Speaker Bios
Monica Agrawal
Assistant Professor of Biostatistics & Bioinformatics
Duke University
Dr. Monica Agrawal is an assistant professor at Duke University, jointly appointed between the Department of Biostatistics and Bioinformatics and the Department of Computer Science. Her research tackles diverse challenges including scalable clinical information extraction, smarter electronic health records, and human-AI interaction. She has been named a Duke Whitehead Scholar, a Rising Star in EECS, and a finalist for the AMIA Doctoral Dissertation award. Dr. Agrawal earned her PhD in Computer Science at MIT in 2023 and is also a co-founder of Layer Health.
▲ Top of Page
Heng Huang
Brendan Iribe Endowed Professor in Computer Science, Electrical and Computer Engineering, and Institute of Health Computing
University of Maryland College Park
Dr. Heng Huang is a Brendan Iribe Endowed Professor in Computer Science, Electrical and Computer Engineering, and Institute of Health Computing at the University Maryland College Park. Dr. Huang received the PhD degree in Computer Science at Dartmouth College. His research areas include machine learning, AI, and biomedical data science. Dr. Huang has published more than 330 papers in top-tier conferences and many papers in premium journals, such as ICML, NeurIPS, KDD, RECOMB, ISMB, ICCV, CVPR, Nature Communications, Nature Machine Intelligence, Nucleic Acids Research, Bioinformatics, Medical Image Analysis, Journal of Machine Learning Research, IEEE TPAMI, TMI, TIP, TKDE, TNNLS, etc. As PI, Dr. Huang currently is leading NIH R01s, U01 (AI4AD consortium), and multiple NSF funded projects on machine learning, AI, imaging-omics, precision medicine, electronic medical record data analysis and privacy-preserving, smart healthcare, and cyber physical system. He is a Fellow of AIBME and served as the Program Chair of ACM SIGKDD Conference 2020.
▲ Top of Page
Bang Liu
Assistant Professor, Canada CIFAR AI chair
Department of Computer Science and Operations Research
Université de Montréal & Mila & Courtois Institute
Bang Liu is an Assistant Professor in the Department of Computer Science and Operations Research (DIRO) at the University of Montreal (UdeM). He is a member of the RALI laboratory (Applied Research in Computer Linguistics) of DIRO, a member of Institut Courtois of UdeM, an associate member of Mila – Quebec Artificial Intelligence Institute, and a Canada CIFAR AI Chair. His research interests primarily lie in the areas of natural language processing, multimodal & embodied learning, theory and techniques for AGI (e.g., understanding and improving large language models, intelligent agents), and AI for science (e.g., material science, health). He has received the WAIC Yunfan Award (Rising Star) 2024 (15 people worldwide), The Web Conference 2023 Best Paper Award Nomination, University of Montreal Research Excellence Award 2022, and George Walker PhD Thesis Award. He has published more than 90 papers and tutorials in high-level conferences and journals, proposed the first large-scale model of materials science, promoted NLP research based on graph learning, and his research have been widely deployed in industry applications with billion-level users.
▲ Top of Page
Jie Liu
Associate Professor of Computational Medicine and Bioinformatics and and Associate Professor of Computer Science & Engineering
University of Michigan
Dr. Liu’s research lab develops computational methods, tools, and resources for understanding the human genome and diseases such as diabetes. Recently, the methodology focuses are knowledge graphs and foundation models. They have developed a knowledge graph GenomicKB to accumulate human-readable knowledge about the human genome. They have extracted genomic knowledge from PubMed and developed another knowledge graph GLKB. They have also developed a genomic foundation model EPCOT which comprehensively predicts multiple genomic modalities.
The lab currently participates in several NIH consortia, including 4DN, IGVF, HIRN, CFDE, and the recent PanKbase program. In particular, Dr. Liu co-leads the Machine Learning Focus Group at IGVF, co-leads the Data/Metadata Working Group at PanKbase, and leads the development of PanKgraph, the knowledge graph within the PanKbase system.
