Stanford University
Showing 51-100 of 166 Results
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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. -
Aimmon Lago
Affiliate, Biomedical Data Science
BioAimmon is a healthcare and technology leader with over 20 years of experience supporting teams at Accenture, Kaiser Permanente, and Stanford Health Care. He currently serves as the Executive IT Director of Revenue Cycle and Population Health Systems at Stanford Health Care where he helps create and sustain financial value, employee engagement, and patient experience improvement.
Aimmon holds a MS in Clinical Informatics Management from Stanford, a MS in Healthcare Administration from California State University East Bay, and a BS in Business Administration from Santa Clara University.
He is excited about the opportunities for cost, quality, and access improvement in healthcare, and seeks to create meaningful and sustained change with the use of technology, organizational management, and financial tools. -
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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Ruotong Liao
Affiliate, Biomedical Data Science
BioVisiting Scholar with Prof. Serena Yeung-Levy at Stanford AI Lab.
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Matthew Lungren
Adjunct Professor, Biomedical Data Science
BioDr. Matthew Lungren is a physician-scientist whose research develops and evaluates machine learning systems that combine medical imaging, electronic health record data, and clinical outcomes. His current work concerns medical foundation models, agentic clinical systems, and the prospective evaluation of AI in real clinical workflows. He joined the Stanford faculty in 2014 in the Department of Radiology, serving as Assistant Professor and then Associate Professor through 2021 while leading a dedicated pediatric interventional radiology service. He co-founded and co-directed the Stanford Center for Artificial Intelligence in Medicine and Imaging. He remains Adjunct Professor of Biomedical Data Science at Stanford, where he co-teaches Generative AI and Medicine, and holds a part-time clinical appointment at UCSF. He serves as an independent board director and scientific advisor to medical technology, health system, and life science organizations. In industry, Dr. Lungren served as Chief Scientific Officer for Health and Life Sciences and as AI Technical Advisor in the Office of the CTO at Microsoft.
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.
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Liam Edward Mulhall
Software Developer, Biomedical Data Science
Current Role at StanfordLiam develops and maintains the HLA Curation Interface, a tool that supports the assessment of HLA alleles and haplotypes for use in precision medicine and research. He also works on internal tools used by the Stanford ClinGen team.
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Fateme (Fatima) Nateghi
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoc researcher at the Division of Computational Medicine, I find myself at the exciting intersection of machine learning and healthcare. My journey began with a PhD in Biomedical Sciences from KU Leuven in Belgium, where I explored the complexities of machine learning algorithms and their transformative potential in clinical settings. My research focused on adapting these algorithms for time-to-event data, a method used to predict when specific events may occur in a patient’s future.
At Stanford, my work centers on building trustworthy AI systems to enhance healthcare delivery. I develop and evaluate machine learning models that integrate structured electronic health records (EHRs) and unstructured clinical notes to support real-world clinical decision-making. My recent projects include predicting treatment retention in opioid use disorder, improving antibiotic stewardship for urinary tract infections, and enabling digital consultations through large language models (LLMs). I'm particularly interested in embedding-based retrieval and retrieval-augmented generation (RAG) methods that help bridge cutting-edge AI research with clinical practice.
My role involves not just advancing the integration of machine learning in healthcare but also collaborating with a diverse team of clinicians, data scientists, and engineers. Together, we're striving to unravel complex healthcare challenges and ultimately improve patient outcomes.
