Bio

Bio


I am a physicist by training and a biotechnologist by profession. I believe that with the explosion of data in healthcare and with new methods to analyze such large amounts of data, we will see massive changes in how human diseases are addressed via novel drugs, large scale genomics, wearable sensors, and software to tie it all together. I want to drive part of this revolution.

Prior to joining Stanford in 2012, I spent a dozen years at various biotechs in the Bay Area.This includes experiences as technology lead at Life Technologies (now Thermo Fisher) and founding team member of Verseon, a drug discovery company. Along the way, I have had fantastic opportunities to work alongside some of the smartest people in the field, learn from some of the most brilliant minds of our times, solve some fundamental technological problems, and delivered business impact.

Current Role at Stanford


I joined Stanford in Oct 2012 as the Director of Bioinformatics at Stanford Center for Genomics and Personalized Medicine (SCGPM). My role at the Center is to develop and lead the bioinformatics team and establish a world class omics and biomedical data analysis facility.

Our bioinformatics team is comprised of a dozen scientists and software engineers. Together the team has a wide range of skill sets including various omics, computational biology, machine learning, software engineering, data management, Databases, Visualization, High Performance Computing, IT, and Cloud DevOps. The team is currently supporting several large scale research and clinical programs at Stanford including prestigious consortium efforts and inter-disciplinary collaborations.

Among our various efforts is Genetics Bioinformatics Service Center, a Big Data Biomedical and Bioinformatics Core Facility, created in 2013 to streamline the availability of infrastructure to the wider biomedical community at Stanford and our research affiliates. The Core facility provides best-in-class high performance computational systems, scalable Cloud computing and cutting edge bioinformatics services for the Stanford community. The core operates under Department of Genetics and is overseen by Office of Dean. The services are NIH dbGaP and SoM security requirements compliant. We also provide a comprehensive analytical stack with over 400 applications installed centrally on the cluster for ease of use by researchers. Currently the Core supports over 75 labs and 750 researchers.

Education & Certifications


  • PhD, Boston University, MA, USA, Statistical and Computational Physics (2000)
  • MSc, Indian Institute of Technology, Madras (aka Chennai), India, Physics (1994)

Publications

All Publications


  • Digital Health: Tracking Physiomes and Activity Using Wearable Biosensors Reveals Useful Health-Related Information. PLoS biology Li, X., Dunn, J., Salins, D., Zhou, G., Zhou, W., Schüssler-Fiorenza Rose, S. M., Perelman, D., Colbert, E., Runge, R., Rego, S., Sonecha, R., Datta, S., McLaughlin, T., Snyder, M. i. 2017; 15 (1): e2001402

    Abstract

    A new wave of portable biosensors allows frequent measurement of health-related physiology. We investigated the use of these devices to monitor human physiological changes during various activities and their role in managing health and diagnosing and analyzing disease. By recording over 250,000 daily measurements for up to 43 individuals, we found personalized circadian differences in physiological parameters, replicating previous physiological findings. Interestingly, we found striking changes in particular environments, such as airline flights (decreased peripheral capillary oxygen saturation [SpO2] and increased radiation exposure). These events are associated with physiological macro-phenotypes such as fatigue, providing a strong association between reduced pressure/oxygen and fatigue on high-altitude flights. Importantly, we combined biosensor information with frequent medical measurements and made two important observations: First, wearable devices were useful in identification of early signs of Lyme disease and inflammatory responses; we used this information to develop a personalized, activity-based normalization framework to identify abnormal physiological signals from longitudinal data for facile disease detection. Second, wearables distinguish physiological differences between insulin-sensitive and -resistant individuals. Overall, these results indicate that portable biosensors provide useful information for monitoring personal activities and physiology and are likely to play an important role in managing health and enabling affordable health care access to groups traditionally limited by socioeconomic class or remote geography.

    View details for DOI 10.1371/journal.pbio.2001402

    View details for PubMedID 28081144

  • Secure cloud computing for genomic data Nature Biotechnology Somalee, D., Keith, B., Michael, S. 2016; 34 (6): 588-91

    View details for DOI 10.1038/nbt.3496

  • Sequence to Medical Phenotypes: A Framework for Interpretation of Human Whole Genome DNA Sequence Data PLOS GENETICS Dewey, F. E., Grove, M. E., Priest, J. R., Waggott, D., Batra, P., Miller, C. L., Wheeler, M., Zia, A., Pan, C., Karzcewski, K. J., Miyake, C., Whirl-Carrillo, M., Klein, T. E., Datta, S., Altman, R. B., Snyder, M., Quertermous, T., Ashley, E. A. 2015; 11 (10)