About
I’m a data science and computational biology executive with 15+ years of experience building and leading cross-functional data science, machine learning, and computational biology teams in biotech and academia. I translate between data science, life sciences, and clinical sciences to maximize the impact of drug discovery and development programs, from early target discovery through Phase 3 clinical trials.
Most recently, as VP, Head of Data Sciences at Neumora Therapeutics, I led a team of data and machine learning scientists across the full R&D pipeline, co-invented multiple clinical ML and therapeutic-use patents, and regularly engaged our executive committee, Board of Directors, and Series A investors. Before that, I spent seven years building a functional genomics research program at the Lieber Institute for Brain Development and Johns Hopkins — securing $20M+ in NIH funding, publishing 145+ research articles, and being named a Highly Cited Researcher (top 1% of research, 2018).
I’m currently exploring new full-time leadership roles in biotech and pharma, and taking on a limited number of fractional and advisory engagements in the meantime — see below.
Highlighted Projects
Causal machine learning to predict drug (or placebo) response
Developed and applied a causal modeling framework for reducing placebo response and increasing trial-level efficacy in neuropsychiatry clinical trials.
Spatial transcriptomics of the human brain
Used spatial transcriptomics (10x Visium) to map layer-specific gene expression across the six layers of the human dorsolateral prefrontal cortex, linked laminar signatures to risk genes, and built an unsupervised clustering framework and public web app (spatialLIBD) for exploring the data.
Modeling maturation and disease biology using iPSC-derived neurons
Developed computational tools to assess the maturity of iPSC-derived neurons using transcriptomics and electrophysiology, and identified deficits in schizophrenia.
Genotype-phenotype mapping in the human brain
Leveraged human postmortem brain tissue and cellular models to identify molecular mechanisms and signatures related to the causes and consequences of severe mental illness.
DNA methylation analyses of heterogeneous tissues
Developed statistical strategies and software for analyzing DNA methylation data from mixtures of cell types.
Single-nucleus RNA sequencing of the human brain
Created a single-nucleus RNA-sequencing resource of 70,615 high-quality nuclei to generate a molecular taxonomy of cell types across five human brain regions that serve as key nodes of the human brain reward circuitry.
Consulting
Fractional / interim leadership
Embedded, part-time Head/VP of Data Science for biotech companies that need senior leadership without a full-time hire.
Hourly advisory
Strategy sessions and technical review on data science, computational biology, and team-building questions.
Project-based engagements
Scoped projects such as a data science org build-out plan, computational pipeline audit, or genomics data strategy.
Expertise
Experience
VP, Head of Data Sciences — Neumora Therapeutics
2020–2026
Led a cross-functional data science, ML, and computational biology team spanning the full drug discovery/development pipeline, from target discovery through Phase 3 neuropsychiatric and neurodegenerative trials. Co-inventor on multiple clinical ML and therapeutic-use patents. Developed fit-for-purpose development strategies to enhance drug response and reduce placebo response using wide array of data modalities.
Group Leader, Data Science & Lead Investigator — Lieber Institute for Brain Development
2013–2020
Built and led a lab of 10+ scientists studying the genomics of brain development and severe mental illness. Principal Investigator on 10 NIH grants totaling $20M+. Published 145+ research articles (H-index 65).
Professor (Adjunct) — Johns Hopkins University
2013–Present
Departments of Psychiatry & Behavioral Sciences and Neuroscience
Publications
145+ peer-reviewed research articles, an H-index of 69, and recognition as a Highly Cited Researcher (top 1% of research, 2018) across computational biology, psychiatric genetics, and statistical methods development. Full list on Google Scholar.