About Me ​

I am a year Ph.D. Candidate in Electrical Engineering and Computer Science at MIT. I work in the Julia Lab under the supervision of Dr. Alan Edelman and Dr. Christopher Rackauckas. My research interests are in the application of numerical methods and scientific computing in deep learning.

In Summer' 22, I was a Student Researcher at Google AI, where I worked on differentiable wildfire simulators in Jax with Dr. Andrey Zhmoginov and Dr. Lily Hu. I completed my undergraduate studies in Computer Science and Engineering at Indian Institute of Technology Kanpur.

News ​

Experience ​

Current Position ​

Education ​

Past Positions ​

Ongoing Projects ​

Boundary Value Problems ​

• Developing fast and accurate solvers for Boundary Value Problems (BVPs) in Julia.
• Exploiting structural sparsity with automatic differentiation for fast Jacobian computation for BVPs.
• Embedding non-linear equality constraints in neural network dynamics using BVPs.

Complementarity Problems ​

• Developing solvers for Complementarity Problems (CPs) in Julia.
• Embedding general inequality constraints in neural network dynamics using CPs.
• Developing adjoint equations for efficient back-propagation through solutions of CPs.

Neural BVPs ​

• Developing Adjoint Methods for Differentiating through solutions of Boundary Value Problems.
• Scaling the Adjoints for large scale Neural Networks.

Research ​

My main research interest involves Scientific Machine Learning -- combining Deep Learning with Differential Equations. I focus on exploring their applications in tackling scalability issues with standard scientific computing models.

Occasionally, I explore domains outside my major area of focus and have published a few works in (Multi-Agent) Reinforcement Learning, Differentiable Graphics, Deep Learning Systems, etc.

Most Significant Bits ​

Less Significant Bits ​

Teaching ​

Open Source Software ​

For a list of open source softwares I have written and maintain see my GitHub Profile

Talks ​