2022.
[SfN'22] Seowung Leem*, Andreas Keil, Peng Liu*, Mingzhou Ding, Ruogu Fang: Differential Aversive and Appetitive Conditioning in Artificial Neural Networks, in Society of Neuroscience Annual Meeting, San Diego, CA. November 12, 2022.
[Frontiers'22] Liu, P.*, Xu, L., Fullerton, G.*, Xiao, Y.*, Nguyen, J.-B., Li, Z., Barreto, I., Olguin, C.,; Fang, R. PIMA-CT: Physical Model-Aware Cyclic Simulation and Denoising for Ultra-Low-Dose CT Restoration. Frontiers in Radiology, 0, 13. 2022
[Frontiers'21] Gullett, J. M., Albizu, A., Fang, R., Loewenstein, D. A., Duara, R., Rosselli, M., Armstrong, M. J., Rundek, T., Hausman, H. K., Dekosky, S. T., Woods, A. J., & Cohen, R. A. Baseline Neuroimaging Predicts Decline to Dementia From Amnestic Mild Cognitive Impairment.Frontiers in Aging Neuroscience, 13, 828. 2021 Dec
[NI'21] See, K. B.*, Arpin, D. J., Vaillancourt, D. E., Fang, R., & Coombes, S. A. Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations. NeuroImage, 245, 118710. 2021 Dec
[MAIN'21] Peng Liu*, Ke Bo, Lihan Cui, Yujun Chen, Charlie Tran*, Mingzhou Ding, Ruogu Fang: A deep neural network for emotion perception, in Montreal AI & Neuroscience 5th Annual Conference. November 29-30, 2021. Best Abstract Award
[SfN'21] Peng Liu*, Ke Bo, Lihan Cui, Yujun Chen, Charlie Tran*, Mingzhou Ding, Ruogu Fang: Emergence of emotion selectivity in deep neural networks trained to recognize visual objects, in Society of Neuroscience Annual Meeting, November 2021.
[SfN'21] Peng Liu*, Ke Bo, Lihan Cui, Yujun Chen, Charlie Tran*, Ruogu Fang, Mingzhou Ding: A deep neural network model for emotion perception, in Society of Neuroscience Annual Meeting, November 2021.
[BMES'21] Garrett Fullerton*, Simon Kato*, Ruogu Fang: MAGIC: Multitask Automated Generation of Inter-modal CT Perfusion Maps via Generative Adversarial Network, in Biomedical Engineering Society Annual Meeting, Orlando, FL. October 6-9, 2021. Department Travel Awards
[BMES'21] Kyle See*, Rachel Judy, Stephen Coombes, Ruogu Fang: Predicting Treatment Outcome in Spinal Cord Stimulation with EEG, in Biomedical Engineering Society Annual Meeting, Orlando, FL. October 6-9, 2021. Department Travel Awards
[BMES'21] Hely Lin*, Ruogu Fang: Ensemble Machine Learning for Alzheimer’s disease Classification from Retinal Vasculature, in Biomedical Engineering Society Annual Meeting, Orlando, FL. October 6-9, 2021.
[NC'21] Liu, P.*, Tran, C.* T., Kong, B., & Fang, R. CADA: Multi-scale Collaborative Adversarial Domain Adaptation for unsupervised optic disc and cup segmentation. Neurocomputing, 469, 209-220. Oct. 2021
[Theoretical and Computational Fluid Dynamics'21] Bhargav Siddani, S. Balachandar., William C. Moore, Yunchao Yang, Ruogu Fang: Machine Learning for Physics-Informed Generation of Dispersed Multiphase Flow Using Generative Adversarial Networks. Theoretical and Computational Fluid Dynamics, 2021. (53 pages, accepted)
[PoF'21] Bhargav Siddani, S. Balachandar, and Ruogu Fang. Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration, in Physics of Fluids, vol. 33, no. 10, Article ID 103323, 18 pages, 2021. https://doi.org/10.1063/5.0066049. Editor's Pick
[Nature SR'21] Jianqiao Tian*, Glenn Smith, Han Guo, Boya Liu, Zehua Pan, Zijie Wang, Shuangyu Xiong, Ruogu Fang: Modular machine learning for Alzheimer's disease classification from retinal vasculature. Nature Scientific Reports, 2021.
[BS'20] Alejandro Albizu, Ruogu Fang, Aprinda Indahlastari, Andrew OShea, Skylar E. Stolte*, Kyle B. See*, Emanuel M. Boutzoukas, Jessica N. Kraft, Nicole R. Nissim and Adam J. Woods: Machine learning and individual variability in electric field characteristics predict tDCS treatment response. Brain Stimulation, 2020.