▲ Top of Page
Qiao Liu
Assistant Professor of Biostatistics
Yale University
Dr. Qiao Liu is an incoming Assistant Professor in the Department of Biostatistics at Yale University. His research lies at the intersection of statistics, artificial intelligence, and computational biology, where he develops practical statistical and AI-driven tools with both theoretical and applied significance. His work leverages advances in generative AI to address high-dimensional statistical challenges, including density estimation and causal inference, with broad applications in single-cell genomics, multi-omics data integration, and genomic large language models. Dr. Liu has authored over 40 publications in leading journals and conferences, including Nature Machine Intelligence, PNAS, Genome Biology, Nucleic Acids Research, ISMB, ECCB, NeurIPS and MICCAI. His contributions have been recognized with prestigious honors, including the NIH Pathway to Independence Award.
Sriram Sankararaman
Professor of Computer Science, Human Genetics, and Computational Medicine
University of California, Los Angeles
Dr. Sankararaman is interested in questions at the interface of computer science, statistics, and biology. Dr. Sankararaman develops statistical and computational methods to make sense of complex, high-dimensional datasets that are being generated in the fields of genomics and medicine to answer questions ranging from how humans have evolved to what are the biological underpinnings of diseases to how we can improve the diagnosis and treatment of diseases. To pursue these questions, he develops and extend tools from a diverse set of disciplines including machine learning, algorithms, optimization, high-dimensional statistics, and information theory. His work has led to the identification of disease genes in diverse populations, such as African Americans and Latinos; to the discovery of interbreeding between humans and Neanderthals; and guidelines for how genetic data can be shared without compromising privacy. Dr. Sankararaman earned a PhD in computer science from the University of California, Berkeley, and completed postdoctoral training at Harvard Medical School. He is the recipient of the Alfred P. Sloan Foundation Research Fellowship, the Okawa Foundation grant, the UCLA Hellman fellowship, the NIH Pathway to Independence Award, a Simons Institute fellowship, and a Harvard Science of the Human Past fellowship as well as the Northrop-Grumman Excellence in Teaching Award at UCLA. His work is supported by the National Science Foundation and the National Institutes of Health.
▲ Top of Page
Fei Wang
Assistant Professor of Healthcare Policy and Research
Weill Cornell Medical College
Fei Wang (https://wcm-wanglab.github.io/index.html) is currently a tenured Professor of Health Informatics in Department of Population Health Sciences at Weill Cornell Medicine (WCM), where he also holds a secondary appointment as a Professor in Department of Emergency Medicine. Dr. Wang is the Founding Director of the WCM Institute of AI for Digital Health (AIDH) and an Adjunct Scientist at Hospital for Special Surgery (HSS). His research interest is machine learning and artificial intelligence in biomedicine. Dr. Wang has published over 350 papers on the major venues of AI and biomedicine, which have received more than 35K citations to date. His H-index is 85. Dr. Wang is an elected fellow of American Medical Informatics Association (AMIA), American College of Medical Informatics (ACMI) and International Academy of Health Sciences and Informatics (IAHSI), and a distinguished member of Association for Computing Machinery (ACM).
▲ Top of Page
May Dongmei Wang
Professor, Wallace H. Coulter Distinguished Faculty Fellow, Kavli Fellow, GCC Distinguished Cancer Scholar
Georgia Tech and Emory University
Dr. May Dongmei Wang is Wallace H. Coulter Distinguished Faculty Fellow and full professor of BME and ECE at Georgia Institute of Technology (GT) and Emory University (EU) in Atlanta, Georgia, USA. She received BEng from Tsinghua University China and MS/PhD from GT. She is Director of Biomedical Big Data Initiative, Georgia Distinguished Cancer Scholar, Board of Directors of American Board of AI in Medicine, Petit Institute Faculty Fellow, Kavli Fellow, AIMBE Fellow, IAMBE Fellow, IEEE Fellow, and ELATES Fellow. Dr. Wang works in Biomedical AI, Big Data, Health Informatics, and Metaverse for predictive, personalized, and precision health (pHealth). She published over 330 articles in referred journals and conference proceedings and has delivered more than 340 invited and keynote lectures. She was awarded GT Outstanding Faculty Mentor for Undergrad Research, and EU MilliPub Award for a high-impact paper cited over 1,000 times. Dr. Wang is the Senior Editor for IEEE Journal of Biomedical and Health Informatics (JBHI), an Associate Editor for IEEE Transactions on BME and IEEE Reviews in BME. She is a panelist for NIH CDMA Study Section, NSF Smart and Connect Health, and Brain Canada. Dr. Wang is ACM Special Interest Group in Bioinformatics (SGIBio) Chair, IEEE Future Directions Committee Member, and The International Academy of Medicine and Biological Engineering (IAMBE) Governing Council Secretary. She was 2014-2015 IEEE Engineering in Medicine and Biology Society (IEEE-EMBS) Distinguished Lecturer, an Emerging Area Editor for Proceedings of National Academy of Sciences, 2022 GT President LeadingWomen, 2021 GT Provost Emerging Leaders, and 2018-2021 GT Carol Ann and David Flanagan Distinguished Faculty Fellow. She was 2015-2017 GT BMI Co-Director in Atlanta Clinical and Translational Science Institute (ACTSI), Director of Bioinformatics and Biocomputing Core in NIH/NCI-sponsored U54 CCNE, and Co-Director of GT Center of Bio-Imaging Mass Spectrometry. Her research has been supported by NIH, NSF, CDC, Georgia Research Alliance, Georgia Cancer Coalition, Shriners’ Children, Children’s Health Care of Atlanta, Enduring Heart Foundation, Coulter Foundation, Imlay Foundation, Carol Ann and David Flanagan Foundation, Horizon Europe, Microsoft Research, HP, UCB, and Amazon.
