Anmol Kabra

anmol (at) cs.cornell.edu   •   CV   •   google scholar   •   github   •   linkedin   •   bsky   •   twitter

images/site/anmolkabra.jpg

I am a Computer Science PhD student at Cornell University, advised by Kilian Weinberger. I study reasoning and agentic capabilities in LLMs, with applications in AI for Science. My research has focused on reinforcement learning, multimodal models, and distribution shifts.

Previously, I was a Research Engineer at ASAPP working on LLMs, privacy, and anomaly detection with Ethan Elenberg and Kilian Weinberger. I was also an AI/ML Quant intern at Bloomberg working on quantitative trading strategies and LLMs with the AI Engineering team.

I received my Bachelors in Science in Computer Science and Applied Math from Cornell University in Spring 2020 and was named a Merrill Presidential Scholar. During my undergraduate term, I worked on research projects with Carla Gomes and Kilian Weinberger, and was generously supported by the Tata Scholarship and Telluride Scholarship. Before returning to Cornell for my PhD, I studied ML for decision-making at Toyota Technological Institute at Chicago (TTIC).


news


Jun 2025

2024

2023

2022

2021

2020

  • Joined ASAPP as a Research Engineer in Ithaca, NY.
  • Graduated from Cornell!
    • Recognized as a 2020 Merrill Presidential Scholar (top 1% of graduating class).
    • Received the 2020 Computer Science Prize for Academic Excellence (highest undergraduate honor in the CS department).

2019


selected papers


* equal contributions

  1. PhantomWiki: On-Demand Datasets for Reasoning and Retrieval Evaluation
    Albert Gong*Kamilė Stankevičiūtė*Chao Wan*Anmol Kabra*, Raphael Thesmar, Johann Lee, Julius Klenke, Carla P. Gomes,  and Kilian Q. Weinberger
    ICML (2025). (Oral presentation at ICML Workshop on Long Context Foundation Models).
  2. Learning from Synthetic Data Improves Multi-hop Reasoning
    Under Review (2025).
  3. Score Design for Multi-Criteria Incentivization
    Foundations of Responsible Computing (FORC) (2024).
  4. The Limitations of Model Retraining in the Face of Performativity
    Anmol Kabra*,  and Kumar Kshitij Patel*
    ICML Workshop on Humans, Algorithmic Decision-Making and Society (2024).
  5. AISciVision: A Framework for Specializing Large Multimodal Models in Scientific Image Classification
    Brendan HoganAnmol KabraFelipe Siqueira PachecoLaura GreenstreetJoshua FanAaron Ferber, Marta Ummus, Alecsander Brito, Olivia Graham, Lillian AokiDrew HarvellAlex Flecker,  and Carla Gomes
    Preprint (2024).
  6. Domain Private Transformers for Multi-Domain Dialog Systems
    Anmol Kabra,  and Ethan R. Elenberg
    Findings-EMNLP (2023).
  7. Exponential Family Model-Based Reinforcement Learning via Score Matching
    NeurIPS (2022). (Oral presentation).
  8. Characterizing the Loss Landscape in Non-Negative Matrix Factorization
    AAAI (2021).

on a less academic note, I like


(listed in increasing order of number of characters in each bullet)
  • biking
  • cooking
  • running
  • playing Table Tennis
  • following Formula 1 and motorsports
  • walking fast so that my legs heat up
  • reading books, newspapers, and research papers — mostly high-fantasy and non-fiction these days