Stanford University
Showing 1-50 of 178 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. -
Negin Ashrafi
Research Assistant, Biomedical Data Science
BioI work on AI in healthcare, building on a background in Artificial Intelligence, Machine Learning, and Optimization. I completed my master’s degree in Analytics at the University of Southern California and my bachelor’s degree at Sharif University of Technology.
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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.
