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.
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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
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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
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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
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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
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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
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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.
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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
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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
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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.
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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)
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