School of Medicine
Showing 1-6 of 6 Results
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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.