Faculty

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

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

Cellular Neurophysiology, Biophysics of Neurons and Computational Neuroscience.

Development of innovative algorithms to solve optimal and nonlinear control synthesis problems, followed by applying those to challenging real-life problems is the main focus of our lab. Techniques developed in our lab so far include model predictive static programming and its various variants, single network adaptive critic, optimal dynamic inversion, generalized dynamic inversion, neuro-adaptive design etc. Application areas in aerospace include guidance and control of missiles, spacecrafts, lunar lander, aircrafts and UAVs. In bio-medicine, our focus has been computer controlled automatic drug delivery using feedback control theory and also deciphering the control activities of human brain. The feedback drug delivery philosophy has been demonstrated to work well (from in-silico experiments) to effectively cure various cancer problems. An important current research focus is to develop an adaptive and customized artificial pancreas system for Type-1 diabetic patients. Other problems of interest to our lab include path planning and guidance of ground vehicles, vibration control, temperature control, process control etc.

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.

Developing efficient CFD algorithms for fluid flows, including central schemes, kinetic schemes and relaxation schemes; exact capturing of shocks and contact discontinuities; low numerical diffusion methods; meshless or grid-free methods; Lattice Boltzman method; non-standard finite difference methods; aerodynamic shape optimization; algorithms for turbulence simulation.

My research interest is in studying constrained and related stochastic dynamical systems with computational mathematical tools. Current work includes applications to problems of data assimilation and rough path behaviour.

Space dynamical systems (such as satellite orbit estimation) and biochemical reactions are examples of fields of application.

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

Recent studies have identified a small subpopulation of cells within several cancers termed as cancer stem cells (CSCs). These CSCs play critical roles in tumor initiation, progression, and maintenance. Furthermore, CSCs remain unaffected by currently used chemotherapeutic drugs, thereby leading to chemotherapy failure and cancer recurrence. The research focus of my laboratory is to understand the unique molecular mechanisms operating within CSCs, and find novels ways for targeting them. Work done in my laboratory has identified AMP activated protein kinase (AMPK) as a key signaling molecule in the regulation of breast cancer stem-like properties, survival and drug resistance. Current interests are in understanding the molecular mechanisms downstream of AMPK activation that contribute to cancer progression.

I received a B.Tech in Electrical Engineering from IIT Kanpur and a PhD in Biomedical Engineering from the Johns Hopkins School of Medicine. My postdoctoral training was in the department of Neurobiology at Harvard Medical School with Dr. John Maunsell. I joined the Center for Neuroscience, IISc, in June 2011. From 2012 onwards, I have also been an associate faculty in the Electrical Engineering Department at IISc.
Our lab studies the neural basis of selective attention, with a focus on a brain rhythm called “gamma” (30-80 Hz), which is modulated by attentional load and is thought to be linked to high-level cognitive processes. Attentional mechanisms have been studied at several different recording scales – from single neurons in monkeys to diffuse population measures such as electro-encephalography (EEG) in humans. However, the relationship between signals recorded from such different scales is poorly understood. The long-term goal of this research is to elucidate the mechanisms of attention by linking the neural recordings obtained from these vastly different scales. This involves recordings from both humans and non-human primates using a variety of techniques while they are engaged in certain cognitive tasks, development of advanced signal processing techniques to build the “links” across recording scales, and mathematical modelling of brain signals, including gamma oscillations, using dynamical system approach as well as detailed biophysical models. Establishment of this cross-species, cross-scale link between brain signals has far reaching applications, such as in Brain-computer Interfacing (BCI) and clinical diagnosis of brain disorders.

Research Interests: Mechanisms of attention and gamma oscillations; Modeling and Signal Processing, Brain-Computer Interfacing.

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.

Signal Processing, Image Processing.

Calculus of Variations, Partial Differential Equations and geometric analysis.

Variational models and PDE models are widely employed in different branches of physics and engineering applications, from models in material sciences ( fracture models, micro-magnetics ) to image reconstruction, impedance tomography, invisibility cloaking etc. A theoretical understanding of the relevant functional from the calculus of variation and PDE perspective both facilitates and in turn is facilitated by the qualitative understanding of the physical phenomena involved.

I am also interested in some toy models in quantum field theory that leads to interesting geometric variational problems, like nonlinear sigma-models etc.

Dynamo theory and models, Planetary magnetism, Magnetohydrodynamics, Vortex dynamics.

Molecular simulation and coarse-graining methods. Membrane biophysics. Mechanobiology.

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.

Sirtuins in Cardiovascular diseases.

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

Cell division and fate in plants and yeasts.

Computational methods in medical imaging, medical image processing (reconstruction/analysis), physiological signal processing, photoacoustic tomography, and diffuse optical tomography.