School of Medicine
Showing 1-50 of 78 Results
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Russ B. Altman
Kenneth Fong Professor and Professor of Bioengineering, of Genetics, of Medicine, of Biomedical Data Science, Senior Fellow at the Stanford Institute for HAI and Professor, by courtesy, of Computer Science
Current Research and Scholarly InterestsI refer you to my web page for detailed list of interests, projects and publications. In addition to pressing the link here, you can search "Russ Altman" on http://www.google.com/
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Vasiliki (Vicky) Bikia
Postdoctoral Scholar, Biomedical Informatics
BioDr. Vasiliki Bikia is a Fellow at the Institute for Human-Centered Artificial Intelligence and Postdoctoral Scholar at Stanford University, working with Prof. Roxana Daneshjou. She received her Advanced Diploma degree in Electrical and Computer Engineering from the Aristotle University of Thessaloniki (AUTH), Greece, in 2017, and her Ph.D. degree in Biomedical Engineering from the Swiss Federal Institute of Technology of Lausanne (EPFL), Switzerland, in 2021. Her Ph.D. research addressed the clinical need for providing non-invasive tools for cardiovascular monitoring leveraging machine learning and physics-based numerical modeling.
Her current work focuses on developing large multimodal models to enhance biomarker identification and patient outcome prediction. At Stanford, she has also contributed to the Stanford Spezi framework, designing and prototyping the Spezi Data Pipeline tool for enhanced digital health data accessibility and analysis workflows. Her research interests include health algorithms, clinical and digital biomarkers, machine learning, non-invasive monitoring, and the application of large language models for personalized healthcare, predictive analytics, and enhancing patient-clinician interactions. -
Alison Callahan
Research Engineer, Med/BMIR
BioAlison Callahan is an Instructor in the Center for Biomedical Informatics and Clinical Data Scientist in the Stanford Health Care Data Science team led by Nigam Shah. Her current research uses informatics to expand and improve the data available about pregnancy and birth, and to develop and maintain and EHR-derived obstetric database. She is also the co-leader of the OHDSI Perinatal & Reproductive Health (PRHeG) working group. Her work in the SHC Data Science team focuses on developing and implementing methods to assess and identify high value applications of machine learning in healthcare settings.
Alison completed her PhD in the Department of Biology at Carleton University in Ottawa, Canada. Her doctoral research focused on developing HyQue, a framework for representing and evaluating scientific hypotheses, and applying this framework to discover genes related to aging. She was also a developer for Bio2RDF, an open-source project to build and provide the largest network of Linked Data for the life sciences. Her postdoctoral work at Stanford applied methodologies developed during her PhD to study spinal cord injury in model organisms and humans in a collaboration with scientists at the University of Miami. -
Jonathan H. Chen, MD, PhD
Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsInformatics solutions ares the only credible approach to systematically address challenges of escalating complexity in healthcare. Tapping into real-world clinical data streams like electronic medical records will reveal the community's latent knowledge in a reproducible form. Delivering this back as clinical decision support will uniquely close the loop on a continuously learning health system.
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Henry C. Cousins
MD Student, expected graduation Spring 2025
Ph.D. Student in Biomedical Informatics, admitted Autumn 2021
MSTP StudentBioHenry is an MD-PhD candidate and Knight-Hennessy Scholar in the Medical Scientist Training Program and the Biomedical Informatics Program, where he is advised by Professor Russ Altman. He develops machine-learning methods to study the effects of complex genetic variation on human disease mechanisms, with focus on neurological and ophthalmic disorders. His goal is to translate genomic discoveries into disease-modifying therapies.
He received an AB summa cum laude from Harvard University in 2017, where he studied genetic mechanisms of retinal development with Professor Joshua Sanes. He then graduated with an MPhil with distinction from the University of Cambridge as a Gates Cambridge Scholar. He previously worked at Leaps by Bayer and the Massachusetts Eye and Ear Infirmary and has received a number of awards related to research and teaching. -
Hejie Cui
Postdoctoral Scholar, Biomedical Informatics
BioDr. Hejie Cui is a postdoctoral researcher at the Stanford Center for Biomedical Informatics Research at Stanford University. Her research focuses on the intersection of machine learning, data mining, and biomedical informatics. At Stanford, Dr. Cui works on large language model (LLM) evaluation and post-training for healthcare. Dr. Cui has authored and co-authored several publications in top computer science and interdisciplinary venues, including NeurIPS, KDD, AAAI, CIKM, TMI, and MICCAI. Her work contributes to advancing the application of artificial intelligence in healthcare and improving the understanding of complex biomedical data. Dr. Cui was selected as a Rising Star in EECS in 2023. She has also received numerous awards, including the Fellowship of 2021 CRA-WP Grad Cohort for Women, Student Travel Grant Award for MICCAI'22, NSF Travel Grant for CIKM'22, and NeurIPS AI4Science Travel Award for NeurIPS'22. Dr. Cui holds a Ph.D. in Computer Science from Emory University (2024) and a B.Eng. in Computer Science and Engineering from Tongji University (2019). During her graduate studies, she gained industry experience through internships at Microsoft Research and Amazon Science.
