3852 East Hall
530 Church Street
Ann Arbor, MI 48104
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.
What do I care about, academically?
- Mathematical problems motivated by tangible, real-world questions. 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?
- 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.
- Working on unifying various reward and problem frameworks in reinforcement learning and bandits with Aldo Pacchiano (Broad Institute of MIT and Harvard) and Mirco Mutti (Politecnico di Milano)
- Designing optimal algorithms for offline-to-online transfer in latent bandits with Kevin Tan (University of Pennsylvania).
- Extending my work with Rishi Sonthalia (UCLA) and Kashvi Srivastava (UMich) to more complex denoising models.
- Working on algorithms for offline policy evaluation (OPE) in linear bandits, and the role of the geometry of action sets.
- 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.
- Thinking about extensions of De Finetti’s theorem to decision processes.
- 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?
- Work on preference-based variants of sequential decision making problems.
- Using the multi-step inverse kinematics perspective for designing algorithms that work outside of Markovian assumptions and have strong empirical performance.
- Explore multi-objective 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.
- Causal inference and its interaction with sequential decision making and RL.
- Using insights from machine learning for biology. In a specific example, learning a hierarchical or causal structure from genomics.
- Dive deeper into the theory behind GNNs and deep learning in general.
- Algorithmic fairness.
|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 7, 2023||Invited to attend the Princeton ML theory Summer School, 2023!|
|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.|