Nihal Nayak

headshot_nihal_2024.jpg

One-Page Resume | CV (Updated in Jan 2025)

I am a PostDoc at Harvard University (SEAS) working with David Alvarez-Melis. My research involves identifying the limitations of various components in intelligent systems, such as large language models (LLMs), and improving them with data-centric solutions.

I completed my Ph.D. in Computer Science at Brown University where I worked with Stephen Bach. During my Ph.D., I focused on building zero-shot systems, a class of intelligent systems that generalize to new classes, tasks, and environments without human annotations. I introduced principled approaches to building and evaluating these systems through synthetic datasets (Bonito), composition (CSP, CLIP Binding), and structured knowledge (ZSL-KG).

Email : nnayak [at] seas [dot] harvard [dot] edu

news

Jul 8, 2025 Our work on pre-training foundation models in Academia was accepted to COLM 2025.
Jun 1, 2025 Started a new position as a Postdoctoral Fellow at Harvard University (SEAS).
May 16, 2025 Our work on predicting unobserved drug interactions using graph paths with large language models was accepted to KDD 2025.
Apr 25, 2025 Invited talks at Ai2 (Video), Netflix, and Snowflake on Data-Centric Approaches to Adapting Foundation Models.

selected publications

  1. Findings
    Learning to Generate Instruction Tuning Datasets for Zero-Shot Task Adaptation
    Nihal V. Nayak, Yiyang Nan, Avi Trost, and Stephen H. Bach
    In Findings of the Association for Computational Linguistics: ACL 2024 2024
  2. Findings
    Does CLIP Bind Concepts? Probing Compositionality in Large Image Models
    Martha Lewis,  Nihal V. Nayak Peilin Yu, Qinan Yu, Jack Merullo,  Stephen H. Bach, and Ellie Pavlick
    In Findings of the Association for Computational Linguistics: EACL 2024 2024
  3. ICLR
    Learning to Compose Soft Prompts for Compositional Zero-Shot Learning
    Nihal V. Nayak Peilin Yu, and Stephen H. Bach
    In International Conference on Learning Representations (ICLR) 2023
  4. TMLR
    Zero-Shot Learning with Common Sense Knowledge Graphs
    Nihal V. Nayak, and Stephen H. Bach
    Transactions on Machine Learning Research 2022
  5. ICLR
    Multitask Prompted Training Enables Zero-Shot Task Generalization
    Victor Sanh, Albert Webson, Colin Raffel,  Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani,  Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Trishala Neeraj, Jos Rozen, Abheesht Sharma, Andrea Santilli, Thibault Fevry, Jason Alan Fries, Ryan Teehan, Stella Biderman, Leo Gao, Tali Bers, Thomas Wolf, and Alexander M. Rush
    In International Conference on Learning Representations (ICLR) 2022