Research Interests: Bioinformatics, Precision Health, Precision Medicine, Longitudinal Data, Multi-Omics, Wearables Sensors, Space Biology, Systems Biology.

Bio: Tejaswini’s research interests lie at the intersection of biomedical research,  More precision medicine, and bioinformatics.  She previously led the Snyder lab arm of the NASA Twins Study, and currently leads the iPOP, hPOP and COVID-19 Wearables studies within the lab. Her work revolves around using longitudinal biomedical data (omics and wearables) to understand physiological variation across individuals, to establish personal healthy molecular profiles, to study individual metabolic dysregulation patterns, and to identify changes associated with lifestyle, illness, and space travel (NASA Twins Study), with the ultimate goal of predicting diseases early and using personalized medicine to manage health.

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Research Interests: Interests: computational mass spectrometry, algorithms for huge data sets in computational biology, multiomics in relation to maternal and cardiovascular health, multiomics in relation to drug response. 

Before this Mihir received a PhD in Computer Science at Carnegie Mellon University, a masters of applied math at Stanford, and a bachelors of electrical engineering at Rice University.   More During his PhD he developed the state of the art computational routines for processing mass spectrometry data in the field of metabalomics. In his postdoc he hopes to use his data science skills to extract biologically meaningful insight that could translate to the real world.

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Research interests:focuses on multi-omics and epigenomics of aging and rejuvenation at Sebastiano Lab (Institute for Stem Cell Biology and Regenerative Medicine) and Snyder Lab (Department of Genetics).

Bio: Mahdi Moqri, PhD is an NLM Postdoctoral Fellow in Biomedical Informatics as well as an academic graduate student at Stanford. He served as an Assistant Professor of Information Systems at Iowa State University, developing large-scale machine learning models for predicting patients’ outcomes, before joining Stanford.

Research interest: Histone epigenetics, chromatin biology, integrated multi-omics. Specifically, understanding the regulatory effects of short-chain acyl-lysine histone modifications and their effect on chromatin structure and gene expression.

Research interests: Molecular epidemiology, complex disease, genomics, metabolomics, proteomics, statistics, bioinformatics

• Research interests: 
  - Impact of diet and sleep on onset and progression of type 2 diabetes using wearable technologies
  - Chrono-nutrition profiles of individuals at risk for type 2 diabetes 
  - Identification of data-driven dietary patterns of people and their effects on multi-omics profiles. 
• Heyjun  More believes that lifestyle factors such as diet and sleep are strong predictors of onset and progression of chronic diseases along with genetic predisposition. Therefore, her primary research goals are to discover novel molecular markers of chronic diseases by analyzing diet and sleep patterns of the individuals at risk or with chronic diseases; and to develop personalized recommendations to optimize health.  Heyjun is a biomedical scientist with her PhD in nutritional sciences at Cornell University; she has expertise in nutrition, physiology, molecular biology, genetics, and epidemiology. She is also a registered dietitian with clinical dietetic training at the UCSF Medical Center before joining the Snyder lab. 

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Research interests: Wearable medical systems for non-invasive and pervasive health monitoring in cardiovascular and critical care contexts; Correlation of genomic aspects of disease with phenotypic data from electronic health records; Informatics and machine learning for precise detection and early warning of infectious diseases; Preemptive protocols for managing disease  More severity trajectories; And IoT based Telemedicine. Ekanath's current research involves large scale study of resting heart rate, clinical symptoms and daily activities as they relate to COVID-19, as well as their time series big data analysis for risk stratification.

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Research interests: exercise physiology, molecular transducers of physical activity, epidemiology and population health, precision medicine, nutrition.

Bio: Aubrey’s graduate research involves characterizing the cardiovascular and multi-omic changes that occur with high intensity interval training (HIIT) vs. traditional endurance training in sedentary individuals. She is also interested in the health of female athletes and the interplay between nutrition, energy expenditure, and bone stress injuries.

Research interests: applications of genomics in comparative medicine and animal models (primates).

Research interests: Understanding the biological effects of  the exposome using multiomics profiling.  The areas of the exposome I am most interested in include lifetime stress and adversity and environmental chemical exposures

Research interests: Metabolomics, Multi-omics, Biostatistics, Systems Biology, and Bioinformatics, and their application in healthcare.

Xiaotao's research focuses  on the development of bioinformatic algorithms and tools for large-scale metabolomics as well as its application in discovering new biomarkers related to human diseases. He has developed several algorithms and tools.  The first algorithm was MetNormalizer, which is a machine learning-based algorithm for data normalization and integration of large-scale metabolomics. Then he developed a web-based tool, MetFlow, aiming to provide a comprehensive pipeline for data cleaning and statistical analysis of large-scale metabolomics. The most important algorithm he has developed is MetDNA, which is a novel algorithm for metabolite identification and dysregulated pathway analysis in untargeted metabolomics.

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Research Interests: Female athlete research, exercise physiology, human performance, multi-omics, interventions

Research interests: statistical method development and analysis, machine learning, -omics data integration, system biology.

Research interest:  Metabolomics, time series, dynamics, machine learning. Yue extracts knowledge from biological time series. He connects biological states from omics data and infers functional variations related to biochemistry, disease, and clinical application. 

Research interest: Exposome and how environmental exposures affect your health.

Research interests: transcriptional regulation in health and in disease.

Research interests: employing new methods to better understand the moving interactions between the human immune system and the environmental factors such as the microbiome.