▲ Top of Page
Wei Wang
Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine
University of California Los Angles
Dr. Wang is the Leonard Kleinrock Chair Professor in Computer Science and Computational Medicine at University of California, Los Angeles and the director of the Scalable Analytics Institute (ScAi). She is also a member of the UCLA Jonsson Comprehensive Cancer Center, Institute for Quantitative and Computational Biology, and Bioinformatics Interdepartmental Graduate Program. She received her PhD degree in Computer Science from the University of California, Los Angeles in 1999. She was a professor in Computer Science and a member of the Carolina Center for Genomic Sciences and Lineberger Comprehensive Cancer Center at the University of North Carolina at Chapel Hill from 2002 to 2012, and was a research staff member at the IBM T. J. Watson Research Center between 1999 and 2002. Dr. Wang’s research interests include big data analytics, data mining, machine learning, natural language processing, bioinformatics and computational biology, and computational medicine. She has filed seven patents, and has published one monograph and more than two hundred eighty research papers in international journals and major peer-reviewed conference proceedings, including multiple best paper awards.
Xin Wang
Assistant Professor in the Department of Epidemiology and Biostatistics
University at Albany, SUNY
Dr. Xin Wang is an Assistant Professor in the Department of Epidemiology and Biostatistics, College of Integrated Health Sciences, and AI Plus Institute, University at Albany, SUNY. His research interests include AI in Health and Medical Innovation, Medical Image Analysis, Reinforcement Learning, Large Language Models, Multimedia, and Trustworthy AI.
He has published 90+ papers and has been granted 30+ US patents. He is a Senior Member of IEEE (Since 2020) and the Associate Editor of the IEEE Transactions on Multimedia.
Huaxiu Yao
Assistant Professor at the Department of Computer Science
University of North Carolina at Chapel Hill
Huaxiu Yao is a tenure-track Assistant Professor at the Department of Computer Science with a joint appointment in the School of Data Science and Society, University of North Carolina at Chapel Hill. He was a Postdoctoral Scholar in Computer Science at Stanford University, working with Chelsea Finn. Currently, his research interests focus on both the theoretical and applied aspects of building generalizable and adaptable foundation models, particularly multimodal foundation models. He is also dedicated to applying these methods to real-world data science applications, such as healthcare and bioinformatics. He has authored over 50 publications in leading machine learning venues, such as ICML, ICLR, and NeurIPS, and has served as an (senior) area chair and workshop organizer at ICML, NeurIPS, ICLR, ACL, EMNLP. His research has been recognized with honors such as TMLR Outstanding paper award, KDD 2024 Best Paper Award, Cisco Faculty Award, AAAI 2024 New Faculty Highlights, and UNC Junior Faculty Development Award.
Hongtu Zhu
Professor Department of Biostatistics
University of North Carolina at Chapel Hill
Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science, and Genetics at the University of North Carolina at Chapel Hill. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016, the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019, and the COPSS 2025 Snedecor Award. He has published more than 342 papers in top journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, AOS, JASA, Biometrika, and JRSSB, as well as presenting 58+ conference papers at top conferences, including meetings for Neurips, ICLR, ICML, AAAI, and KDD. He is the coordinating editor of JASA and the editor of JASA ACS.
▲ Top of Page
