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.
side-quests
- 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
news
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! |
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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. |