Biomedical Data Science
Showing 1-100 of 179 Results
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Shahira Abousamra
Postdoctoral Scholar, Biomedical Data Sciences
BioShahira Abousamra is a Postdoctoral scholar in the department of Biomedical Data Science at Stanford University, working with Dr. Sylvia Plevritis at the Plevritis Lab. She earned her PhD in Computer Science from Stony Brook University in 2024 under the supervision of Dr. Chao Chen and Dr. Dimitris Samaras.
In her research, she integrates mathematical modeling with computer vision to create more robust solutions, particularly in the context of advancing cancer research and enhancing our understanding of the tumor microenvironment. She leverages computational topology and spatial statistics to provide spatial semantic grounding to complement machine learning models. She publishes in top computer vision, artificial intelligence, and medical image analysis conferences including CVPR, ECCV, ICCV, AAAI, and MICCAI. -
Corinne Beck
Administrative Program Manager - Plevritis Lab, Biomedical Data Science
Current Role at StanfordProgram Manager
Plevritis Lab
Department of Biomedical Data Science (DBDS)
School of Medicine | Stanford University -
Vasiliki (Vicky) Bikia
Postdoctoral Scholar, Biomedical Data Sciences
BioDr. Vasiliki Bikia is a Postdoctoral Researcher at Stanford University, jointly affiliated with the Institute for Human-Centered Artificial Intelligence (HAI) and the Department of Biomedical Data Science, where she works under the mentorship of Prof. Roxana Daneshjou. She holds an Advanced Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTH), Greece (2017), and a Ph.D. in Biomedical Engineering from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland (2021). Her doctoral work focused on addressing the clinical need for non-invasive cardiovascular monitoring by combining machine learning with physics-based numerical modeling.
Dr. Bikia's research centers on the development of large multimodal models to improve patient outcome prediction. She is also passionate about building patient-facing chatbots that help individuals better understand complex medical information, ultimately aiming to enhance communication and empower patients in their care journey. Moreover, she has contributed to the Stanford Spezi framework, designing and prototyping the Spezi Data Pipeline tool for enhanced digital health data accessibility and analysis workflows. -
Daniel Bloch
Professor (Research) of Biomedical Data Science (BDS), Emeritus
BioI received my PhD. in Mathematical Statistics in 1967. I joined the research community at the Stanford University School of Medicine, Division of Immunology & Rheumatology, in 1984 as head statistician directing the biostatistics consulting and analytic support of the Arthritis Rheumatism Aging Medical Information System (ARAMIS) and Multipurpose Arthritis Center (MAC) grant-related research programs. In 1993 I was appointed Associate Professor with a joint appointment in the Departments of Medicine and of Health Research & Policy, and am currently Professor of Biostatistics at Stanford University, emeritus since 2007. My contributions to the statistics literature span numerous fields, including methods of sample size estimation, efficiency and bias of estimators, research methods for kappa statistics, non-parametric classification methods and methods of assessing multi-parameter endpoints. I have over 200 peer-reviewed publications. I have been directly involved with the development of numerous criteria rules for classification of diseases and with establishing guidelines for clinical trial research and in proposing responder criteria for osteoarthritis drugs. Since 1987, I have been a consultant on an ad hoc basis to pharmaceutical and biotechnical firms, including both start-up and established companies. I have extensive experience with devices, drugs and biologics and have participated in all aspects of applying statistics to implement investigational plans; e.g.: for protocol development, design of trials, database design. I’ve been a member of the FDA Statistical Advisors Panel, the statistical member on numerous data safety monitoring boards, and frequently represent companies at meetings with the FDA
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Pauline Brochet
Postdoctoral Scholar, Biomedical Informatics
BioPauline Brochet is a French scientist from Souraide, France. She completed her undergraduate studies in Molecular, Cellular and Physiological Biology (BSc, Université Clermont-Auvergne) and earned a Master's degree in Software Development and Data Analysis (MSc, Aix-Marseille Université). Pauline pursued a PhD at TAGC (Theories and Approaches for Genomic Complexity) in Marseille, France.
Under the supervision of Dr. Christophe Chevillard and Dr. Lionel Spinelli, Pauline integrated multi-omics data from human heart tissue to investigate the pathogenic processes associated with Chagas Disease Cardiomyopathy (CCC). Notably, she contributed to the development of ChagasDB, the first database associating key features with the different stages of Chagas disease. Her research identified the involvement of mitochondrial DNA mutations, non-coding RNA, transcription factors, and DNA methylation in various pathogenic processes, all leading to the progression of CCC.
