I am a postdoctoral researcher at the Center for Complexity and Collective Computation, part of the Wisconsin Institute of Discovery at the University of Wisconsin-Madison.

Here you can find my CV.

My research focuses on predictive modeling of (and in) complex biological systems using real-world data. I am especially interested in methods for inferring the simplified models that are necessary for making good predictions when one has limited data and limited computational power.

Useful heuristics for such a task exist under the name of "model selection" in the field of statistics, and similar algorithms are likely at the heart of adaptive cognitive systems. Interesting questions include:

What must a monkey attend to in order to efficiently strategize in future conflicts?

What heuristics guide a brain that is learning to categorize high-dimensional input into predictive concepts?

How can a computer best build intuitions about how a gene network is likely to behave under varying conditions?

Specifically, I have worked on conflict in macaques, dynamics and statistics of cellular biochemical networks, and DNA conformational dynamics. I work mainly with computational and statistical methods (often with a physics flavor), which have included sparse coding, statistical model selection, maximum entropy models, continuous time sigmoidal networks, and "sloppy" models.