I am interested in algorithms and mathematics for understanding dynamical systems in physics and biology. In particular, I am investigating computational methods for data mining and hypothesizing about the intrinsic physical and chemical relationships that govern many natural systems.
I am part of the Cornell Computational Synthesis Lab (CCSL) at Cornell and work with Hod Lipson. Much of my research includes symbolic regression and related evolutionary algorithms.
I am funded by the National Science Foundation (NSF) Graduate Research Fellowship Program (GRFP), and formerly by the IGERT fellowship program.
I recently had a paper published in Science. Please see the Press Coverage page and the Research page for more information.
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Schmidt M., Lipson H. (2009) "Distilling Free-Form Natural Laws from Experimental Data," Science, Vol. 324, no. 5923, pp. 81 - 85. (see supplemental materials)
Schmidt, M. D., Lipson, H., (2008) "Coevolution of Fitness Predictors," IEEE Transactions on Evolutionary Computation, Vol.12, No.6, pp.736-749.
Schmidt M., Lipson H. (2009), "Symbolic Regression of Implicit Equations," Genetic Programming Theory and Practice, vol. 6, in press.Schmidt M., Lipson H. (2007), "Learning Noise", Genetic and Evolutionary Computation Conference (GECCO'07), pp. 1680-1685.