About me

I am a Master of Science in Machine Learning student at Carnegie Mellon University (expected graduation: December 2025). I am interested in topics like Graph Representation Learning, Natural Language Processing, Foundation Models and applications to diverse domains like User Recommendations, Code Generation and Quantum Computing. Prior to my current studies, I spent two impactful years as a Research Fellow at Microsoft Research, India, where I was forunate to be advised by Arun Iyer, Aditya Kanade and Sundararajan Sellamanickam. During this time, I developed novel methods for graph representation learning and natural language-to-code generation. I received the Bachelor of Engineering in Computer Science Degree, along with a Minor in Physics from BITS Pilani in 2021.

Please find my Resume here.

Publications:

Recent updates:

  • August 2024: Began the Master of Science in Machine Learning program at CMU!
  • June 2024: Our paper on repository-level code generation was accepted at NeurIPS '23 !
  • September 2023: Our paper on graph representation learning was accepted at NeurIPS '23 !(also presented at MLG, KDD '23).
  • July 2021 - August 2022: Worked as a Software Engineer II at Walmart Global Tech.
  • March 2021 - June 2021: Worked as a Research Consultant at Terra Quantum AG, Switzerland, with publications and patents in quantum computing. Accepted in November 2022: Co-authored a journal article on "Capturing symmetries of Quantum Optimization Algorithms using Graph Neural Networks. Patent Pending: Application for "METHOD AND SYSTEM FOR SOLVING QUBO PROBLEMS WITH HYBRID CLASSICAL-QUANTUM SOLVERS."
  • August 2021: Completed B.E. in Computer Science with a minor in Physics at BITS Pilani.
  • January 2021 - May 2021: Completed an undergraduate thesis on the application of Graph Neural Networks to Quantum Computing.
  • March 2021 - Developed the PAL Desktop Application at MIT Media Lab for screentime intervention.
  • May 2020 - July 2020: Worked as a Software Engineer (Summer Intern) at Walmart Global Tech, focusing on scaling automated tests.