[NTS'20] Albizu A, Fang R, Indahlastari A, Nissim NR, OShea A, Woods AJ: Determinants of Treatment Response to Transcranial Direct Current Stimulation. 5th Annual NYC Neuromodulation Conference, April 20-22, 2020. Outstanding Presentation by Early Career Scientist Award
[JOSA A'20] Robledo EA, Schutzman R, Fang R, Fernandez C, Kwasinski R, Leiva K, Perez-Clavijo F, Godavarty A: Physiological wound assessment from coregistered and segmented tissue hemoglobin maps. Journal of the Optical Society of America A 2020 Aug 1;37(8):1249-56. DOI:10.1364/JOSAA.394985
[MIA'20] Stolte S*, Fang R: A Survey on Medical Image Analysis in Diabetic Retinopathy. Medical Image Analysis 2020 May 30:101742. DOI:10.1016/j.media.2020.101742
[RSNA'20] Maximillian Diaz*, Jianqiao Tian*, Ruogu Fang: Machine Learning for Parkinsons Disease Diagnosis Using Fundus Eye Images, Annual Meeting of Radiology Society of North America, December, 2020. Featured by Forbes and 30+ Media
[SIIM'20] Yao Xiao*, Manuel M. Arreola, Izabella Barreto, Wesley E. Bolch, W. Christopher Fox, Keith Peters, Dhanashree A. Rajderkar, John H. Rees, and Ruogu Fang: Multi-Series CT Image Super-Resolution by using Transfer Generative Adversarial Network, in Society for Imaging Informatics in Medicine (SIIM) Annual Meeting Austin, Texas, June 24-26, 2020 (Oral)
[ISBI'20] Yao Xiao*, Keith R. Peters, W. Christopher Fox, John H. Rees, Dhanashree A. Rajderkar, Manuel M. Arreola, Izabella Barreto, Wesley E. Bolch, and Ruogu Fang: Transfer-GAN: Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Networks, in IEEE International Symposium on Biomedical Imaging (ISBI), Iowa City, Iowa, April 3-7, 2020. Travel Awards funded by US National Institutes of Health (NIH), National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Cancer Institute (NCI), and Graduate Student Council.
[SPIE Medical Imaging'20] Yao Xiao*, Ruogu Fang: Transfer Generative Adversarial Network for Multimodal CT Image Super-Resolution, in SPIE Medical Imaging, Houston, Texas, Feb 15-20, 2020 (Oral Presentation), Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131306 (17 March 2020) [pdf]
[INS'20] Albizu A, Indahlastari A, Nissim NR, OShea A, Fang R, Woods AJ: Building Personalized Medicinee Medicine Models for Therapeutic Applications of Transcranial Electrical Stimulation, in the 48th Annual Meeting of the International Neuropsychological Society in February 2020
[SNAMC'20] Justin L Brown, Daniel El Basha*, Nathalie Correa, Yao Xiao*, Izabella Barreto, Ruogu Fang, Chan Kim, Wesley E. Bolch: Monte Carlo Dosimetry For CT Brain Perfusion Studies Utilizing Volumetric Acquisitions, in Joint International Conference on Supercomputing in Nuclear Applications + Monte Carlo, May 18-22, 2020.
[ASCPT'20] Marwa Tantawy, Sonal Singh, Guang Yang, Matt Gitzendanner, Yiqing Chen, Yonghui Wu, Ruogu Fang, William Hogan, Yan Gong: ZMAT4 and DOCK9 Variants Associated with Heart Failure in Breast Cancer Patients in the UK Biobank data, in American Society for Clinical Pharmacology and Therapeutics Annual Meeting in Houston, TX, March 18-21, 2020. Presidential Trainee Award, 2020 David J. Goldstein Trainee Award (This award is presented each year to recognize the highest scoring trainee abstract.).
