Causal perturbation at scale
Standardized CRISPRi workflows, including an ongoing platform interrogating more than 3,000 proteostasis-network genes across HSV-1, HCMV, and Vaccinia virus.
Senior Scientist · Biohub · San Francisco
Building systems that make complex biology measurable.
I develop experimental and computational platforms for causal perturbation, dynamic single-cell measurement, and quantitative analysis.
My work brings together CRISPR screens, live-cell reporters, quantitative microscopy, and Python-based analysis to uncover causal biology. Virology is the application domain in which I have developed and tested these systems.
What I build
Research approach →I work across the experimental–computational boundary, designing the measurement system as carefully as the biological experiment.
Dynamic single-cell measurement
Live-cell reporters and imaging workflows that convert infection and cell-state transitions into quantitative trajectories.
Quantitative infrastructure
Python workflows for screen exploration, microscopy processing, Cellpose segmentation, Ultrack tracking, visualization, and classification.
Mechanism and translation
Experimental follow-up connecting perturbation-screen hits to molecular mechanism, compound activity, and validation in relevant biological models.
A connected scientific workflow
01PerturbationCRISPRi · comparative screens
→
02MeasurementFACS · reporters · live imaging
→
03AnalysisPython · tracking · classification
→
04Biological insightDependencies · mechanisms · interventions
Selected platforms
View all platforms →These projects follow a recurring pattern: identify a measurement bottleneck, design a system that resolves it, validate it across relevant conditions, and use the resulting data to uncover mechanism.
PreprintPublic code
V-SWITCH
A single-vector OFF-to-ON fluorescent reporter for live RNA-virus infection, paired with a public single-cell imaging and trajectory-analysis pipeline.
View case study →Ongoing workInternal platform
Comparative CRISPRi platform
A standardized perturbation framework for comparing host dependencies across three DNA viruses using quantitative screening and shared analysis.
Read the research →Ongoing work
Multi-agent scientific analysis
A structured hypothesis-generation workflow with specialized agents, literature-grounded evidence review, deterministic guardrails, and quantitative reliability auditing.
View project →What connects the work
I am an experimental scientist who tends to work at the level of the system: the assay, perturbation strategy, measurement framework, and analytical workflow that determine which questions can be answered reliably.
I am especially motivated when progress is limited not by a shortage of hypotheses, but by the absence of a robust way to test them. That pattern has led me from genetic and small-molecule screening to comparative CRISPR platforms, live-cell reporters, high-content microscopy, and single-cell analysis.
Computation became increasingly central because many experimental bottlenecks continue after data acquisition. I build tools that make complex datasets interpretable and auditable, and I am beginning to explore how AI-assisted reasoning can support hypothesis generation, evidence review, and experimental decisions without replacing scientific judgment.
Selected publications
View all publications →Peer-reviewed studies and preprints demonstrating how the platforms are used to investigate virus–host biology, infection dynamics, and therapeutic opportunities.