Currently, at Stanford University, under the guidance of Dr. Matthew Wheeler and Dr. Daniel Katz, Pauline is conducting postdoctoral research on multi-omics data analysis as part of the Molecular Transducers of Physical Activity Consortium (MoTrPAC). Her work focuses on identifying key covariable features associated with physical exercise, with the ultimate goal of discovering exercise-mimetic drugs that could help prevent heart diseases. -
Michelle Whirl-Carrillo
Principal Investigator and Director, ClinPGx, Biomedical Data Science
Current Role at StanfordPrincipal Investigator and Director, PharmGKB/ClinPGx
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Ying Cui
Postdoctoral Scholar, Biomedical Data Sciences
BioI am currently a postdoctoral scholar in the Department of Biomedical Data Science at Stanford Universiry. I received my Ph.D. in Biostatistics at Emory University. Prior to Emory, I received my B.S. in Statistics from Nankai University.
My research, located at the intersection of biomedical data science and statistics, is dedicated to enhancing the integration of statistical insights and data science innovations in biomedical research. I have a broad interest in developing innovative statistical methods and easy-to-use computational tools to understand complex associations using nonparametric and semiparametric methods, with recent work exploring their intersections with machine learning and causal inference to advance precision health. I have also been involved in various collaborative researches in multiple domains, including clinical trials and large language models (LLMs). -
Francisco M. De La Vega
Adjunct Professor, Biomedical Data Science
BioFrancisco De La Vega is a distinguished geneticist and computational biologist, and an experienced technical executive, widely recognized for his expertise in clinical and population genomics, and bioinformatics. Currently serving as the Vice President of Hereditary Disease at Tempus Labs, Francisco is spearheading the development of comprehensive germline genetic tests and conducting innovative research into racial disparities in cancer leveraging Tempus’ multimodal Real-World Data. His work focuses on uncovering the connections between genetic ancestry and cancer genome mutational profiles that may help explain the differences in cancer incidence and outcomes across races and ethnicities. In addition to his role at Tempus Labs, Francisco is an Adjunct Professor in the Department of Biomedical Data Science at Stanford University School of Medicine and is a member of the Board of Directors of the International Society of Computational Biology, serving from 2022 to 2025.
Francisco teaches BIODS-235: "Best practices for developing data science software for clinical and healthcare applications" every Winter quarter. -
Idan Gabdank
Senior Biocuration Scientist, Biomedical Data Science
Current Role at StanfordManage data wrangling and curation for innovative cutting-edge single cell and CRISPR screen experiments within the Billion Cell Project funded by CZI, serving as a key member of the Lattice team at Stanford working in close collaboration with CZI and academy labs to ensure standardized data processing and quality control across high-throughput experimental datasets. Integrate AI tools and automate cloud-based pipelines for data validation and curation, streamlining quality assurance processes and reducing manual oversight requirements while maintaining data integrity standards.
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Margarita Geleta
Graduate, Biomedical Data Science
BioMargarita Geleta is a computer science PhD student at University of California, Berkeley (major in Artificial Intelligence and minor in Human-Computer Interaction), and a graduate exchange student at Stanford University. Ms. Geleta received her M.S. in computer science at University of California, Berkeley.
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Li Gong
Scientific Data Curator 3, Biomedical Data Science
Current Role at StanfordProgram manager and senior scientific curator for ClinPGx, coordinator for the ClinGen Pharmacogenomics Interpretation Committee (PGxIC).
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Ryunosuke (Ryan) Goto
Ph.D. Student in Biomedical Data Science, admitted Autumn 2024
BioRyunosuke (Ryan) Goto is a PhD student in Biomedical Data Science and a Knight-Hennessy Scholar. Prior to Stanford, Ryan was a Chief Resident in Pediatrics at Nagano Children's Hospital and the University of Tokyo Hospital. He is working with Prof. Robert Tibshirani and Prof. Jonathan K. Pritchard to develop and apply statistical tools to investigate gene regulatory networks in human traits. Ryan’s work has been published in The Lancet, JAMA Pediatrics, and Pediatrics, among other journals.
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François Grolleau
Postdoctoral Scholar, Biomedical Informatics
BioFrançois Grolleau MD, MPH, PhD is a Postdoctoral Scholar at the Stanford Center for Biomedical Informatics Research. His research work centers on developing and evaluating computational systems that use large language models and other advanced methods from statistics and machine learning to assist medical decision-making.
