data science, graph algorithms, distributed algorithms, approximate routing, classification and clustering for high-dimensional data, coloring and its generalizations, computational molecular biology

Improving performance and reliability of networked systems, specifically cloud-based systems, mobile and wireless systems, and the Internet. Also, interested in designing technologies for developing regions.

Machine learning, structured output learning, algorithmic fairness, weak supervision, probabilistic inference, graph
mining, social media analysis, data science, big data, computational social science.

data science, algorithms for analysis of biological networks, gene and pathway regulation in human development, algorithms for precision medicine, computational approaches to pharmacogenomics and drug discovery or repositioning

data science, statistical signal processing, inverse problems, compressed sensing, information theory, convex optimization, machine learning, algorithms for geophysical signal processing, compressed sensing architectures and evaluation, video and image data acquisition and processing

Applied dynamical systems, applied probability theory, kinetic theory, agent-based modeling, mathematical models of the economy, theoretical and computational fluid dynamics, complex systems science, quantum computation
Current research emphasis is on mathematical models of economics in general, and agent-based models of wealth distributions in particular. The group's work has shed new light on the tendency of wealth to concentrate, and has discovered new results for upward mobility, wealth autocorrelation, and the flux of agents and wealth. The group's mathematical description of the phenomenon of oligarchy has also shed new light on functional analysis in general and distribution theory in particular.
Secondary projects include new directions in lattice Boltzmann and lattice-gas models of fluid dynamics, kinetic theory, and quantum computation.

computer architecture, computer systems, power-aware computing, embedded systems, mobile computing, computer systems for machine learning, workload characterization, quantum computing, learning sciences and computer systems for human subjects research

Quantum Information, Quantum Simulation, Adiabatic Quantum Computation, Computational Physics
Quantum information faces three basic questions. Firstly, what are quantum computers good for? Secondly, how do we build one? Thirdly, what will quantum information contribute if technological obstacles to constructing a large scale quantum computer prove insuperable? The first question is the search for problems which quantum computers can solve more easily than classical computers. The second is an investigation of which physical systems one could use to build a quantum computer. The third leads to the search for spinoffs in classical computation, and the question of where the classical/quantum boundary lies. I am interested in all three questions.

Signal and image processing, tomographic image formation and object characterization, inverse problems, regularization, statistical signal and imaging processing, and computational physical modeling. Applications explored include medical imaging and image analysis, environmental monitoring and remediation, landmine and unexploded ordnance remediation, and automatic target detection and classification.

Parallel Computing: Fault Tolerance and Graceful Degradation. Applications of Graph The-
ory and Discrete Mathematics to Network modelling and Communications Problems, such
as Hypercube Permutation Routing and Routing Problems in Weighted Graphs. Theory of
NP-Completeness.