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. This past summer (2024), I interned at Apple with Kunal Talwar, where I worked on online learning and differential privacy!

My research interests are broadly in statistical learning theory and algorithm design. Some specific areas of interest include: online learning theory, bandits, differential privacy, adversarial robustness, and algorithms with predictions. Currently, I am interested in bridging learning theory and generative machine learning with applications to Transformers and Large Language Models (LLMs).

I am grateful to be supported by the National Science Foundation Graduate Research Fellowship Program (NSF GRFP) from Fall 2022. Feel free to shoot me an email if you’d like me to review your GRFP application! You can find my application materials here.

Click here for my (probably approximately) recent CV.

Working Preprints

1. Generation through the lens of learning theory
   with Ambuj Tewari
   Preprint, 2024

2. The Complexity of Sequential Prediction in Dynamical Systems
   with Unique Subedi, Ambuj Tewari
   Preprint, 2024

In Submission

1. Faster Rates for Private Adversarial Bandits
   with Hilal Asi, Kunal Talwar
   In Submission, 2024
2. A Unified Theory of Supervised Online Learnability
   with Unique Subedi, Ambuj Tewari
   In Submission, 2024

Publications

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

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

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

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

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

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

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

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

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

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

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

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

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

Other

1. Probabilistically Robust PAC Learning
   with Unique Subedi, Ambuj Tewari
   Conference on Neural Information Processing Systems (NeurIPS, ML Safety Workshop), 2022

2. Online Boosting for Multilabel Ranking with Top-k Feedback
   with Daniel Zhang, Young Hun Jung, Ambuj Tewari
   Preprint, 2020

Talks

• 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

• 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)

Mentoring

• Tiffany Parise (MS ECE): Fairness via Robust Machine Learning. 2022 - Present.

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.