François is a certified Anesthesiologist and Critical Care Medicine specialist from France. He holds an MPH degree and a PhD in Biostatistics from Paris Cité University. In 2016/2017, he worked as a research fellow in the Department of Health Research Methods, Evidence, and Impact at McMaster University, Canada (Profs Yannick Le Manach and Gordon Guyatt). During his doctorate with Prof. Raphaël Porcher, he utilized causal inference, personalized medicine methods, and statistical reinforcement learning for medical applications in the ICU. -
Dina Hany
Postdoctoral Scholar, Biomedical Data Sciences
BioI am currently a postdoctoral researcher in the laboratory of Prof. Sylvia Plevritis, Department of Biomedical Data Sciences. My current work involves establishing drug testing platforms to evaluate tumor drug responses with respect to the tumor microenvironment and the its spatial organization. I hold a Ph.D. in Life Sciences (Pharmaceutical Sciences) from the University of Geneva, Switzerland, where I conducted research in Prof. Didier Picard's laboratory from 2017 to 2022. Prior to that, I earned a Master’s degree in Pharmacology and Experimental Therapeutics from Alexandria University, Egypt, and a Bachelor’s degree in Pharmacy with honors from Pharos University. My professional experience includes postdoctoral research in molecular pharmacology at UNIGE and a lecturer position in Pharmacotherapeutics and Cancer Biology at Pharos University. I have extensive teaching experience, supervising undergraduate and postgraduate courses, and have successfully guided master's thesis projects. My research has focused on endocrine resistance in breast cancer, utilizing CRISPR/Cas9 screens and exploring drug combinations, resulting in several relevant publications. I have presented my work at numerous conferences and received several awards, including the Ernst et Lucie Schmidheiny Fondation grant and the Ph.D. Booster prize from the faculty of medicine, Geneva, Switzerland. I am an active member of the Life Sciences Switzerland (LS2) and the European Association of Cancer Research (EACR).
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Siyu He
Postdoctoral Scholar, Biomedical Data Sciences
BioI am a postdoctoral fellow in the Department of Biomedical Data Science at Stanford University, where I am advised by Dr. James Zou and Dr. Stephen Quake.
My research interests lie at the intersection of statistical machine learning, computational biology, stem cell engineering, and disease modeling. My mission is to leverage AI methodologies in biomedicine to accelerate our understanding of diseases. I earned my PhD in Biomedical Engineering from Columbia University, where I am co-advised by Dr. Kam Leong and Dr. Elham Azizi. I hold a Bachelor's degree in Physics from Xi'an Jiaotong University. -
Jason Hilton
Senior Research Engineer, Biomedical Data Science
Current Role at StanfordPI & Director, Lattice
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Asef Islam
Masters Student in Biomedical Data Science, admitted Winter 2023
Current Research and Scholarly InterestsAI in medicine and other fields, particularly ML and CV techniques
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Dr Mohit Kaushal MD
Adjunct Professor, Biomedical Data Science
BioDr. Mohit Kaushal is an accomplished entrepreneur, investor, and physician with a distinguished career spanning clinical medicine, academia, public policy and industry. He has served as an investor and board member for numerous public and private transformative companies, including Oak Street Health (NYSE: OSH, acquired by CVS Health, NYSE: CVS), Humedica (acquired by Optum, NYSE: UNH), RxAnte (acquired by Millennium), Change Healthcare (acquired by Emdeon), Universal American (NYSE: UAM, acquired by WellCare, NYSE: WCG), goBalto (acquired by Oracle, NYSE: ORCL), CitiusTech (acquired by Baring), Wellframe (acquired by HealthEdge), and George Clinical (acquired by Hillhouse).
During the Obama administration, Dr. Kaushal served on the White House Health IT Task Force, contributing to the implementation of the Affordable Care Act’s technology initiatives and testifying before Congress on the role of technology and payment reform in Medicare. He also established and led the first dedicated healthcare team at the Federal Communications Commission, where his work included partnering with the FDA to streamline regulation of converged telecommunications, analytics, and medical devices, ultimately resulting in the FDA’s mobile medical applications guidance. His team also restructured the Rural Healthcare Fund into the Healthcare Connect Fund, aligning its resources with broader healthcare technology and payment reforms.
In academia, Dr. Kaushal is an Adjunct Professor in the Department of Biomedical Data Science at Stanford University, which integrates AI, biomedical informatics, biostatistics and computer science to advance precision health. His teaching emphasizes the application of data—ranging from molecular and tissue-level information to imaging, EHR, biosensors, and population health—to improve medical outcomes.
