I am a Ramanujan Faculty Fellow in the Department of Mechanical Engineering at Indian Institute of Technology Palakkad. My research lives at the boundary of computational science, engineering, and applied mathematics – focused on building mathematical and computational methods for physical systems governed by partial differential equations.
In practice, this means combining numerical simulation, mathematical modeling, and scientific machine learning to understand and solve forward and inverse problems across thermo-fluid systems, electromagnetics, and multi-physics phenomena. The underlying ambition is simple: to develop tools that make complex physical systems legible – amenable to monitoring, prediction, and understanding.
Beyond research, I enjoy teaching, mentoring, and conversations that wander well beyond science. My door is open.
Open Positions
I am actively looking for motivated students and researchers to join the group at IIT Palakkad.
M.S. (Research) & Ph.D. -- Positions are available in computational modeling and scientific machine learning for thermo-fluid and other multi-physics systems. Candidates with a background in mechanical or aerospace engineering, or applied mathematics are encouraged to apply through the IIT Palakkad admissions portal.
B.Tech. / M.Tech. Projects -- IIT Palakkad students interested in thesis or project work are welcome to get in touch directly by email.
Postdoctoral Positions -- no funded openings currently, but strong candidates are encouraged to reach out to discuss fellowship options (SERB-NPDF, NBHM, IPDF etc.).
Recent News
Research at a Glance
Parameter Estimation & Inverse Problems
- Deterministic and Bayesian approaches for unknown parameters in PDE-based models
- Regularization methods for stable recovery from sparse and noisy measurements
- State estimation and real-time monitoring of physical systems
- Digital twins for thermo-fluid and multi-physics systems
- Applications in thermo-fluid, other engineering, and biological systems
Numerical Methods for PDEs
- High-order discretization methods for PDEs (e.g. Discontinuous Galerkin)
- Structure- and constraint-preserving numerical schemes
- Applications in fluid flow, electromagnetics, MHD, and flow MRI
- Time integration and solver strategies for stiff problems
- Scalable solvers and high-performance computing for large-scale PDE systems
Emerging Directions
- Scientific machine learning and physics-informed neural networks for PDEs
- Uncertainty quantification for robust engineering predictions
- Reduced order models for fast and reliable simulation
- High-order methods for computational electromagnetics--foundations in place, looking for motivated students to take this further
- MHD, compressible flow, and aeroacoustics -- areas I intend to grow into