Speakers

Dr. Lifang He is an Assistant Professor in Lehigh University. She received the B.S. degree in Computational Mathematics from Northwest Normal University, and the Ph.D. degree in Computer Science from South China University of Technology. Before joining Lehigh's faculty, she was a postdoctoral associate in the Department of Biostatistics, Epidemiology and Informaticswithin the Perelman School of Medicine at University of Pennsylvania, as well as the Weill Cornell Medical College of Cornell University. Her research interests include machine learning/deep learning, data mining and tensor analysis, with applications in brain connectome analysis.

Dr. Archana Venkataraman is a John C. Malone Assistant Professor in the Department of Electrical and Computer Engineering at Johns Hopkins University. She directs the Neural Systems Analysis Laboratory and is a core faculty member of the Malone Center for Engineering in Healthcare and the Mathematical Institute for Data Science. Dr. Venkataraman’s research lies at the intersection of artificial intelligence, network modeling and clinical neuroscience. Her work has yielded novel insights in to debilitating neurological disorders, such as autism, schizophrenia, and epilepsy, with the long-term goal of improving patient care. Dr. Venkataraman completed her B.S., M.Eng. and Ph.D. in Electrical Engineering at MIT in 2006, 2007 and 2012, respectively. She is a recipient of the MIT Provost Presidential Fellowship, the Siebel Scholarship, the National Defense Science and Engineering Graduate Fellowship, the NIH Advanced Multimodal Neuroimaging Training Grant, the CHDI Grant on network models for Huntington's Disease, numerous best paper awards, and the National Science Foundation CAREER award. Dr. Venkataraman was also named by MIT Technology Review as one of 35 Innovators Under 35 in 2019.

Dr. Liang Zhan is an Associate Professor in the Department of Electrical & Computer Engineering and Bioengineering at University of Pittsburgh, where he also serves as the associate director of the Pittsburgh Center for Artificial Intelligence Innovation in Medical Imaging (CAIIMI). His research areas include brain connectomics and data mining, as well as clinical/translational research on brain diseases, such as Alzheimer’s disease, Parkinson’s disease, bipolar disorder, depression, and Traumatic Brain Injury, etc. He has extensive experience with graph neural network models for brain network data. He received his PhD from University of California, Los Angeles (UCLA) in 2011. Besides NSF Career award, his research is supported by NIH R21, R01s, U01, NSF IIS, and OIS, as well as US Department of Veterans Affairs.

Ying Guo is Professor in the Department of Biostatistics and Bioinformatics at Emory University, an appointed Graduate Faculty of the Emory Neuroscience Program and an Associate Faculty in Emory Department of Computer Science. She is a Founding Member and current Director of the Center for Biomedical Imaging Statistics (CBIS) at Emory University. Dr. Guo’s research focus on developing analytical methods for neuroimaging and mental health studies. Her main research areas include statistical modeling and learning for agreement and reproducibility studies, brain network analysis, multimodal neuroimaging, hierarchical modeling and imaging-based prediction. Dr. Guo has served on the Editorial Boards of Biometrics, Statistics in Biosciences and Psychosomatic Medicine. She is a Fellow of American Statistical Association (ASA) and Chair-Elect of the ASA Statistics in Imaging Section. Dr. Guo has served as the principal investigator on several NIH R01 awards and she is a Standing Member of NIH Emerging Imaging Technologies in Neuroscience (EITN) Study Section. Dr. Guo was the Principal Organizer of The Statistical Methods in Imaging (SMI) 2021 Conference and has also served on the organizing committees of several national and international conferences.

Dr. Yize Zhao is an Associate Professor in the Department of Biostatistics, Yale School of Public Health, Yale University. She is also affiliated with Yale Center for Analytical Sciences, Yale Alzheimer's Disease Research Center, and Yale Computational Biology and Bioinformatics. Dr. Zhao's methodological research focuses on the development of statistical and machine learning methods to analyze large-scale complex data (neuroimaging, -omics, EHRs), Bayesian methods, feature selection, predictive modeling, data integration, missing data and network analysis. Her most recent research agenda includes analytical method developments and applications on brain network analyses, multimodal imaging modeling, imaging genetics, and the integration of biomedical data with EHR data. She also has strong interests in subject matter fields including aging, mental health and cancer.

Dr. Ehsan Adeli is an Assistant Professor at Stanford University, School of Medicine, Department of Psychiatry and Behavioral Sciences, Computational Neuroscience (CNS) Lab. He is also affiliated with the Computer Science Department, Stanford AI Lab (SAIL), Stanford Vision and Learning (SVL), and the Stanford Partnership in AI-Assisted Care (PAC). His research interests include computer vision, computational neuroscience, medical image analysis, and biomedical data science for healthcare. Dr. Adeli is an Executive Co-Director of Stanford AGILE Consortium (Advancing technoloGy for fraIlty & LongEvity), and a faculty member of Stanford Wu Tsai Neurosciences Institute, Stanford Institute for Human-Centered AI, and Stanford Center for AI in Medical Imaging. He is an Associate Editor of IEEE Journal of Biomedical and Health Informatics and the Journal of Ambient Intelligence and Smart Environments. He is a Senior Member of IEEE and has recently served as area chair or associate editor for several top conferences.

Dr. Carl Yang is an Assistant Professor in Emory University. He received his Ph.D. in Computer Science at University of Illinois, Urbana-Champaign and B.Eng. in Computer Science and Engineering at Zhejiang University. His research interests span graph data mining, applied machine learning, knowledge graphs and federated learning, with applications in recommender systems, biomedical informatics, neuroscience and healthcare. Carl's research results have been published in top venues like TKDE, KDD, WWW, NeurIPS, ICML, ICLR, ICDE, SIGIR and ICDM. He also received the Dissertation Completion Fellowship of UIUC in 2020, the Best Paper Award of ICDM in 2020, the Best Paper Award of KDD Health Day in 2022, the Outstanding Paper Award of ML4H in 2022, the Amazon Research Award and multiple Emory internal research awards.