Hi there 👋! I’m an Applied Scientist at Microsoft AI. My interests are in building efficient and interpretable machine learning systems. I work on distributed data pipelines, large-scale daily re-training, counterfactual modeling, analyzing prediction fluctuations and predicting model behavior. I also enjoy building software frameworks and methods that push efficiency further. I have built a PyTorch-like framework with accelerated hardware-optimized kernels, explored paratemer-efficient pre-training techniques, and built Bayesian optimization techniques for neural architecture search.
I earned my Master’s degree at the Language Technologies Institute, Carnegie Mellon University and my Bachelor’s degree in Computer Science and Engineering at PES University. I briefly worked with Prof. Emma Strubell on ways to reuse parameters while growing and fine-tuning models. I also worked with Prof. Yonatan Bisk on evaluating the semantic understanding capabilities of text-to-image generation models. In the past, I have worked on various other projects and you can find a list of my publications and my resume.
Personal Interests
I believe in the power of visual storytelling and teaching. I enjoy making study notes and I was awarded by PES University’s Department of Computer Science and Engineering for my immense contributions. Feel free to check out my notes! They are categorized by subject – most of them are CS subjects, but some are math/science.
I learn to play the South-Indian instrument Saraswathi Veena in the Carnatic style. I also enjoy cooking and baking quite a bit. I have recently been super into hiking in and around the Seattle area. 🌲
News
- 2025-03: Returning to Microsoft AI as a full-time Applied Scientist
- 2024-12: Graduated with my Master’s from CMU LTI
- 2024-05: Excited to spend my summer at Microsoft AI as an Applied Scientist Intern
- 2023-12: Attending my first NeurIPS
- 2023-08: Starting my Master’s at CMU LTI
- 2023-05: I graduated with my Bachelor’s in Computer Science and Engineering from PES University.
- 2023-01: Spending the next 6 months interning at Apple to work on Applied ML!
Education
Carnegie Mellon University | Pittsburgh, PA
Master of Computational Data Science (MCDS)
Coursework: Intro to Deep Learning (PhD), Intro to ML, On-Device ML (PhD), Cloud Computing, Advanced NLP (PhD)
PES University | Bangalore, India
Bachelor of Technology in Computer Science and Engineering
Awards: Prof. CNR Rao Scholarship (top 2%), Immense Contribution Award