Faculty

My research interests are in the intersection of statistical physics, combinatorics and probability.

Fluid Mechanics; Combustion; Numerical Methods; High Performance Computing; Data Science; Scientific Machine Learning

MEMS/NEMS MEMS and NEMS Sensors Fundamental research in NEMS/MEMS

Visual perception, Image processing, Neuroscience

Compilation for multicores and accelerators, Automatic parallelization, Programming language and runtime system design

Computer Vision, Deep-learning, Video Analytics, Multimedia

Materials Science Ceramics

Tensor Decompositions, Neural Networks, Numerical Linear Algebra, Generalized Inverses of Tensors, Wavelets in Scientific Computing, High-Performance Computing.

Seismic Imaging; Seismic Interferometry; Earthquake Seismology

Machine Learning, Convex Optimisation, Autonomous Systems

Visual analytics, Data association over graphs, Data fusion and consistency, Applications of computer vision and machine learning in bio-medical image analysis, video surveillance.

Research areas that are being pursued actively in the laboratory are broadly described under two categories: (A) Systems Biology, (B) Structural Bioinformatics, both combined towards addressing fundamental aspects of disease processes, obtaining global perspectives of perturbations in disease and application of that knowledge to tackle specific problems in disease diagnosis, drug discovery and immunology. A range of mathematical models, at multiple levels and scales are utilized for these.

Design and analysis of algorithms, graph theory

Image Processing, Algorithms, Convex Optimization

Mathematical signal processing, statistical inference and learning, applied to communication systems, network sciences, and computational imaging

Solid Mechanics, Material Instabilities, Mathematical Aspects of Materials Science.

Neural mechanisms of learning and memory

How does our brain enable us to pay attention selectively to some things, and to ignore others? What happens in the brain when we make important decisions? Our research focuses on understanding the neural basis of cognitive phenomena such as selective attention and decision making. To address these questions we follow a quantitative approach that combines neuroscience experiments, model-based analyses (e.g., linear/nonlinear dynamical systems, control theory, machine learning) as well as large-scale computer simulations. We directly measure or perturb brain activity with a variety of techniques, including functional neuroimaging (fMRI), diffusion imaging (dMRI), high-density electro-encephalography (EEG), and transcranial electrical and magnetic stimulation (tES/tMS). The overarching goal is to develop a unified framework that describes how cognitive phenomena emerge from neural computations by a systematic analysis of brain and behavior.

Statistical Machine Learning, Spectral Graph Methods, Deep Learning, Algorithmic Algebra.

Finite Element Analysis, High Performance Computing, Computational Fluid Dynamics, Bio-medical Applications