Systems Biology
Mathematical and computational models for understanding how interactions between biological components give rise to complex cellular dynamics.
Research
Understanding biological systems through models and computation.
My research interests lie at the intersection of systems biology, biophysical modeling, and machine learning. Currently, my work focuses on mechanistic models of cellular signaling, providing a starting point for exploring how biological knowledge and machine learning can complement one another.
More broadly, I am interested in discovering patterns in biological data and connecting different perspectives on complex systems. These interests continue to evolve, with room for new questions and approaches across biology and computation.
Theoretical Biophysics
Mechanistic and quantitative approaches to biological systems, with a particular interest in dynamical models and emergent behavior.
Scientific Machine Learning
Machine learning methods that incorporate physical or biological knowledge rather than treating scientific systems purely as black-box prediction problems.
Computational Modeling
Reproducible computational workflows for simulation, inference and analysis of complex biological models.