Two Ph.D. positions are open under my guidance in WPI's Department of Aerospace Engineering, USA. 

For more details, see : Positions Available

About Me    

From July 2024, I will be joining the Department of Aerospace Engineering, Worcester Polytechnic Institute as an assistant professor (tenure-track). I am currently serving as an assistant professor of applied mathematics (research) at Brown University in the USA. My academic journey includes earning both my PhD and Master's degrees in Aerospace Engineering from the esteemed Indian Institute of Science (IISc) in India. Following this, I engaged in postdoctoral research at the Tata Institute of Fundamental Research—Center for Applicable Mathematics (TIFR-CAM) in India. Subsequently, I transitioned to Brown University to continue my postdoctoral research within the Division of Applied Mathematics.

My research is uniquely positioned at the intersection of mechanical/aerospace engineering, applied mathematics, and computation. I am particularly dedicated to advancing scientific machine learning algorithms that seamlessly integrate data and physics, offering versatile applications across computational physics. My areas of expertise encompass scientific machine learning, deep learning, data- and physics-driven deep learning techniques, uncertainty quantification, and propagation, as well as multi-scale/multi-physics simulations (solids, fluids, and acoustics). I bring proficiency in spectral/finite element methods, WENO/DG schemes, and domain decomposition techniques, among others. Beyond these, I am actively engaged in more traditional machine learning algorithms such as deep generative models, and novel artificial neural network architectures, such as quantum and graph neural networks. To this end, my interests also extend to spiking neural networks and other bio-inspired computing techniques. 

Experience:

• Assistant Professor (tenure-track) : Department of Aerospace Engineering, Worcestor Polytechnic Institute, USA. Starting July 2024.

• Assistant Professor of Applied Mathematics (Research) : Division of Applied Mathematics, Brown University, USA .   Jan. 2021 -  Present

• Postdoctoral Research Associate :  Division of Applied Mathematics, Brown University, USA .   Jan. 2019 -  Dec. 2020

• Postdoctoral Fellow : Centre for Applicable Mathematics, Tata Institute of Fundamental Research, India.  Sept. 2016 - Dec. 2018

• Junior Research Associate : Department of Aerospace Engineering, Indian Institute of Science, India.  Aug. 2015 - Jan. 2016

• Visiting Researcher : University of Luxembourg, Luxembourg, May 01 - July 31, 2013.

• Visiting Researcher : Technical University of Braunschweig, Germany, Mar 01 - April 31, 2012.

Education

•  Doctor of Philosophy (Ph.D), Aerospace Engineering, Indian Institute of Science, India  (2016).

•  Master of Engineering (M.E.), Aerospace Engineering, Indian Institute of Science, India (2010).

•  AMAeSI (Bachelor  in Aeronautical Engineering), The Aeronautical Society of India, India (2008).

•  Bachelor of Science (BSc - Mathematics), University of Madras, India (2008).

•  Diploma in Mechanical Engineering (T. Eng.), Institution of Mechanical Engineers, India (2003).

Research Funding

• (Co-PI) Numerics-Informed Neural Networks, Small Business Technology Transfer (STTR) program (Phase-II), U.S. Department of Defense,  ( Duration: Oct. 01, 2023 - Sept. 30,  2025, Status: Ongoing,  Award: $1.1M )

• (Co-PI) Numerics-Informed Neural Networks, Funded by Small Business Technology Transfer (STTR) program (Phase-I), U.S. Department of Defense,  ( Grant No: GR5291245, Duration: May 06, 2022 - Feb. 05, 2023, Status: Completed,  Award: $167K )

Editorial Board Member

I'm an editorial board member of the following international journals.

• Neural Networks, Elsevier (IF: 7.8)  [Journal Link]

• Neurocomputing, Elsevier (IF: 6.0)  [Journal Link]

• Journal of Machine Learning Research (IF: 5.177) [Journal Link]

• Frontiers in Neuroinformatics (IF:  3.5)  [Journal Link]

• Frontiers in Computational Neuroscience (IF:  3.2) [Journal Link]

Recently Invited Talks

• Mathematical Sciences Departmental Colloquium, WPI, USA.  (April 12, 2024). 

