Chinmaya Kausik

Mathematics Ph.D. Student, University of Michigan. Curriculum Vitae.


3852 East Hall

530 Church Street

Ann Arbor, MI 48104

Hi there! I’m Chinmaya Kausik, a rising 2nd year mathematics Ph.D. student 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 past projects (along with post-mortems), publications and personal interests!

What do I care about, academically?
  • Mathematical problems motivated by tangible, real-world questions. These days, my work focuses on sequential decision making with offline data. I also have side projects in deep learning and control theory, and I intend to combine my current interests with ideas in differential privacy very soon. 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. This is part of why I co-founded and co-organize Monsoon Math Camp - an online math camp for promising high school students introducing them to advanced undergraduate and graduate-level math. You should check it out! 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?
  • Working on projects on offline RL in confounded tabular MDPs and learning mixtures of MDPs.
  • Thinking about minimax optimal algorithms for offline policy evaluation (OPE) and the role of the geometry of action sets.
  • Working on double descent in denoising under the guidance of Rishi Sonthalia. Work continued from MREG 2022.
  • Continuing work on our project from LOGML 2022! I was a participant in Dr. Eli Meirom’s group, planning to work on using RL for graph rewiring in GNNs to prevent oversquashing for long range problems.
  • Mentoring undergraduate students on a project about using machine learning to enhance model predictive control.
  • Fleshing out ideas for more academic communities like Monsoon Math.
What do I want to learn about/do in the future?

primary goals

  • Work on learning other mixtures of time series with control input.
  • Work on generating synthetic data privately for time series, starting with MDPs and Markov Chains.
  • Learn about differential privacy and its intersection with sequential decision-making.
  • Start maintaining my progress log again.
  • Learn about safe RL and think about techniques beyond primal-dual ones, perhaps using model-based RL with uncertain models.
  • Watch lectures from the Data Driven Decision Processes program at the Simons Institute this semester.
  • Gain a comprehensive view of techniques that go into minimax lower bounds in RL.


  • The theory behind GNNs and deep learning in general.
  • Causality and its interaction with sequential decision making and RL.
  • Algorithmic fairness.
  • Geometric and topological insights for data analysis and machine learning (for example, non-positive curvature representation learning).
  • Natural Language Processing.
  • Using insights from machine learning for biology. In a specific example, learning a hierarchical or causal structure from genomics


Aug 14, 2022 My paper with Prof. Stefan Friedl and Jose Pedro Quintanilha on an algorithm for generalized Seifert matrices has been accepted by the Journal of Knot Theory and its Ramifications!
Aug 14, 2022 Monsoon Math has unfortunately been cancelled for summer 2022.
Mar 5, 2022 Monsoon Math is gearing up for 2022! Email me if you have ideas about making it more effective and impactful.