[MIA'20] Prasanna Porwal, Samiksha Pachade, Manesh Kokare, Girish Deshmukh, Jaemin Son, Woong Bae, Lihong Liu, Jianzong Wang, Xinhui Liu, Liangxin Gao, TianBo Wu, Jing Xiao, Fengyan Wang, Baocai Yin, Yunzhi Wang, Gopichandh Danala, Linsheng He, Yoon Ho Choi, Yeong Chan Lee, Sang-Hyuk Jung, Zhongyu Li, Xiaodan Sui, Junyan Wu, Xiaolong Li, Ting Zhou, Janos Toth, Agnes Baran, Avinash Kori, Sai Saketh Chennamsetty, Mohammed Safwan, Varghese Alex, Xingzheng Lyu, Li Cheng,D, Qinhao Chu, Pengcheng Li, Xin Ji, Sanyuan Zhang, Yaxin Shen, Ling Dai, Oindrila Saha, Rachana Sathish, Tania Melo, Teresa Araujo, Balazs Harangi, Bin Sheng, Ruogu Fang, Debdoot Sheet, Andras Hajdu, Yuanjie Zheng, Ana Maria Mendonca, Shaoting Zhang, Aurelio Campilho, Bin Zheng, Dinggang Shen, Luca Giancardo, Gwenole Quellec, Fabrice Meriaudeau: IDRiD: Diabetic Retinopathy Segmentation and Grading Challenge, in Medical Image Analysis, vol. 59, 2020. (First Place in the International Diabetic Retinopathy Grading and Segmentation Challenge) [pdf]
[MIA'20] Jose Ignacio Orlando, Huazhu Fu, Joao Barbossa Breda, Karel van Keer, Deepti R. Bathula, Andres Diaz-Pinto, Ruogu Fang, Pheng-Ann Heng, Jeyoung Kim, JoonHo Lee, Joonseok Lee, Xiaoxiao Li, Peng Liu*, Shuai Lu, Balamurali Murugesan, Valery Naranjo, Sai Samarth R. Phaye, Sharath M. Shankaranarayana, Apoorva Sikka, Jaemin Son, Anton van den Hengel, Shujun Wang, Junyan Wu, Zifeng Wu, Guanghui Xu, Yongli Xu, Pengshuai Yin, Fei Li, Xiulan Zhang, Yanwu Xu, Hrvoje Bogunovic : REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs, in Medical Image Analysis, vol. 59, 2020. [pdf]
[MICCAI'19] Peng Liu*, Bin Kong, Zhongyu Li, Shaoting Zhang, Ruogu Fang: CFEA: Collaborative Feature Ensembling Adaptation for Domain Adaptation in Unsupervised Optic Disc and Cup Segmentation, in Medical Image Analysis and Computer Assisted Intervention, October, 2019. (Early Acceptance Rate = 10%-15%)
[RSNA'19] Yao Xiao*, Manual Arreola, Izabella Barreto, W. Christopher Fox, Keith Peters, Ruogu Fang: Multimodal CT Image Super-Resolution via Transfer Generative Adversarial Network, in Annual Meeting of Radiology Society of North American, December 2019. (Oral presentation)
[BMES'19] Yao Xiao*, Ruogu Fang: Multimodal CT Image Super-Resolution via Transfer-GAN, in Biomedical Engineering Society Annual Meeting, October, 2019, Philadelphia, PA.
[BMES'19] Jianqiao Tian*, Max Diaz*, Ruogu Fang: Deep Learning-based Alzheimer's Disease Classification of FDG-PET and AV45 PET Images, in Biomedical Engineering Society Annual Meeting, October, 2019, Philadelphia, PA.
[BMES'19] Skylar Stolte*, Ruogu Fang: Artificial Intelligence For Automated Diagnosis of Glaucoma in Stereoscopic Images, in Biomedical Engineering Society Annual Meeting, October, 2019, Philadelphia, PA.
[BMES'19] Kyle See*, Ruogu Fang: Classification of Neural Stimulations In The Brain With Super Voxels, in Biomedical Engineering Society Annual Meeting, October, 2019, Philadelphia, PA.
[BMES'19] Skylar Stolte*, Kyle See*, Daniel El Basha*, Ruogu Fang: Retinal Disease Diagnosis Using Mobile Devices, in Biomedical Engineering Society Annual Meeting, October, 2019, Philadelphia, PA.
* indicates students mentored by the PIs
1. Systems And Methods For Functional Task Prediction Using Dynamic Supervoxel Parcellations: Ruogu Fang, Kyle B. See, and Stephen Coombes. PCT/US2022/081801. Filed on December 16, 2022.
2. System And Methods of Predicting Parkinson's Disease Based on Retinal Images Using Machine Learning. Ruogu Fang, Maximillian Diaz. U.S. Patent No. 18/038,517. Filing date: March 24, 2023.
3. Systems And Methods For Predicting Perfusion Images From Non-contrast Scans. Inventor(s): Ruogu Fang, Garrett Carlton Fullerton, and Simon Kato. Ref No: T18616US001 (222111-8310). U.S. Patent No. 18/230,835. Filed on August 7, 2023.
4. Systems And Methods For Image Denoising via Adversarial Learning. Inventors: Ruogu Fang, Peng Liu (T18195US001 (222107-8185)) U.S. Patent Application Serial No. PCT/US2021/043635, Published.
5. A Machine Learning System And Method For Predicting Alzheimer’s Disease Based On Retinal Fundus Images. Inventor(s): Ruogu Fang, Jianqiao Tian. U.S. Patent No. 17/928,345. Filed on November 29, 2022.
6. System and Method for Precision Dosing For Electrical Stimulation of the Brain. Adam Woods, Aprinda Indahlastari, Alexjandro Albizu, Ruogu Fang. U.S. Patent No. 18/018,734. Filed on January 30, 2023.
