Monica Agrawal is an assistant professor at Duke University, jointly appointed between the Department of Biostatistics and Bioinformatics and the Department of Computer Science. She is also the co-founder of a health technology startup, Layer Health. In her research, she tackles diverse challenges including scalable clinical information extraction, smarter electronic health records, and human-AI interaction.  Dr. Agrawal earned her PhD in Computer Science at MIT and previously obtained a BS/MS in Computer Science from Stanford University. 

Mohammed AlQuraishi is an Assistant Professor in the Department of Systems Biology and a member of Columbia’s Program for Mathematical Genomics, where he works at the intersection of machine learning, biophysics, and systems biology. His lab develops machine learning models for predicting protein structure and function, protein-ligand interactions, and learned representations of proteins and proteomes. They also apply these models in a proteome-wide fashion to investigate the organization, combinatorial logic, and computational paradigms of signal transduction networks, how these networks vary in human populations, and how they are dysregulated in human diseases, particularly cancer. 

Andrea Califano is the Clyde and Helen Wu Professor of Chemical and Systems Biology at Columbia University Irving Medical Center. In October 2023, he stepped down from his positions as Founding Chair of the Department of Systems Biology and Director of the Columbia Genome Center, to take a new role as the President of the Chan Zuckerberg Biohub New York.  The Califano Lab uses a combination of computational and experimental methodologies to reconstruct the regulatory logic of human cells in a genome-wide fashion to identify master regulator proteins responsible for human disease, including cancer and neurodegenerative syndromes. This has resulted in several clinical trials, including a very innovative N-of-1 study for precision cancer medicine. Prof. Califano is also Co-founder of DarwinHealth Inc. 

Bio information will be posted when available

Timothy J. Crimmins received his BS and MD from The George Washington University. He completed residency in internal medicine at The University of Washington in Seattle and board certification in internal medicine in 2001. He completed training in Vascular Medicine at Columbia University through the informal pathway and completed board certification in Vascular Medicine in 2008. Dr. Crimmins serves as Medical Director of the Non-Invasive Vascular Medicine Lab where ultrasound is used for the diagnosis of vascular disorders. Clinical interests include vascular medicine, non-invasive vascular diagnosis, general internal medicine and medical education. He has a master’s certificate in Leadership Development having completed the Master Teacher in Medical Education Program at The George Washington University. His clinical goals of care include patient education, patient-centered care and use of systems to enable high-quality patient care. His education goals include enabling independent thinking and the use of metacongnition to foster an understanding of the underlying thought processes involved in medical decision-making.

Noémie Elhadad is Associate Professor and Chair of the Department of Biomedical Informatics at Columbia University VP&S (Vagelos College of Physicians and Surgeons) and Director of Medical Information Services at NewYork-Presbyterian. She leads the AI at VP&S Initiative and Eve_n, Columbia University’s research initiative on data-powered women’s health. Her research sits at the intersection of artificial intelligence, human-centered computing, and medicine, with a focus on developing novel machine-learning methods. She designs AI-driven tools to support patients and clinicians, ensuring that the AI systems of the future are safe, effective, and advance medicine.

Xi Fu is a 5th-year Ph.D. candidate in the Program of Mathematical Genomics at Columbia University Irving Medical Center. His research focuses on transcriptional regulation, the role of transcription factors in various cancers, and the development of foundation models for biological discovery. He earned a bachelor’s degree in Cell and Molecular Biology and an M.Phil. in Computer Science from The Chinese University of Hong Kong, where he studied the role of non-coding variants in Hirschsprung’s disease and neural crest cell migration. 

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Gamze Gürsoy is the Herbert Irving Assistant Professor of Biomedical Informatics at Columbia University and a Core Faculty Member at the New York Genome Center.  Her lab develops computational and privacy-preserving approaches for genomics and machine learning in healthcare, with a focus on secure data sharing and federated learning. She leads multiple NIH- and foundation-funded projects on genomic privacy and AI in medicine.  

Shalmali Joshi is an Assistant Professor in the Department of Biomedical Informatics at Columbia University, where she leads the reAIM Lab. She is a member of the Data Science Institute and holds affiliations with the SNF Center for Precision Psychiatry & Mental Health and the Department of Computer Science. Dr. Joshi’s research focuses on the development of artificial intelligence and machine learning methodologies to advance scientific inference and predictive modeling in the biomedical sciences. She develops adaptive foundation models for health and medical observational data, and novel approaches for analyzing high-dimensional, multimodal data to enhance the robustness of inference and prediction in health and medicine. Applications of her work span multiple clinical domains, including psychiatry, cardiology, radiology, rheumatology, and neurocritical care. 

Lena Mamykina earned a master’s and PhD in human-computer interaction and human-centered computing at the George Institute of Technology in Atlanta. She earned a master’s and completed a postdoc in biomedical informatics at Columbia University. Her bachelor’s degree in computer science is from the Ukrainian State Maritime Technical University. Dr. Mamykina’s broad research interests include an individual’s sensemaking and problem-solving in context of health management, collective sensemaking within online health support communities, clinical reasoning and decision-making, communication and coordination of work in clinical teams, and ways to support these practices with informatics interventions. She also focuses on analysis of health information technologies and how they are used among critical care teams, as well as social computing platforms for facilitating knowledge sharing within clinical communities, and within online health support groups.

