Alok Shah

I'm a student at the University of Pennsylvania, where I study Computer Science, Mathematics, and Electrical Engineering.

Outside of class, I lead MLR@Penn, the undergraduate AI and ML research organization and community at Penn and serve on the Student Advisory Board for the Wharton AI & Analytics Initiative.

In my free time, I enjoy historical documentaries, geography trivia, trying new food trucks and restaurants around Philly, and burning off those calories by playing pickup soccer.

Email  /  CV  /  Twitter  /  Github

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Projects

I play simple tricks on machine learning models to study their training dynamics. I tend to interpret the phenomena which emerge from these experiments with a lens informed by theory. I'm interested in applying such insights to problems in robotics and language.

Dropout, Johnson-Lindenstrauss, Low-Rank Bias, and Generalization
Alok Shah*, Mohul Aggarwal*, Khush Gupta

CIS 6770 Final Project: An alternate perspective on the generalizing behavior of dropout through the lens of random projections and low-rank bias

Language Modeling With Learned Meta-Tokens
Alok Shah*, Khush Gupta*, Keshav Ramji, Pratik Chaudhari
ICML LCFM, 2025

Showed that meta-tokens provably improve context propagation in language models across extreme lengths via rate-distortion analysis and positional ablations

Investigating Language Model Dynamics using Meta-Tokens
Alok Shah*, Khush Gupta*, Keshav Ramji, Vedant Gaur
NeurIPS ATTRIB, 2024

ESE 5460 Final Project: Explored how to coerce communication between token-level checkpoints for more interpretable, capable models

Modeling Human Behavior Without Humans: Prospect Theoretic Multi-Agent RL
Sheyan Lalmohammed*, Khush Gupta*, Alok Shah*, Keshav Ramji,
ICML MAS, 2025

STAT 4830 Final Project: A cool way to induce human-like risk-averse behavior between multiple agents by warping the reward landscape in Choquet integrals in accordance with Cumulative Prospect Theory

Weak-to-Strong In-Context Optimization of Language Model Reasoning
Keshav Ramji*, Alok Shah*, Vedant Gaur*, Khush Gupta*
NeurIPS ATTRIB, 2024

Developed in-context optimization method leveraging weak learners to improve reasoning in strong large language models without additional finetuning

A Note on Multitask Learning with Task-Adaptive Priors
Alok Shah , Sidhant Srivastava, Tara Kapoor

CIS 7000 Final Project: By extracting task dependent statistics, we learn a prior that provably nudges the model toward representations that generalize better to new tasks.

Conformal Actor-Critic: Distriution-Free Uncertainty Quantification for Offline RL
Alok Shah, Nikhil Kumar, Khush Gupta, Mohul Aggarwal

CIS 6200 Final Project: Integrated conformal prediction into offline reinforcement learning, providing statistically robust uncertainty quantification to curb overestimation bias of Q-values

Deep Compression with Adversarial Robustness via Decision Boundary Smoothing
Alok Shah, Michael Shao

ESE 5390 Final Project: Smoothed adversarial retraining during compression produces compact models with stronger robustness

AlfLLM: Limitations on LLMs as Reward Function Surrogates
Alexander Kyimpopkin, Alok Shah, Dominic Olaguera-Delogu

ESE 6500 Final Project: Investigated the efficacy of using large language models as surrogate reward functions for reinforcement learning in the ALFWorld environment.

Selected Coursework

* denotes graduate level; ** denotes doctoral level

  • Honors Multivariate Analysis
  • Discrete Mathematics
  • Linguistics
  • Data Structures and Algorithms
  • Advanced Linear Algebra*
  • Probability*
  • Computer Systems
  • Machine Learning*
  • Real Analysis
  • Topology*
  • Differential Geometry*
  • Operating System and Design
  • Ethical Algorithm Design*
  • Convex Optimization**
  • State Estimation, Control, and Reinforcement Learning**
  • Deep Learning*
  • Theoretical Computer Science
  • Analysis of Algorithms*
  • Uncertainty Quantification**
  • Learning for Dynamics and Control**
  • Hardware/Software Co-Design for Machine Learning*
  • Stability in Optimization and Statistics**
  • Bayesian Optimization**
  • Randomized Algorithms and Numerical Linear Algebra**
  • Numerical Optimization for Data Science and Machine Learning
  • Statistical Topics in Large Language Models**

Teaching

I enjoy teaching and have served on staff for several courses.

* denotes graduate level; ** denotes doctoral level; ^ denotes head teaching assistant

  • Mathematics of Machine Learning (Eric Wong)
  • Machine Learning*^ (Surbhi Goel, Eric Wong, Jake Gardner, Lyle Ungar)
  • Statistics for Data Science** (Hamed Hassani)
  • Deep Learning* (Pratik Chaudhari)
  • State Estimation, Control, and Reinforcement Learning** (Pratik Chaudhari)
  • Convex Optimization** (Nikolai Matni)

Shamelessly forked from Jon Barron.