Faculty

Bioinformatics, algorithms for genome sequencing, combinatorial algorithms, high-performance computing.

Machine Learning, Convex Optimisation, Autonomous Systems

Geometrically inspired and gauge theories for continuum mechanics of solids (emphasizing non-Euclidean defect kinematics) Non-equilibrium thermodynamics of solids and fluctuation relations valid far from equilibrium Defect engineering and metamaterials with acoustic band gaps Optimization based on stochastic search on Riemannian manifolds Computational Mechanics: Mesh-free and Finite Element Methods; Peridynamics; SPH-based simulations in computational impact dynamics Mechanics of structured continua; the role of local symmetry in the continuum physics of solids; applications to modelling of plasticity, damage, piezo-electricity etc. Stochastic Filtering Techniques for Inverse Problems; nonlinear structural system identification and medical diagnostic imaging in soft materials Bayesian updates on Riemannian manifolds.

Debnath Pal’s group works at the interface of biology and chemistry, mixing it with interesting and innovative ideas from physics, maths and computer science. His research application areas span the domains of genomics, transcriptomics, proteomics, metabolomics, structural biology and drug discovery. His group develops new methods and algorithms to address unanswered problems. He is interested in building next generation life science applications on accelerator platforms. He also runs a wet laboratory to test some of his computational work.

Computational mechanics, Stochastic finite elements, Probabilistic methods, Domain decomposition techniques, Parallel computing.

Role of Dynamic Nanoscale Molecular Organization in the Signal processing at the Synapse.

Uncertainty quantification and analysis – Theory, Numerics, Software, Applications; Bayesian Inference and Data Assimilation; Dynamic data-driven stochastic ocean modeling; Cyclone track prediction; Optimal routing of marine and aerial vehicles.

Electromagnetics, Antenna.

Time series analysis; Applications in neuroscience; Nonlinear dynamics and chaos.

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

Image Processing, Algorithms, Convex Optimization

Design and analysis of algorithms, graph theory

Single-molecule biophysics, DNA-Protein interactions, Super-resolution imaging, genome organization

Epithelial Mesenchymal Plasticity; Non-genetic mechanisms of adaptive resistance; Collective behaviour in cancer population.

Matrix analysis & Numerical methods, Sampling & Estimation in high dimensions, Optical properties & Condensed matter, Computational physics.

Homogenization, Exact Controllability, Optimal Control ; Tomography and Image Reconstruction; Viscosity Solutions and Differential Games.

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.

Error-Control Coding, Quantum Coding, Information Theory, Data Compression, DNA-Based Data Storage, Discrete Applied Mathematics (a broad umbrella for much of what I do).

Multi-modal Neuroimaging using Machine Learning

Cell Metabolism and related disorders