# Research Groups

The department’s research activities encompass a wide range of applied mathematics and statistics. These set the tone for the faculty’s individual and collaborative research, the master’s doctoral dissertations of the graduate students, and a very active undergraduate research program.

Some, but certainly not all, of the faculty’s research interests are listed in the groupings below in no particular order.

### Numerical Analysis and Computational Mathematics

- Andrei Draganescu
- Numerical analysis, PDE-constrained optimization
- Matthias Gobbert
- Numerical analysis, scientific and parallel computing, industrial and computational mathematics
- Florian Potra
- Numerical optimization, simulation of multibody systems, numerical solution of nonlinear differential and integral equations, bioinformatics
- Muruhan Rathinam
- Numerical approximation of stochastic dynamical systems, stochastic dynamics in biochemistry and financial markets, geometric nonlinear control theory
- Rouben Rostamian
- Partial differential equations, mathematical modeling, fluid and solid mechanics, programming of computational algorithms
- Bedřich Sousedík
- Computational mathematics, numerical analysis, scientific computing, uncertainty quantification, stochastic finite elements, domain decomposition, multigrid
- Manil Suri
- Numerical analysis, partial differential equations

### Differential Equations and Dynamical Systems

- Animikh Biswas
- Analysis of PDEs, mathematical fluid dynamics
- Matthias Gobbert
- Numerical analysis, scientific and parallel computing, industrial and computational mathematics
- Kathleen Hoffman
- Calculus of variations, differential equations, mathematical biology, singular perturbation theory
- Jacob Kogan
- Control theory, optimization, data/text mining, machine learning
- James Lo
- Computational intelligence, intelligent systems, neural networks approach to systems control and signal processing, stochastic systems
- Brad Peercy
- Mathematical biology, differential equations, bifurcation theory, perturbation theory
- Florian Potra
- Numerical optimization, simulation of multibody systems, numerical solution of nonlinear differential and integral equations, bioinformatics
- Muruhan Rathinam
- Numerical approximation of stochastic dynamical systems, stochastic dynamics in biochemistry and financial markets, geometric nonlinear control theory
- Rouben Rostamian
- Partial differential equations, mathematical modeling, fluid and solid mechanics, programming of computational algorithms
- Thomas Seidman
- Control theory, non-linear partial differential equations, inverse problems
- Jinglai Shen
- Control theory, nonsmooth switching systems, continuous and dynamic optimization, nonlinear and geometric control theory
- Justin Webster
- Nonlinear evolution partial differential equations, dynamical systems, fluid-structure interactions, aeroelasticity

### Mathematical Modeling and Simulation

- Matthias Gobbert
- Numerical analysis, scientific and parallel computing, industrial and computational mathematics
- Kathleen Hoffman
- Calculus of variations, differential equations, mathematical biology, singular perturbation theory
- Hye-Won Kang
- Stochastic modeling, analysis, and simulation of biological systems
- Thu Nguyen
- Stochastic Approximation, Monte Carlo Methods, Bayesian Estimation/Inference, Machine Learning, Stochastic Systems, Stochastic Processes, Stochastic Simulation, Dynamical Systems
- Brad Peercy
- Mathematical biology, differential equations, bifurcation theory, perturbation theory
- Florian Potra
- Numerical optimization, simulation of multibody systems, numerical solution of nonlinear differential and integral equations, bioinformatics
- Muruhan Rathinam
- Numerical approximation of stochastic dynamical systems, stochastic dynamics in biochemistry and financial markets, geometric nonlinear control theory
- Rouben Rostamian
- Partial differential equations, mathematical modeling, fluid and solid mechanics, programming of computational algorithms
- Thomas Seidman
- Control theory, non-linear partial differential equations, inverse problems

### Mathematical Biology

- Matthias Gobbert
- Numerical analysis, scientific and parallel computing, industrial and computational mathematics
- Kathleen Hoffman
- Calculus of variations, differential equations, mathematical biology, singular perturbation theory
- Hye-Won Kang
- Stochastic modeling, analysis, and simulation of biological systems
- Brad Peercy
- Mathematical biology, differential equations, bifurcation theory, perturbation theory

### Optimization and Control

- Muddappa Gowda
- Applied analysis, optimization
- Osman Guler
- Convex programming, computational complexity, interior-point methods, mathematical programming
- Jacob Kogan
- Control theory, optimization, data/text mining, machine learning
- James Lo
- Computational intelligence, intelligent systems, neural networks approach to systems control and signal processing, stochastic systems
- Florian Potra
- Thomas Seidman
- Control theory, non-linear partial differential equations, inverse problems
- Jinglai Shen
- Control theory, nonsmooth switching systems, continuous and dynamic optimization, nonlinear and geometric control theory

### Probability and Stochastic Processes

- Thomas Armstrong
- Functional analysis and measure theory, probability, mathematical economics
- Hye-Won Kang
- Stochastic modeling, analysis, and simulation of biological systems
- Weining Kang
- Probability theory, stochastic processes, and their applications
- Thu Nguyen
- Stochastic Approximation, Monte Carlo Methods, Bayesian Estimation/Inference, Machine Learning, Stochastic Systems, Stochastic Processes, Stochastic Simulation, Dynamical Systems
- Muruhan Rathinam

### Computational Statistics

- Nagaraj Neerchal
- Time series analysis, overdispersion models, environmental statistics, data analysis
- Thu Nguyen
- Stochastic Approximation, Monte Carlo Methods, Bayesian Estimation/Inference, Machine Learning, Stochastic Systems, Stochastic Processes, Stochastic Simulation, Dynamical Systems
- Anindya Roy
- Time series, econometrics, multivariate methods, mathematical finance

### Bayesian Statistics

- Thu Nguyen
- Anindya Roy
- Time series, econometrics, multivariate methods, mathematical finance
- Bimal Sinha
- Multivariate analysis, statistical inference, linear models, decision theory, robustness and asymptotic theory

### Decision Theory and Inference

- Yaakov Malinovsky
- Design theory, stochastic ordering, sampling, group testing, and nonparametric methods
- Thomas Mathew
- Inference in mixed models, multivariate analysis, exposure data analysis, tolerance regions, bioequivalence testing
- Bimal Sinha
- Multivariate analysis, statistical inference, linear models, decision theory, robustness and asymptotic theory

### Environmental Statistics

- Nagaraj Neerchal
- Time series analysis, overdispersion models, environmental statistics, data analysis
- Bimal Sinha
- Multivariate analysis, statistical inference, linear models, decision theory, robustness and asymptotic theory

### Multivariate Statistics

- Seungchul Baek
- Dimension reduction, semiparametrics, high-dimensional data analysis
- Thomas Mathew
- Inference in mixed models, multivariate analysis, exposure data analysis, tolerance regions, bioequivalence testing
- Bimal Sinha

### Time Series Analysis

- James Lo
- Computational intelligence, intelligent systems, neural networks approach to systems control and signal processing, stochastic systems
- Nagaraj Neerchal
- Time series analysis, overdispersion models, environmental statistics, data analysis
- Anindya Roy
- Time series, econometrics, multivariate methods, mathematical finance