He remains active in public policy as a Scholar in Residence at the Duke-Margolis Center for Health Policy and was previously a Visiting Scholar at the Brookings Institution. His policy work includes previous appointments to the FDASIA Workgroup of the Health IT Policy Committee and the National Committee on Vital and Health Statistics, advising HHS on data access and use.
Dr. Kaushal is an emergency physician by training, holds an MBA from Stanford University, and earned his MD with distinction from Imperial College London. -
Clarissa Klein
Scientific Data Curator 2, Biomedical Data Science
BioScientific curator and coordinator for the Clinical Genome Resource (ClinGen) and the Pharmacogenomics Knowledgebase (PharmGKB) / ClinPGx. Program Manager for Stanford ClinGen.
Coordinator for ClinGen's Data Access, Protection, and Confidentiality (DAPC) Working Group, the Rheumatologic and Autoimmune Diseases Clinical Domain Working Group (RAD-CDWG), Multigenic Taskforce, HLA Working Group, and the Pharmacogenomics Working Group (PGxWG). Coordinator for PharmGKB's submission for FDA recognition of their clinical annotation database, and curator with a focus on PharmGKB Pediatric. -
Philip W. Lavori
Professor of Biomedical Data Science, Emeritus
Current Research and Scholarly InterestsBiostatistics, clinical trials, longitudinal studies, casual inference from observational studies, genetic tissue banking, informed consent. Trial designs for dynamic (adaptive) treatment regimes, psychiatric research, cancer.
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Sheng Liu
Postdoctoral Scholar, Biomedical Data Sciences
BioSheng Liu is a postdoctoral fellow at Stanford University. In May 2023, He received a Ph.D. degree from New York University, majoring in Data Science and Machine Learning. His background is in the area of robust and trustworthy machine learning, machine learning for healthcare.
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Pan Lu
Postdoctoral Scholar, Biomedical Data Sciences
Current Research and Scholarly InterestsMy research goal is to build machines that can reason and collaborate with humans for the common good. My primary research focuses on machine learning and NLP, particularly machine reasoning, mathematical reasoning, and scientific discovery:
1. Mathematical reasoning in multimodal and knowledge-intensive contexts
2. Tool-augmented large language models for planning, reasoning, and generation
3. Parameter-efficient fine-tuning for fondation models
4. AI for scientific reasoning and discovery -
Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Matthew Lungren is a physician-scientist and AI leader whose work has helped shape modern multimodal healthcare AI from early research through large-scale deployment. He joined Stanford University in 2014 as clinical research faculty, where he led a fully dedicated pediatric interventional radiology clinical service and established an NIH- and industry-supported clinical AI research program that helped catalyze what became the Stanford Center for AI in Medicine & Imaging. He remains an Adjunct Professor of Biomedical Data Science at Stanford and also holds a part-time clinical appointment at UCSF.
Dr. Lungren has authored more than 200 peer-reviewed publications with more than 35,000 citations, and he has taught more than 100,000 learners through AI-in-healthcare courses across platforms including Coursera and LinkedIn Learning. His broader contributions include advancing multimodal imaging-plus-EHR approaches, open-sourcing AI-ready medical imaging datasets and models, and serving in national leadership roles across the radiology AI community. After a sabbatical in 2021, he transitioned from academia to industry and joined Microsoft, where he served in senior leadership roles including Chief Scientific Officer for Microsoft Health & Life Sciences. At Microsoft, he founded and led cross-company teams that shipped multimodal healthcare foundation models and agentic, auditable generative AI workflows into production, including healthcare agent orchestration capabilities and major EHR partnerships, and led the health and life sciences partnerships with OpenAI.
Dr. Lungren is also a top rated instructor leading AI in Healthcare courses designed especially for learners with non-technical backgrounds:
Stanford/Coursera: https://www.coursera.org/learn/fundamental-machine-learning-healthcare
LinkedIn Learning: https://www.linkedin.com/learning/an-introduction-to-how-generative-ai-will-transform-healthcare -
Daniel Mas Montserrat
Affiliate, Biomedical Data Science
BioDaniel Mas Montserrat holds a PhD in Electrical and Computer Engineering from Purdue University. Previously he graduated summa cum laude from the Polytechnic University of Catalonia in Audiovisual Systems in Telecommunications Engineering. Currently, he is a research fellow at the Stanford School of Medicine (Department of Biomedical Data Science). His research focuses on applying computational methods to problems in population genetics and biomedicine.