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N. Lance Downing
Clinical Assistant Professor, Medicine - Biomedical Informatics Research
BioI am board-certified internal medicine and clinical informatics. I am a primary care physician and teaching hospitalist. I have published work in the New England Journal of Medicine, Health Affairs, Annals of Internal Medicine, and the Journal of the American Medical Informatics Association. My primary focus throughout my career has been to deliver personalized and compassionate care that incorporates the latest advancements in medical science. I aim to help all of my patients maximize their healthspan and age with the best quality of life possible.
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Matthew A. Eisenberg
Clinical Assistant Professor (Affiliated), Med/BMIR
BioDr. Matthew A. Eisenberg joined Stanford Health Care in early 2013 and is the Medical Informatics Director for Analytics & Innovation with a focus on interoperability and health information exchange, regulatory reporting, health care analytics, patient reported outcomes and other uses of technology to meet our strategic initiatives.
Dr. Eisenberg is board certified in Pediatrics and Clinical Informatics. He is a Clinical Assistant Professor (Affiliated) in the Stanford Center for Biomedical Informatics Research at the Stanford University School of Medicine and he serves as the Stanford Health Care site director for the Stanford Clinical Informatics Fellowship Program. He previously held the position of Clinical Assistant Professor in Pediatrics at the University of Washington School of Medicine. He is a current member of the eHealth Exchange Coordinating Committee, a Sequoia Project Board member and serves as the current chair of the Epic Care Everywhere Network Governing Council. He is a member of the Carequality Advisory Council (past co-chair) and a member of IHE USA Implementation Committee. He is a Fellow of the American Academy of Pediatrics and a member of the American Medical Informatics Association and their Clinical Informatics Community. -
Jason Fries
Research Engineer, Med/BMIR
Current Role at StanfordI'm currently working as a staff research scientist in the Shah Lab and research scientist at Snorkel AI. My interests fall in the intersection of computer science and medical informatics. My research interests include:
• Machine learning with limited labeled data, e.g., weak supervision, self-supervision, and few-shot learning.
• Multimodal learning, e.g., combining text, imaging, video and electronic health record data for improving clinical outcome prediction
• Human-in-the-loop machine learning systems.
• Knowledge graphs and their use in improving representation learning -
Andrew Gentles
Associate Professor (Research) of Pathology, of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsComputational systems biology
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Olivier Gevaert
Associate Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsMy lab focuses on biomedical data fusion: the development of machine learning methods for biomedical decision support using multi-scale biomedical data. We primarily use methods based on regularized linear regression to accomplish this. We primarily focus on applications in oncology and neuroscience.
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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. -
Summer Han
Associate Professor (Research) of Neurosurgery, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy current research focuses on understanding the genetic and environmental etiology of complex disease and developing and evaluating efficient screening strategies based on etiological understanding. The areas of my research interests include statistical genetics, molecular epidemiology, cancer screening, health policy modeling, and risk prediction modeling. I have developed various statistical methods to analyze high-dimensional data to identify genetic and environmental risk factors and their interactions for complex disease.
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Josef Hardi
Software Dvlpr 3, Med/BMIR
BioI'm a software engineer with a keen interest in data science. I have over 10 years’ experience in software development and 5 years in the data processing. Currently, I work as a backend developer for the Stanford Center of Biomedical Informatics Research; tackling issues in data and metadata management and interoperability. I also actively engage in the work of converting health and claim records to the OMOP common data model as part of my collaboration with the Stanford Population Health Sciences. I have experience with Java, Python, R, RDF, OWL, OBDA, Schema.org and Elasticsearch.
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Zihuai He
Assistant Professor (Research) of Neurology and Neurological Sciences (Neurology Research), of Medicine (BMIR) and, by courtesy, of Biomedical Data Science
Current Research and Scholarly InterestsStatistical genetics and other omics to study Alzheimer's disease.
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Tina Hernandez-Boussard
Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsMy background and expertise is in the field of computational biology, with concentration in health services research. A key focus of my research is to apply novel methods and tools to large clinical datasets for hypothesis generation, comparative effectiveness research, and the evaluation of quality healthcare delivery. My research involves managing and manipulating big data, which range from administrative claims data to electronic health records, and applying novel biostatistical techniques to innovatively assess clinical and policy related research questions at the population level. This research enables us to create formal, statistically rigid, evaluations of healthcare data using unique combinations of large datasets.
