About Me
I am a 5th year Ph.D. Candidate in Electrical Engineering and Computer Science at MIT CSAIL. I work in the Julia Lab under the supervision of Dr. Alan Edelman and Dr. Christopher Rackauckas. My research interests are in the intersection of AI compilers and AI for science, where I focus on performance optimizations for neural network training and inference in the context of scientific computing. For some representative examples of my work, see Publications and Presentations.
News
Aug. '25
New publication on Neural Surrogates for Hypersonic Flow Predictions accepted at AIAA SciTech 2025.
Jul. '25
NonlinearSolve.jl now accepted at ACM Transactions on Mathematical Software!
May '25
New pre-print on Semi Explicit Neural DAEs released on ArXiv.
Nov '24
New publication on B-Spline KANs accepted at NeurIPS Workshop on Science for Deep Learning.
Mar. '24
New pre-print on NonlinearSolve.jl released on ArXiv.
Experience
Current Position
Massachusetts Institute of Technology
Electrical Engineering and Computer Science
Ph.D. Candidate
Advisor(s): Dr. Alan Edelman
Sep. '21 - May '26 (est.)
Cambridge, MA
GPA: 4.9 / 5.0
Education
Massachusetts Institute of Technology
Electrical Engineering and Computer Science
Master of Science (S.M.)
Advisor(s): Dr. Alan Edelman
Sep. '21 - May '23
GPA: 4.8 / 5.0
Indian Institute of Technology Kanpur
Computer Science and Engineering
Bachelor of Technology (B.Tech.)
Jul. '17 - May '21
GPA: 9.9 / 10.0
Past Positions
| Summer '25 | Student Researcher, XLA:TPU Optimizations | Google Cloud | New York City, NY |
| Summer '24 | Graduate Research Intern, Parallel Computing Lab | Intel Parallel Computing Labs | Santa Clara, CA |
| Summer '22 | Student Researcher | Google AI | Mountain View, CA |
| Jan - July '21 | Engineering Simulation Intern | Julia Computing | Remote |
| Jan - Nov '20 | Research Intern | Vector Institute | Toronto, CAN |
| Summer '18, '19 | Google Summer of Code Participant | JuliaLang Organization | Remote |
| Summer '18 | Software Engineering Intern | IIT Kanpur NYO Office | Kanpur, IND |
Publications
My research interests are in the intersection of AI compilers and AI for science, where I focus on performance optimizations for neural network training and inference in the context of scientific computing. Occasionally, I explore domains outside my major area of focus and have published a few works in (Multi-Agent) Reinforcement Learning, Differentiable Graphics, etc.
Conference Proceedings
Geometry & Mesh Invariant Neural Surrogates for Hypersonic Flows
AIAA SciTech Forum (Accepted, To Appear) • 2025
Locally Regularized Neural Differential Equations: Some Black Boxes Were Meant to Remain Closed!
International Conference on Machine Learning (ICML) • 2023
Avik Pal, Alan Edelman, & Chris Rackauckas
Continuous Deep Equilibrium Models: Training Neural ODEs Faster by Integrating Them to Infinity
IEEE High Performance Extreme Computing (HPEC) • 2023 • Best Student Paper Award
Avik Pal, Alan Edelman, & Chris Rackauckas
Opening the Blackbox: Accelerating Neural DEs by Regularizing Internal Solver Heuristics
International Conference on Machine Learning (ICML) • 2021
Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time ESNs as Implicit ML
International Federation of Automatic Control (IFAC) • 2021
Ranjan Anantharaman, Anas Abdelrehim, Anand Jain, Avik Pal, Danny Sharp, Utkarsh Rajput, & Chris Rackauckas
Composing Modeling and Simulation with Machine Learning in Julia
International Modelica Conference • 2021
Humor@IITK at SemEval-2021 Task 7: Language Models for Quantifying Humor And Offensiveness
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval) • 2021
TorchGAN: A Flexible Framework for GAN Training and Evaluation
Journal of Open Source Software (JOSS) • 2021
Avik Pal, & Aniket Das
Journal Papers
NonlinearSolve.jl: High-Performance and Robust Solvers for Systems of Nonlinear Equations
ACM Transactions on Mathematical Software (TOMS) (Accepted, To Appear) • 2024
(Peer-Reviewed) Workshop Papers
Understanding the Limitations of KANs: Convergence Dynamics and Computational Efficiency
NeurIPS Workshop on Science for Deep Learning • 2024
Avik Pal, & Dipankar Das
Fashionable Modelling with Flux
NeurIPS Workshop on Systems for Machine Learning • 2019
Mike Innes, Elliot Saba, Keno Fischer, Dhairya Gandhi, M.C. Rudilosso, Neethu Mariya Joy, Tejan Karmali, Avik Pal, & Viral B. Shah
Pre-prints
Making Waves in the Cloud: A Paradigm-Shift for Scientific Computing and Ocean Modeling through Compiler Technology
Under Review • 2025
Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints
Under Review • 2025
Avik Pal, Alan Edelman, & Chris Rackauckas
Differentiable Programming for Differential Equations: A Review
Under Review at SIAM Review • 2024
Presentations
Learned Cost Models for TPU Window Config Ranking, Google ML Compilers Reading Group 2025