Chinmaya Kausik

Mathematics Ph.D. Student, University of Michigan.


Hi there! I’m Chinmaya Kausik, a 3rd year mathematics Ph.D. candidate at UMich working on sequential decision making, statistics, optimization and machine learning. I am being co-advised by Prof. Ambuj Tewari and Prof. Martin Strauss.

Check out my papers, projects, and personal interests!

What do I care about, academically?
  • Tackling tangible, real-world questions with a principled mathematical approach. These days, my work focuses on sequential decision making under various settings - offline-to-online transfer, partial observability/latent information and non-standard feedback and reward models. I also have side projects in deep learning. On the other hand, a lot of my undergraduate background was in geometry, topology and dynamics, with work in computer-assisted topology and geometry.
  • Increasing accessibility to and in higher mathematics and creating communities where ideas cross pollinate and people pull each other up. I have started the Stats, Physics, Astronomy, Math (SPAM) graduate student social initiative at the University of Michigan. I also co-founded and co-organize Monsoon Math Camp. I have also been involved in building and expanding other mathematical communities, like platforms for the PolyMath REU, DRP programs and the undergraduate math organization at IISc, etc.
What am I doing these days?
  • Interning at Microsoft in the advertiser optimization team under Ajith Moparthi, working on bidding algorithms and value maximization!
  • Collaborating with Yonathan Efroni (Meta), Aadirupa Saha (Apple), Nadav Merlis (ENSEA) on algorithms for bandit and reinforcement learning algorithms with feedback at varying costs and accuracies, also called multi-fidelity feedback.
  • Thinking about principled approaches to data collection and learning for RLHF under real-world considerations.
  • Formulating problems in learning under latent information and nonstationarity in bandits.
  • Organizing an interdepartmental social initiative, SPAM (Statistics, Physics, Astronomy, Mathematics).
  • Fleshing out ideas for more academic communities like Monsoon Math.
What do I want to learn about/do in the future?

primary goals

  • Complete an empirical study of RLHF methods on LLMs of varying size and understand the implementation nuances of major RLHF methods.
  • Work on a large scale applied recommender systems project using the latent bandit algorithms that I designed (LOCAL-UCB and ProBALL-UCB).
  • Applying ideas from RLHF and bandits to mental health studies that my advisor is involved in.


  • Design a codenames bot using one LLM and train it againts players designed using a different LLM.
  • Explore the nuances of implementing various RL algorithms in simulated motion settings.
  • Design meaningful experiments to compare LLM agents trained using language feedback with RL agents trained using numerical feedback, using benchmark frameworks like LLF-bench.


Nov 29, 2023 I have received the Rackham International Student Fellowship, which is offered to 25 students across graduate departments under Rackham!
Oct 29, 2023 Our paper “Denoising Low-Rank Data Under Distribution Shift: Double Descent and Data Augmentation” has been accepted to the NeurIPS workshop on the Mathematics of Modern Machine Learning (M3L)!
Jun 15, 2023 Two new preprints (confounded RL and double descent phenomena with input noise) added to arXiv!
Apr 29, 2023 My paper on learning mixtures of Markov chains and MDPs with Kevin Tan and my advisor, Prof. Tewari, has been accepted to ICML 2023 (Oral)!
Apr 07, 2023 Invited to attend the Princeton ML theory Summer School, 2023!