Matthew McDermott is Assistant Professor of Biomedical Informatics at Columbia University. His research is focused on building high-capacity “foundation models” and other representation learning systems over EHR data. Some of his key prior works include Clinical BERT, one of the most widely used pre-trained clinical language models; Structure-inducing Pre-training, a framework for pre-training that enables incorporating domain-specific external knowledge with provable guarantees; and multiple software packages for performing machine learning at scale over structured EHR data, including the recent ESGPT package and MEDS framework. 

Smaranda Muresan is an Associate Professor and the Associate Chair of the Department of Computer Science at Barnard College, Columbia University. Before joining Barnard in 2024, she was a Research Scientist at the Data Science Institute at Columbia University. Her research focuses on human-centric Natural Language Processing for social good and responsible computing. She develops theory-guided and knowledge-aware computational models for understanding and generating language in context (e.g., visual, social, multilingual, multicultural) with applications to computational social science, education, public health and creativity support. Recently, her research interests include explainable models and human-AI collaboration frameworks for high-quality datasets creation, helping humans solve tasks, and aligning AI systems with human values.  

Karthik Natarajan is an Assistant Professor in the Department Biomedical Informatics at Columbia University. He received his BS in computer science at the University of Texas at Austin. After working in the technology sector, he went on to obtain his PhD in biomedical informatics at Columbia University. Dr. Natarajan’s research interests are in operationalizing clinical informatics solutions. His specific area of interest is in applying scalable information retrieval and text processing methods on clinical data in order to build applications that will support both health professionals and researchers. Dr. Natarajan is the Co-Director of the Biomedical Informatics Resource in the CTSA and holds operational responsibilities overseeing some clinical applications at New York Presbyterian Hospital. He is also an active member of the Observational Health Data Sciences and Informatics (OHDSI) collaborative and oversees the local instances of OHDSI at Columbia and NYPH.

Dr. Bethany (Beth) Percha is Adjunct Assistant Professor of Biomedical Informatics at Columbia University and Chief Data and Analytics Officer at NewYork-Presbyterian Hospital. Previously, she was Chief Data Officer at Summit Health/CityMD, a large multispecialty and urgent care network in the northeast, staying on after its acquisition by VillageMD/Walgreens to lead data engineering, governance, and operations across all VillageMD markets and business lines. Prior to her role at Summit, Beth was CTO of the Precision Health Enterprise (PHE), an applied research and product development group at the Mount Sinai Health System, and head of R&D at the Health Data and Design Innovation Center (HD2i), Mount Sinai’s first Silicon Valley outpost. She was formerly Vice President of Research and Development at Kyron, a venture-backed startup building an information retrieval platform for electronic medical records. She has also served as a technical adviser for several other health technology startups in New York and Silicon Valley. The overarching goal of Beth’s research is to develop methods that allow data to inform the practice of medicine. In particular, she sees great potential in the mountains of observational and unstructured data that we collect daily in the form of electronic medical records (EMRs), scientific and clinical documents, and public databases of all kinds. Her main research focus is biomedical natural language processing – using machine learning to extract structured information from the unstructured text of the biomedical research literature and clinical documents. Her dissertation, which won the AMIA Doctoral Dissertation Award, described a new method for mining the unstructured text of the scientific literature to uncover relationships among drugs, genes, diseases and side effects and connect them to important biomedical phenomena like drug-drug interactions and patient-level variation in drug response. She is the creator of the Global Network of Biomedical Relationships (GNBR), a scientific resource that represents the biomedical research literature as a network of structured facts that can be searched or combined to generate new hypotheses. GNBR has been downloaded over 27,000 times to date and is used in academia and industry.

Raul Rabadan is the Gerald and Janet Carrus Professor in the Departments of Systems Biology, Biomedical Informatics and Surgery at Columbia University. He is the director of the Program for Mathematical Genomics (PMG) and the Center for Topology of Cancer Evolution and Heterogeneity. His research interest focuses on developing mathematical, statistical, and computational approaches to model and understand the dynamics of biological systems.  

Bio information will be posted when available

Bio information will be posted when available

Bio information will be posted when available

Xuhai “Orson” Xu is an assistant professor at Columbia DBMI. He completed his postdoc at the MIT Department of Electrical Engineering and Computer Science. He earned his PhD degree in Information Science at the University of Washington, and his bachelor’s degree in Industrial Engineering and Computer Science at Tsinghua University. Dr. Xu’s research straddles multiple disciplines, including human-computer interaction, ubiquitous computing, artificial intelligence, and health. Using everyday sensor data and health records, he develops deployable behavior modeling algorithms to monitor various health and well-being conditions. Leveraging these models, he further designs and deploys intelligent intervention and interaction techniques that help end-users and patients achieve personal health and well-being goals, as well as support health experts in making decisions.

Fei Wang is currently the Associate Dean of Artificial Intelligence and Data Science at Weill Cornell Medicine (WCM), where he is also a tenured Professor and Chief of the Division of Health Informatics and Artificial Intelligence in the Department of Population Health Sciences (primary), and a Professor in the Department of Emergency Medicine (secondary). Dr. Wang is the Founding Director of the WCM Institute of AI for Digital Health (AIDH) and the Founding Co-Director of the WCM Data Coordination Center. He is a Senior Technical Advisor at New York Presbyterian hospital, a Senior Faculty Fellow of Clinical Artificial Intelligence at Cornell Tech, and an Adjunct Scientist at Hospital for Special Surgery (HSS). His research interest is machine learning and artificial intelligence in biomedicine.