School of Medicine

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  • Francisco De La Vega

    Francisco De La Vega

    Adjunct Professor, Biomedical Data Science

    Bio Prof. Francisco M. De La Vega is a geneticist and computational biologist with interests in cancer, population, and clinical genomics, and with extensive experience in the life sciences industry. He is a Distinguished Scientific Fellow and Vice President of Bioinformatics and at TOMA Biosciences, a privately held start-up company commercializing a technology for precision oncology derived from inventions at Stanford. Francisco is also Adjunct Professor in the Department of Biomedical Data science of the Stanford School of Medicine, a Director of the International Society of Computational Biology, and is or has been a member of the Steering Committee of the NIST-led Genome-in-a-Bottle consortium, the PanCancer Analysis of Whole Genomes project of the ICGC, and the Steering Committee of the 1000 Genomes Project. He has more recently contributed to start-up companies in the life sciences area in positions such as CSO (Annai Systems) and VP of Genomics (Real Time Genetics, Omicia). Previously, he spent over 13 yeas at Applied Biosystems (later Life Technologies and currently Thermo-Fisher), where he played a pivotal role in the development of several successful genetic analysis technologies. For this, he was inducted in 2009 to the Innovation & Invention Society of Life Technologies, a program that recognized the company’s most elite inventors, and in 2008 was a co-recipient of the Bio-IT World Best Practices Award in Basic Research.

  • Manisha Desai

    Manisha Desai

    Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and, by courtesy, of Health Research and Policy

    Current Research and Scholarly Interests Dr. Desai is the Director of the Quantitative Sciences Unit. She is interested in the application of biostatistical methods to all areas of medicine including oncology, nephrology, and endocrinology. She works on methods for the analysis of epidemiologic studies, clinical trials, and studies with missing observations.

  • Bradley Efron

    Bradley Efron

    Max H. Stein Professor and Professor of Statistics and of Biomedical Data Science

    Current Research and Scholarly Interests Research Interests:

  • Andrew Gentles

    Andrew Gentles

    Assistant Professor (Research) of Medicine (Biomedical Informatics) and, by courtesy, of Biomedical Data Science

    Current Research and Scholarly Interests Computational systems biology of human disease. Particular focus on integration of high-throughput datasets with each other, and with phenotypic information and clinical outcomes.

  • Olivier Gevaert

    Olivier Gevaert

    Assistant Professor of Medicine (Biomedical Informatics) and of Biomedical Data Science

    Current Research and Scholarly Interests My 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.

  • Trevor Hastie

    Trevor Hastie

    John A. Overdeck Professor, Professor of Statistics and of Biomedical Data Sciences

    Current Research and Scholarly Interests Flexible statistical modeling for prediction and representation of data arising in biology, medicine, science or industry. Statistical and machine learning tools have gained importance over the years. Part of Hastie's work has been to bridge the gap between traditional statistical methodology and the achievements made in machine learning.

  • Tina Hernandez-Boussard

    Tina Hernandez-Boussard

    Associate Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and of Surgery

    Current Research and Scholarly Interests My 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.

  • Alexander Ioannidis

    Alexander Ioannidis

    Postdoctoral Research Fellow, Biomedical Data Sciences

    Bio Alexander graduated from Harvard in Chemistry and Physics and earned an M.Phil in Computational Biology and Diploma in Greek from the University of Cambridge. He has a Ph.D. in Computational and Mathematical Engineering from Stanford, where he teaches machine learning and data science. Prior to Stanford, he worked in superconducting computing research at Northrop Grumman. As a current research fellow in the Stanford School of Medicine (Department of Biomedical Data Science), his work focuses on applying computational methods to problems in human genetics and population history.


    I work on methods for creating synthetic genomic data for DNA privacy, as well as on novel algorithm design (particularly ancestry related) for several large-scale genomic studies that aim at understanding genetic causes of disease.

    I also focus on projects at the intersection of computational history and population genetics, including work with native communities. As the grandson of Cappadocians expelled from their homeland, I try to engage with the complex sentiments of displaced native peoples in these projects. Pain over the disruption of community heritage and over dispossession from traditional sites often remains raw. If engagement with descendant communities is lacking, research into our past can often feel like a continuation, even a legitimation, of our dispossession. Combined alongside a dialogue with indigenous peoples, however, genetics can play a small role in helping us to reclaim ancestral stories and dispersed community connections. I hope my work in this area plays a constructive role in that process.

    As written by the poet Rumi in the language of the Cappadocians (Rum),
    پیمی تیِ پَاثیِسْ پیمی تی خاسِس
    “Tell me what happened to you, tell me what you have lost.”
    [Rumi; Konya ms 67; translit. πε με τι έπαθες, πε με τι έχασες]

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