I am a Ph.D. student in the Statistics Department at the University of Michigan advised by Prof. Ambuj Tewari. Previously, I studied Computer Science and Chemical Engineering also at UM where I worked with Prof. Mahdi Cheraghchi, Dr. Sindhu Kutty, and Prof. Andrej Lenert.

My research interests are broadly anything in Theoretical Machine Learning. Some specific areas of interest include: learning theory, online learning, boosting, bandits, differential privacy, and adversarial robustness.

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

Coming soon :)

In Submission

1. The Complexity of Sequential Prediction in Dynamical Systems
   with Unique Subedi, Ambuj Tewari
   In Submission, 2024

2. Apple Tasting: Combinatorial Dimensions and Minimax Rates
   with Ananth Raman, Unique Subedi, Ambuj Tewari
   In Submission, 2024

3. A Combinatorial Characterization of Supervised Online Learnability
   with Unique Subedi, Ambuj Tewari
   In Submission, 2024

4. Online Learning with Set-Valued Feedback
   with Unique Subedi, Ambuj Tewari
   In Submission, 2024

5. A Characterization of Multioutput Learnability
   with Unique Subedi, Ambuj Tewari
   In Submission, 2023

Publications

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

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

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

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

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

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

7. 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

• 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.