I'm an engineer and scientist with expertise in mathematical modeling and simulation.
My passion for interdisciplinary research and innovation has led me to collaborate with engineers, physicists, and biologists on a wide variety of topics in the life sciences, including bat echolocation and insect development. Most recently I worked as a postdoctoral fellow at the European Molecular Biology Laboratory in Heidelberg, Germany, where I used a combination of dynamical systems and stochastic processes to study how quickly genes turn on and off.
I received my Ph.D. in Electrical Engineering at the University of California, Berkeley, advised by Murat Arcak. My dissertation focused on developing and applying control theory and signal processing to analyze pattern formation in biological systems, both real and synthetic. For my graduate research I was awarded the Leon O. Chua Award for outstanding achievement in nonlinear science. Prior to that, I completed my B.S. in Electrical Engineering with a minor in Biology at Stanford University.
When I'm not taking Fourier transforms or wrangling spaghetti code, I enjoy reading, writing, drawing, and spending time outdoors.
Highlights
Theory-driven designs
I've developed mathematical frameworks based on signal processing, control theory, and biophysics to propose new ways in which networked or coupled systems may support multicellular life. For example, networks of interacting cells can behave like image processing filters to generate spatial patterns, and DNA packaging helps cells optimize their performance on different tasks when coupling between actuators and controllers is unavoidable. These theoretical results can inform human-engineered biological systems as well as provide insight into naturally evolved organisms.
M. L. Perkins, J. Crocker, and G. Tkačik (in preparation) "Chromatin enables precise and scalable gene regulation with factors of limited specificity."
Predictive mathematical models
Gene expression is regulated by complex processes whose behavior can be difficult to predict. I've teamed up with experimental biologists to design predictive dynamical systems models that are descriptive and quantitative. This means that all relevant model parameters are physically interpretable and can be accurately quantitated through empirical measurements. The model's predictive power therefore derives from an understanding of how the process works, rather than on phenomenology alone.
My models have guided the design of genetically engineered "communities" of cells, and have also helped us understand how identical cells in a young embryo become different types of cells in the adult organism.
Designed and simulated mathematical models to compare optimal gene expression accuracy in "simple" vs. "complex" organisms (e.g., bacteria vs. animals)
