Research Areas

Disease perturbs our balance—homeostasis. Microscopes can show how molecules interact for hours, as well as how cells are organized into tissues. Yet, it requires image analysis and computer vision to identify the nuances that lead to disease.

I develop new computational methods that transform images into quantifiable data, spanning from dynamic protein spots [Deliz-Aguirre et al., 2021; Srikanth et al., 2024] to multiplexed tissue samples (20+ tissue marker images). My pipelines combine physics and AI (machine and deep learning) to clean up, identify, and measure millions of cells with speed and accuracy. Applied to large sets of patient samples, my new tools have given experimentalists the volume of data needed to make new discoveries.

Life unfolds in space and time. The principles governing biology speak a common language: mathematics. The same equations that describe the dynamics between wolves and sheep populations (predator-prey equations) apply to how immune protein complexes assemble and disassemble [thesis]. But, only through careful data curation do these subtleties become apparent.

I apply systems biology to microscopy and proteomics to predict large-scale outcomes from microscopic local interactions. Leveraging statistics and dynamics analysis [R package], I have been able to uncover processes that lead to inflammation and treatment failure.

Studying patient biopsies and mouse tissues has helped reconstruct cancer development. The field has found that cancer progression is correlated with the cellular landscape that surrounds tumors. However, decades of research have only highlighted the complexity of oncology.

Using computational and experimental systems [Zheng et al., 2015], I study how tumors emerge and respond to treatment. My goal is to develop new diagnostics and therapeutic strategies through integrative data analysis.

Infections disrupt our immune system—from the increased molecule production to the community transmission level. These shifts can be quantified with different tools, and analyzed with the same statistical tools.

Using synthetic biology, I have visualized how cells respond to threats [Deliz-Aguirre et al., 2024] and create synergy [Cao et al., 2023]. I have applied these insights to model epidemic dynamics, and was able to predict how COVID-19 spread in cities [KGNS News Interview, 2020]. I have also collaborated with clinicians to explain infection biology [Castro-Lainez et al., 2019], and contributed to discussions on warfare, AI, and biological weapons at the United Nations [IGF, 2023; IGF, 2024]. I aim to continue discovering how infections trigger immune responses.