Faculty Directory
Madhav Mani

Assistant Professor of Engineering Sciences and Applied Mathematics


2145 Sheridan Road
Evanston, IL 60208-3109

Email Madhav Mani


Madhav Mani Research


Engineering Sciences and Applied Mathematics


PhD Program in Interdisciplinary Biological Sciences

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Ph.D Applied Mathematics, Harvard University, Cambridge, MA

S.M. Engineering Sciences, Harvard University, Cambridge, MA

Masters of Advanced Studies in Mathematics and Theoretical Physics, Cambridge University, England

M.A. in Mathematics and Theoretical Physics, Trinity Hall, Cambridge University, England

B.A. in Mathematics and Theoretical Physics, Trinity Hall, Cambridge University, England

Research Interests

The group is comprised of problem-solvers that come from diverse backgrounds, including mathematicians, physicists, engineers, and biologists. The problems we address are those revealed by cutting-edge imaging and sequencing data in biology, and a recognition of inappropriate descriptions/parametrizations/representations of phenomena in living systems. While traditional modeling approaches are still pursued when necessary, we often take a statistical approach to modeling. Modeling is always done in close collaboration with rigorous statistical analysis of experiments done performed in collaborating biology labs, often by ourselves. 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. The over-arching theme in the group is to take a statistical (physics) approach to open questions in biology in close collaboration with experimental labs.

Projects include:

- Pattern formation in embryos

- 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 physicist, and I have been interested in biology for a long time. While continuing to develop my strengths in computation and theory, I enjoy working very closely with experimentalists and the data they generate in the lab. Having a broad background in the physical and mathematical sciences I let the nature of the data and collaboration 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. 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, 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. 

Significant Recognition

  • Simons Postdoctoral Fellowship, 2010 - 2013
  • Derek Bok Undergraduate Teaching Award, Harvard University, 2009
  • Robert L. Wallace Prize Fellowship, Harvard University, 2007 - 2008

Selected Publications