I am a 4th year Ph.D. student in the Statistics Department at the University of Michigan advised by Ambuj Tewari. Previously, I studied Computer Science and Chemical Engineering also at UM where I worked with Mahdi Cheraghchi, Sindhu Kutty, and Andrej Lenert. Last summer (2024), I interned at Apple with Kunal Talwar and Hilal Asi, where I worked on online learning and differential privacy. Currently, I’m back interning at Apple working on the theory and practice of generative machine learning with Kunal Talwar, Hilal Asi, and Satyen Kale. This coming summer (2025), I will be interning at Google Research with Matthew Joseph!

My research interests broadly lie in statistical learning theory and algorithm design. Some specific areas of interest include: online learning, bandits, differential privacy, adversarial robustness, and algorithms with predictions. Nowadays, I am interested in the theory and practice of generative machine learning, especially problems around efficient alignment and reasoning.

I am grateful to be supported by the National Science Foundation Graduate Research Fellowship (NSF GRFP) and the 2025 Apple Scholars in AI/ML PhD Fellowship.

Click here for my (probably approximately) recent CV.

Working Preprints

Some work on generation and alignment coming soon :)

In Submission

1. Tracking the Best Expert Privately
   with Hilal Asi and Aadirupa Saha
   In Submission, 2025.
2. Representative Language Generation
   with Charlotte Peale and Omer Reingold
   In Submission, 2025.
3. Generation through the lens of learning theory
   with Jiaxun Li and Ambuj Tewari
   In Submission, 2025.
4. Generation from Noisy Examples
   with Ananth Raman
   In Submission, 2025.
5. Faster Rates for Private Adversarial Bandits
   with Hilal Asi, Kunal Talwar
   In Submission, 2025

Publications

1. The Complexity of Sequential Prediction in Dynamical Systems
   with Unique Subedi, Ambuj Tewari
   Oral at Conference on Learning for Dynamics and Control (L4DC), 2025

2. A Unified Theory of Supervised Online Learnability
   with Unique Subedi, Ambuj Tewari
   Outstanding Paper Award at Conference on Algorithmic Learning Theory (ALT), 2025

3. A Characterization of Multioutput Learnability
   with Unique Subedi, Ambuj Tewari
   Journal of Machine Learning Research (JMLR), 2024

4. Online Classification with Predictions
   with Ambuj Tewari
   Conference on Neural Information Processing Systems (NeurIPS), 2024

5. Smoothed Online Classification can be Harder than Batch Classification
   with Unique Subedi, Ambuj Tewari
   Conference on Neural Information Processing Systems (NeurIPS), 2024

6. Multiclass Transductive Online Learning
   with Steve Hanneke, Amirreza Shaeiri, Unique Subedi
   Spotlight at Conference on Neural Information Processing Systems (NeurIPS), 2024

7. Apple Tasting: Combinatorial Dimensions and Minimax Rates
   with Ananth Raman , Unique Subedi, Ambuj Tewari
   Conference on Learning Theory (COLT), 2024

8. Online Learning with Set-Valued Feedback
   with Unique Subedi, Ambuj Tewari
   Conference on Learning Theory (COLT), 2024

9. Online Infinite-Dimensional Regression: Learning Linear Operators
   with Unique Subedi, Ambuj Tewari
   Conference on Algorithmic Learning Theory (ALT), 2024

10. Multiclass Online Learnability under Bandit Feedback
    with Ananth Raman , Unique Subedi, Idan Mehalel, Ambuj Tewari
    Conference on Algorithmic Learning Theory (ALT), 2024

11. On the Learnability of Multilabel Ranking
    with Unique Subedi, Ambuj Tewari
    Spotlight at Conference on Neural Information Processing Systems (NeurIPS), 2023

12. On Proper Learnability between Average- and Worst-case Robustness
    with Unique Subedi, Ambuj Tewari
    Conference on Neural Information Processing Systems (NeurIPS), 2023

13. Multiclass Online Learning and Uniform Convergence
    with Steve Hanneke, Shay Moran, Unique Subedi, Ambuj Tewari
    Conference on Learning Theory (COLT), 2023

14. Online Agnostic Multiclass Boosting
    with Ambuj Tewari
    Conference on Neural Information Processing Systems (NeurIPS), 2022

15. Design of thermophotovoltaics for tolerance of parasitic absorption
    with Tobias Burger, Andrej Lenert
    Optics Express, 2019

Projects

I have also been working on more empirical research, especially towards understanding the abilities and limitations of the Transformer architecture. Below are some ongoing projects which highlight some of my findings.

• Learning Permutations
• On the Limitations of Self-Attention as a Sequence-to-Sequence Map

Talks

• A Unified Theory of Supervised Online Learnability (ALT 2025)
• Generation through the lens of learning theory (Apple 2025)
• Generation through the lens of learning theory (NEU CS Theory Seminar) [slides]
• Generation through the lens of learning theory (STATS 700 Guest Lecture)
• Trichotomies in Online Learnability (Student ML Research Seminar 2024) [slides]
• Trichotomies in Online Learnability (Apple 2024) [slides]
• Revisiting the Learnability of Apple Tasting (MSSISS 2024)
• Multiclass Online Learnability under Bandit Feedback (ALT 2024)
• Multiclass Online Learning and Uniform Convergence (UM EECS Theory Seminar) [slides]
• On Classification-Calibration of Gamma-Phi Losses (COLT 2023) [slides]

Awards and Fellowships

• Apple Scholars in AI/ML PhD Fellowship (2025)
• ALT Outstanding Paper Award (2025)
• MSSISS Best Oral Presentation (2024)
• NeurIPS Scholar Award (2022, 2023)
• Outstanding First-year Ph.D. Student (2022)
• NSF Graduate Research Fellowship (2022)
• First-year Rackham Fellowship (2021)

Teaching

I really enjoy teaching. Here are a couple courses and organizations that I have taught for in the past:

• PhD Math Preparation Workshop, Fall 2023
• STATS 507 (Data Science using Python), Fall 2022, Winter 2023
• STATS 315 (Introduction to Deep Learning), Winter 2022
• STATS 250 (Introduction to Statistics), Fall 2021
• InspiritAI, Summer 2021
• AI4ALL, Summer 2021
• ChE 330 (Chemical and Engineering Thermodynamics), Winter 2018

Hobbies

Apart from research, I am a big fan of bodybuilding and actively keep up with the Mr. Olympia.