Research

From perturbation
to mechanism.

My research connects technology development, cardiovascular biology, and predictive computation.

Research logic

Build causal measurements. Discover regulatory mechanisms. Predict the next intervention.

Research pipeline from biological question to perturbation, multi-omic measurement, modeling, and validation
01

Perturbation Omics Technologies

Make cellular causality measurable.

Experimental platforms that pair programmed perturbations with transcriptomic, epigenomic, and spatial phenotypes.

  • Perturb-seq
  • Perturb-multiome
  • Perturb-spatial
Research rationale

Single-cell atlases reveal biological heterogeneity but are primarily observational. Perturbation omics moves from association to mechanism by measuring controlled interventions across thousands of individual cells.

The long-term goal is a unified platform that progresses from pooled discovery to multimodal mechanism and spatial validation.

IMPACT-seq experimental workflow
IMPACT-seq · In vivo perturbation with transcriptomic and chromatin-accessibility readouts
02

Cardiovascular Functional Genomics

Resolve the regulatory logic of the heart.

Causal maps of the programs that govern cardiac maturation, maladaptive remodeling, and regenerative potential.

  • Cardiac maturation
  • Heart failure
  • Regeneration
Research rationale

Cardiovascular development and disease involve coordinated changes across cardiomyocytes, fibroblasts, vascular cells, immune cells, and other populations.

My future program will connect human genetics, physiological models, multi-omic measurements, and perturbation screens to experimentally testable mechanisms.

Cell segmentation across a cardiac tissue section
Cardiac tissue cell segmentation · Spatial context, not a perturbation result
03

AI-enabled Biology

Let experiments train the model.

Interpretable computational systems that predict cellular responses and guide more informative experiments.

  • Perturbation prediction
  • Computational frameworks
  • AI agents for biology
Research rationale

Perturbation datasets contain defined interventions and therefore provide an unusually strong foundation for biological prediction.

Models will be evaluated by whether they identify mechanisms, generalize across contexts, and prioritize experiments that produce new knowledge.

AI-enabled biology framework
Conceptual framework · Experiment–model feedback

Current projects

Work in progress

Public descriptions remain deliberately concise while studies are ongoing.

Active

Perturb-multiome

CRISPR perturbation × single-cell gene expression and chromatin accessibility.

Active

Visium HD Perturb-spatial

Spatial response fields and local tissue effects of perturbation.

Active

In vivo Perturb-seq

Programmable gene regulation in cardiovascular models.

In preparation

Cardiac maturation snATAC atlas

Chromatin dynamics across postnatal cardiac maturation.