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Jessica Herrmann
MD Student with Scholarly Concentration in Biomedical Ethics & Medical Humanities, expected graduation Spring 2025
Masters Student in Medicine, admitted Spring 2022BioJessica is a Berg Scholar pursuing both an M.D. and an M.S. in Medicine in Biomedical Investigation at the Stanford University School of Medicine. She holds a bachelor’s degree in biomedical engineering from Harvard College, and a master’s degree in bioengineering from Stanford University. Jessica aspires to translate computational and device-based interventions into the clinic as a physician-scientist. Working in the laboratory of Dr. Mark Skylar-Scott, she researches 3D bioprinting approaches to pediatric single ventricle heart defects. She has received the Alpha Omega Alpha Carolyn L. Kuckein Student Research Fellowship, the Dorothy Dee and Marjorie Helene Boring Trust Research Award, and the Irvin David Yalom, M.D. Literary Award. Outside of research and clinical interests, she enjoys creative writing and playing the flute.
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Zepeng Huo
Postdoctoral Scholar, Biomedical Informatics
BioConducting research on Foundation Models for medicine
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Daniel Katz
Assistant Professor of Medicine (BMIR)
BioDaniel Katz is an Assistant Professor of Medicine in the Stanford Center for Biomedical Informatics Research (BMIR) and the Cardiovascular Medicine Divisions. He practices as an Advanced Heart Failure and Transplant Cardiologist. He completed internal medicine residency at Massachusetts General Hospital, general cardiology training at Beth Israel Deaconess Medical Center, and then joined Stanford in 2021 for his advanced heart failure training. Since medical school, his research has focused on identifying the various pathophysiologic patterns and mechanisms that lead to the heterogeneous syndrome of heart failure. His efforts leverage high dimensional data in many forms including clinical phenotypes, plasma proteomics, metabolomics, and genetics. He is presently engaged in analysis of multi-omic data from the Molecular Transducers of Physical Activity Consortium (MoTrPAC) and the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. His clinical interests include advanced heart failure, transplant cardiology, and mechanical circulatory support.
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Teri Klein
Professor (Research) of Biomedical Data Science, of Medicine (BMIR) and, by courtesy, of Genetics
Current Research and Scholarly InterestsCo-founder, Pacific Symposium on Biocomputing
NIEHS, Site Visit Reviewer
NIH, Study Section Reviewer -
Curtis Langlotz
Senior Associate Vice Provost for Research, Professor of Radiology (Integrative Biomedical Imaging Informatics), of Medicine (Biomedical Informatics Research), of Biomedical Data Science and Senior Fellow at the Stanford Institute for HAI
Current Research and Scholarly InterestsMy laboratory develops machine learning methods to help physicians detect disease and eliminate diagnostic errors. My laboratory is developing neural network systems that detect and classify disease on medical images. We also develop natural language processing methods that use the narrative radiology report for contrastive learning and other multi-modal methods that improve the accuracy and capability of machine learning systems.
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Maya Mathur
Associate Professor (Research) of Pediatrics, of Medicine (Biomedical Informatics) and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsSynthesizing evidence across studies while accounting for biases
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Tushar Mungle
Postdoctoral Scholar, Biomedical Informatics
Current Research and Scholarly InterestsUse electronic health records (EHRs) to identify and classify common ocular diseases such as glaucoma, diabetic retinopathy, and macular degeneration. We aim to develop an approach to accurately identify these conditions using EHRs. This will be followed by cluster analysis to identify novel subtypes of these conditions that have not been recognized before. Finally, we will develop an approach to extract outcome data from EHRs for patients with these conditions in the primary care setting.
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Mark Musen
Stanford Medicine Professor of Biomedical Informatics Research, Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science
Current Research and Scholarly InterestsModern science requires that experimental data—and descriptions of the methods used to generate and analyze the data—are available online. Our laboratory studies methods for creating comprehensive, machine-actionable descriptions both of data and of experiments that can be processed by other scientists and by computers. We are also working to "clean up" legacy data and metadata to improve adherence to standards and to facilitate open science broadly.
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Fateme Nateghi Haredasht
Postdoctoral Scholar, Biomedical Informatics
BioAs a postdoctoral scholar at the Stanford Center for Biomedical Informatics Research, 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 delved into the complexities of machine learning algorithms and their transformative potential in healthcare settings. My research, particularly focused on adapting these algorithms for time-to-event data (a method used for predicting specific events in a patient’s future), has not only been a challenging endeavor but also a deeply fulfilling one.
Now at Stanford, my role involves not just advancing machine learning integration in healthcare, but also collaborating with a diverse team of experts. Together, we're striving to unravel complex healthcare challenges and improve patient outcomes. -
Madelena Ng
Postdoctoral Scholar, Biomedical Informatics
BioDr. Ng is a postdoctoral fellow at the Stanford Center for Biomedical Informatics Research, mentored by Dr. Tina Hernandez Boussard. Her research aims to illuminate the evolving ethical and practical challenges with emerging technologies used for health purposes. Prior to joining Stanford, Dr. Ng facilitated mobile- and internet-based health research initiatives with the Health eHeart Study and the Eureka Digital Research Platform and developed research study prototypes that used blockchain technology for health data exchange. Her current work focuses on discerning key challenges that exist at each stage of the AI life cycle and generating informed guidance to drive the responsible and equitable use of AI for patient care.
