Faculty

The faculty members participating in the Computational Sciences (CSci) graduate program represent various departments in the College of Science and Mathematics. Students in the CSci program will have a primary advisor and a secondary advisor, typically from a different department. Prospective students are encouraged to contact faculty to inquire about the availability of positions in their research groups. Below there is a list of faculty who are currently involved in the CSci program. Students interested in working with faculty members not listed below are encouraged to contact the Program Director for further information.

Biology

Todd Riley – computational & systems biology, cancer genomics, molecular biophysics

Chemistry

Jason Green – non-equilibrium statistical mechanics, nonlinear dynamics, molecular modeling and simulations

Computer Science

Nurit Haspel – computational structural biology and structural bioinformatics
Dan Simovici – information-theoretical and linear methods in data mining; semantic models in databases; algebraic aspects of multiple-valued logic
Wei Ding – data mining, machine learning, artificial intelligence, computational semantics, with applications to natural sciences

Engineering

Ping Chen – bioinformatics, data mining, computational semantics

Physics

Rahul Kulkarni – stochastic modeling of gene expression, non-equilibrium statistical mechanics, bioinformatics
Maxim Olchanyi – quantum non-equilibrium dynamics and integrable systems
Bala Sundaram – quantum and classical chaos, the quantum-to-classical transition and applications of nonlinear dynamics in biology and cognitive science

Mathematics

David Degras – high-dimensional statistics, functional data analysis, statistical computing, machine learning, neuroimaging
Timothy Killingback – Mathematical biology, evolutionary dynamics, dynamical systems, and game theory
Mirjana Vuletic – statistical physics, probability, representation theory, combinatorics
Kourosh Zarringahalam – Mathematical biology, bioinformatics, and machine learning