Stock Price Prediction using Bi-Directional LSTM based Sequence to Sequence Modeling and Multitask Learning. - Siddartha Mootha; Sashank Sridhar; Rahul Seetharaman; S. Chitrakala (UEMCON, 2020)

Singularity: Planet-Scale, Preemptive and Elastic Scheduling of AI Workloads. - Dharma Shukla; Muthian Sivathanu; Srinidhi Viswanatha; Bhargav S. Gulavani; Rimma Nehme; Amey Agrawal; Chen Chen; Nipun Kwatra; Ramachandran Ramjee; Pankaj Sharma; Atul Katiyar; Vipul Modi; et al. (CoRR, 2022)

From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries. - Hitesh Wadhwa; Rahul Seetharaman; Somyaa Aggarwal; Reshmi Ghosh; Samyadeep Basu; Soundararajan Srinivasan; Wenlong Zhao 001; Shreyas Chaudhari; Ehsan Aghazadeh (CoRR, 2024)

Quantifying reliance on external information over parametric knowledge during Retrieval Augmented Generation (RAG) using mechanistic analysis. - Reshmi Ghosh; Rahul Seetharaman; Hitesh Wadhwa; Somyaa Aggarwal; Samyadeep Basu; Soundararajan Srinivasan; Wenlong Zhao 001; Shreyas Chaudhari; Ehsan Aghazadeh (CoRR, 2024)

InsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking. - Rahul Seetharaman; Kaustubh D. Dhole; Aman Bansal (CoRR, 2025)

"Lost-in-the-Later": Framework for Quantifying Contextual Grounding in Large Language Models. - Yufei Tao 004; Adam Hiatt; Rahul Seetharaman; Ameeta Agrawal (CoRR, 2025)