• Physics-Informed Neural networks and Neural Operator Networks: Methods and Applications, Oak Ridge National Laboratory, USA. (February 8, 2024) (Flyer)

• Phi-ML meets Engineering, The Alan Turing Institute, UK.  (February 1, 2024) (Flyer)

• A physics-informed neural network-based solution to inverse problems in high-speed fluid flows, International Symposium on Recent Trends in Numerical Methods, IIT Kanpur, India.  (January 21, 2024)

• Physics-Informed Deep Learning: Methods and Applications in Scientific Computing, Worcester Polytechnic Institute, USA.  (January 10, 2024)

• Physics-Driven Deep Learning Methods for Scientific Computing, 5th International Conference on Mathematical Techniques and Applications (ICMTA-2024), SRM, India. (January 3, 2024)

• Physics-Informed Deep learning: Merging Data with Physics, Shell .ai, Aryabhata Series, Shell Technology Center Bangalore, India, Oct. 20, 2023 (Flyer)

• Short Course on Physics-Informed Deep Learning at TIFR-CAM, Bengaluru, India (18 Oct.  2023) [Link]

• Scientific Machine Learning through the Lens of Physics-Informed Neural Networks, Lawrence Livermore National Laboratory, April 14, 2023. 

• Scientific Machine Learning through the Lens of Physics-Informed Neural Networks, SankhyaSutra Labs, India,  Feb. 24, 2023.  (Flyer)

• Physics-Informed Neural Networks for Inverse Problems in Supersonic Flows, Theoretical Division, Los Alamos National Laboratory, USA Oct. 24, 2022 

• Artificial Neural Networks for scientific computations: Embedding physics and data, The University of Texas at El Paso (UTEP), USA, Oct. 21, 2022.

• Physics-Informed Neural Networks for Scientific Computations: Algorithms and Applications, BIMSA-Tsinghua seminar on Machine Learning and Differential Equations, Yanqi Lake Beijing Institute of Mathematical Sciences and Applications, and Tsinghua University, China, Oct. 12, 2022. (Flyer)

• Physics-Informed Machine Learning: Merging Data and Physics, High-Performance Computing, and AI Predictive Tools in Fluids and Thermal, NIT Rourkela, India, July 26, 2022.

• Physics-Informed Machine Learning for scientific computations: Recent Advances and Applications, Theoretical Division, Los Alamos National Laboratory, USA, May 20, 2022.

• Physics-Informed Neural Networks: A new paradigm for learning physical laws, Conference on PDE and Numerical Analysis, Tata Institute of Fundamental Research - Center for Applicable Mathematics, Bengaluru, India, April 30, 2022. (Link)

• Physics-Informed Machine Learning for scientific computations: Recent Advances and Applications, LANS Seminar, Argonne National Laboratory, USA, February 23, 2022. (Flyer)

• Physics-Informed Machine Learning for scientific computations, ECE Colloquium Series, University of Connecticut, USA, November 19, 2021.

• Scientific Machine Learning: From PINNs to eXtended PINNs (XPINNs), SRM University, India, September 11, 2021. (Flyer) 

• A Generalized Space-Time Domain Decomposition based Extended Physics-Informed Neural Networks for partial differential equations: Method and Implementation, Carnegie Mellon University, USA, June 17, 2021.

• Parallel Physics-Informed Neural Networks via Domain Decomposition, Seminars of the Interdisciplinary Area of Computational Engineering and Science, COPPE/Federal University of Rio de Janeiro, Brazil,  June 10,  2021.  (Poster)

• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations,  AI Chair OceaniX Webinars 2021, IMT Atlantique, Brest, France, March 24, 2021.

• Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations,  AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning in Physics  Sciences, Stanford University, Palo Alto, California, USA, March 22, 2021.