7. Collaborative Feature Ensembling Adaptation For Domain Adaptation In Unsupervised Optic Disc And Cup Segmentation. Inventor(s): Ruogu Fang, Peng Liu. U.S. Patent Application No. 18/007,366. Filed on January 30, 2023.
8. Neural Network Evolution Using Expedited Genetic Algorithm for Medical Image Denoising. Inventors: Ruogu Fang, Peng Liu. Ref. No: UF#17344, Filed on 9/3/2019, will be published on March 12, 2020. U.S. Utility Patent Application Serial No. 16/558,779
9. Multimodal CT Image Super-Resolution Via Transfer Generative Adversarial Network. Ruogu Fang, Yao Xiao. Ref No: T17996UF001. Provisional Patent Filed on March 25, 2020.
1. DOMINO: DOMINO 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. (Pytorch and MATLAB) https://github.com/lab-smile/DOMINO
2. CADA: Multi-scale Collaborative Adversarial Domain Adaptation for Unsupervised Optic Disc and Cup Segmentation. https://github.com/lab-smile/CADA
3. EEGAILab: Toolbox for analyzing and visualizing dipole sources from resting electroencephalography data. https://github.com/lab-smile/EEGAILab
Peng Liu, Biology and Neuroscience Inspired Deep Learning, Ph.D. Dissertation, University of Florida, 2021.
Yao Xiao, Deep Learning for Multimodal CT Image Quality Enhancement and Radiation Exposure Optimization, Ph.D. Dissertation, University of Florida, 2020
Ruogu Fang: Keynote Talk. "A Tale of Two Frontiers - When Brain Meets AI", Neural Information Processing System (NeurIPS) 2022 workshop on "Medical Imaging Meets NeurIPS", New Orleans, Lousiana, December 2, 2022.
Ruogu Fang, "Artificial Intelligence in Cognitive Aging and Brain-Inspired AI", College of Medicine, Stanford University, November 11, 2022.
Ruogu Fang, "Generative, Trustworthy, and Precision AI in Radiology", Weill Cornell Medical College, Department of Radiology, MRI Research Institute (MRIRI), August, 2023.
Ruogu Fang, "Trustworthy AI and Large Vision Models for Neuroimages", Nvidia Artificial Intelligence Technology Center (NVAITC), May, 2023.
Ruogu Fang, Invited Talk. "Artificial Intelligence and Machine Learning for Cognitive Aging: Novel Diagnosis and Personalized Intervention", UF Intelligent Critical Care Center (IC3), Gainesville, FL, United States, March 6, 2023
Ruogu Fang, Invited Talk. "Artificial Intelligence and Machine Learning for Cognitive Aging: Novel Diagnosis and Precision Intervention", UF Summer Neuroscience Internship Program Neuromedicine Seminar Series, Gainesville, FL, United States, June 20, 2022.
Albizu, A., Fang, R., Indahlastari, A., & Woods, AJ. "Machine-Learning Defined Precision Brain Stimulation", 10th Annual William G. Luttge Research Competition. March 2023.
Albizu, A., Fang, R., Indahlastari, A., Huang, Z., Suen, P., Brunoni, AR., & Woods, AJ. "Machine-Learning and Electric Field Variability Predicts Improvements in Unipolar Depression after Transcranial Direct Current Stimulation", 32nd Annual Institute for Learning in Retirement Aging Research Contest. March 2023.
Albizu, A., Gullett, JR., Zapata, R., Fang, R., & Woods, AJ. "Artificial Intelligence Approaches to Early Detection of Dementia", 13th Annual Meeting of the North Central Florida Chapter of the Society for Neuroscience (SfN). February 2023.
Albizu, A., Fang, R., Indahlastari, A., Huang, Z., Suen, P., Brunoni, AR., & Woods, AJ. "Machine-Learning and Electric Field Variability Predicts Improvements in Unipolar Depression after Transcranial Direct Current Stimulation", 13th Annual UF College of Medicine Research Day. February 2023.
Skylar Stolte, "Domain-Aware Calibration in Medical Image Segmentation", Fall HiPerGator Symposium. Fall 2022.
Charlie Tran, Ruogu Fang: Graph Neural Networks for Alzheimer's Disease From Retinal Imaging, Florida Undergraduate Research Conference, Florida State University, Tallahassee, FL (Virtual), US, April 2021
Graduate: Kyle See, Skylar Stolte, Joseph Cox, Seowung Leem
Undergraduate: Garrett Fullerton, Simon Kato, Brian John Braddock
Spring 2023: BME6938 Multimodal Data Mining
Fall 2022: BME3053C Computer Applications for BME
Fall 2021: BME3053C Computer Applications for BME
Spring 2021: BME6938 Multimodal Data Mining
Fall 2020: BME3053C Computer Applications for BME
Fall 2019: BME3053C Computer Applications for BME
Weekly in-person or online meetings since 2019 on this project