School of Medicine
Showing 1-8 of 8 Results
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Tao Wang (王韬)
Director of Precision Diabetes Care, Genetics
Current Role at StanfordPrincipal Investigator, AI for Precision Diabetes Management
Project Manager & Scientific Co-lead, PsychENCODE Project
Project Initiator & Clinical Co-lead, Long COVID Clinical RCT with TCM
Project Initiator & Manager, AI & Wearables Toolkit for Biomedical Sciences
ENCODE and PsychENCODE Project Data Manager
Research Scientist, US Veteran Affairs Hospital
SCGPM HPC System Administrator -
Tauska Lan
Affiliate, Genetics - BASE
BioI'm an ML engineer specializing in LLM post-training and agentic systems—with a particular focus on domains where rigor matters: health, biology, and scientific discovery.
Long-horizon agents — Designed and shipped multi-step orchestration systems (Pantheon-CLI, OmicVerse Agent) that outperform general SWE-agent baselines on biomedical tasks. Built cross-provider query routing and sandboxed execution to keep complex workflows robust over extended interactions. My agents don't just respond—they plan, recover from failure, and complete real research pipelines end-to-end.
Agentic science — Created infrastructure where AI doesn't assist research—it conducts it. Vectorized 30 years of NHANES data; parallelized Bayesian kernel machine regression on Kubernetes; built TCGA/GEO pipelines that bridge wet-lab and dry-lab workflows. Co-developed OmicVerse, an open-source platform powering reproducible multi-omics and single-cell analyses across hundreds of studies.
Experience engineering — Scaled rubric-based reward datasets to 1M+ pairs; trained summary and chain-of-thought reward models via RLAIF/RLHF; delivered measurable benchmark lifts in health AI. I care about the full loop: data curation → reward shaping → careful ablation → verifiable outcome—no cherry-picked demos—just metrics that survive scrutiny.
Currently pursuing advanced agentic studies at Karolinska Institutet and Stanford!
Open-source: OmicVerse · Pantheon-CLI · RAG Web UI · AstrBot
If you're working on post-training at scale, scientific agents, or high-integrity data pipelines—I'm always interested in systems that move from promising results to verifiable outcomes. Let's talk. -
Shannon White
Postdoctoral Scholar, Genetics
BioHi, I'm Shannon White. I began my postdoctoral fellowship in Michael Snyder's lab in the fall of 2020. I received my PhD from Georgetown University in Tumor Biology in Chunling Yi's lab. My graduate worked explore the signaling and metabolic vulnerabilities of NF2-mutant tumors following YAP/TAZ depletion. My postdoctoral work is exploring the epigenetic hallmarks that contribute to colon cancer progression and drug resistance. I am developing colon organoids derived from pre-cancerous polyp tissue collected from Familial Adenomatous Polyposis patients as a model system to investigate epigenetic and signaling responses to chemoprevention treatments.
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Monte Winslow
Associate Professor of Genetics and of Pathology
Current Research and Scholarly InterestsOur laboratory uses genome-wide methods to uncover alterations that drive cancer progression and metastasis in genetically-engineered mouse models of human cancers. We combine cell-culture based mechanistic studies with our ability to alter pathways of interest during tumor progression in vivo to better understand each step of metastatic spread and to uncover the therapeutic vulnerabilities of advanced cancer cells.
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John Witte
Professor of Epidemiology and Population Health, of Biomedical Data Science and of Genetics
Current Research and Scholarly InterestsThe Witte Lab is a computational and statistical genetics group focused on deciphering the genetic and molecular mechanisms underlying cancer and other complex traits. We undertake integrative analyses across large multi-ancestry cohorts and biobanks, developing and applying methods at the interface of epidemiology, statistical genetics, and machine learning.
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Yue Wu
Postdoctoral Scholar, Genetics
Current Research and Scholarly InterestsI built computational methods to integrate and model biological time series, including metabolic dynamics, longitudinal multi-omics data, and micro-sampling. I reduce dimensions, built clusters, and search for causal links.
