The group is comprised of problem-solvers that come from diverse backgrounds, including applied mathematicians, physicists, engineers, and biologists. The problems our theory group addresses are those revealed by close collaborations with experimentalists, and cutting-edge imaging and sequencing data. Our primary interest is in embryonic patterning, with a growing interest in metabolism in unicellular and multicellular contexts. Projects involve a back-and-forth where experimental design, statistical analysis of generated data, and simple mathematical modeling inform each other. While traditional modeling approaches are still pursued when necessary, we take a statistical approach to modeling when needed. The goal is to synthesize diverse observations into an explanatory and predictive framework, with the eventual goal to guide and inspire new experiments in the lab and make discoveries.
- Pattern formation in embryos (transcriptional and mechanical patterns)
- Tissue mechanics in embryos
- Imaging-based force-inference techniques for cellular aggregates
- Statistical analyses of single-cell sequencing data
- Statistical assessment of variation in animal forms
- The structure-function mapping in microbial ecologies
- Development of novel dimensionality-reduction techniques
- Deep-learning approaches to image-analysis of biological data
I am trained as an applied mathematician and theoretical physicist, and I am fascinated by living systems. How do embryos build themselves? How does metabolism constrain what cells can do? While continuing to develop my strengths in computation and theory, I enjoy working closely with experiments and experimentalists, letting the nature of the questions be my guide.
Why Biology? The phenomena manifest in living systems are amongst the most fascinating in the universe. Our abilities to image and sequence cells and tissues are giving us an unprecedented view into the complexity of life, in particular into embryonic patterning. This data is quantitative, high-dimensional, and, to a large extent, remains intractable. To leverage these technologies to the maximum will require, I believe, new computation, new mathematics, new models, and perhaps even new physics. For those of us that are well-versed in the mathematical and physical sciences, and like solving hard problems, biology is the field to be a part of in the 21st century.
Engineering Sciences and Applied Mathematics
2145 Sheridan Road
Evanston, IL 60208