I am a Research Scientist at Salesforce AI Research in Palo Alto, where I work with Dr. Shafiq Joty. Language models now produce fluent answers faster than anyone can check them, so most of my work is on verification: judging a model’s reasoning step by step rather than taking its conclusions on trust.
Hard2Verify, our ACL 2026 paper, asks whether verifiers can find the faulty step in a solution to an open-ended mathematics problem instead of only grading its final answer. SFR-DeepResearch uses reinforcement learning to train a single agent that reasons and searches on its own, and a related project synthesizes training data for web agents of progressively increasing difficulty. A second thread makes mixture-of-experts models cheaper to train, most recently in Least-Loaded Expert Parallelism at ICML 2026.
I started out at BITS Pilani, Goa Campus, where I studied computer science and led the Society for Artificial Intelligence and Deep Learning. From 2021 I worked with Ameet Deshpande and Karthik Narasimhan at Princeton NLP on data augmentation for low-resource languages, and the next summer built a gesture detector for TV news at Red Hen Lab through Google Summer of Code. My undergraduate thesis, at Microsoft Research India with Dr. Sunayana Sitaram, measured what compressing a multilingual model costs in fairness; that work became an ACL 2023 paper.
At the University of Texas at Austin I completed an M.S. in Computer Science, advised by Dr. Greg Durrett and Dr. Ying Ding. My thesis research in the AI Health Lab produced MedHallu, an EMNLP 2025 benchmark for detecting medical hallucinations, and Teaching with Lies, which trains compact hallucination detectors; alongside it I worked with Dr. Durrett on repairing model-written programs and on CodeUpdateArena, and spent the summer of 2024 at Salesforce on SFR-RAG. After graduating in 2025 I returned to Salesforce — and to the question of telling correct reasoning from convincing reasoning.
Education
- University of Texas at Austin M.S. in Computer Science · advised by Greg Durrett and Ying Ding 2023 – 2025
- BITS Pilani, Goa Campus B.E. in Computer Science, minor in Data Science · thesis at Microsoft Research India 2019 – 2023
News
- Aug 2026New preprint: Privileged Likelihood Is Not Automatically Value — token-credit checks for on-policy self-distillation.
- Jul 2026New preprint: Mixture-of-Parallelisms — a memory-efficient training stack for trillion-parameter MoE models.
- Jul 2026Hard2Verify appears at ACL 2026 as a main-conference long paper.
- May 2026Least-Loaded Expert Parallelism is accepted at ICML 2026.
- Oct 2025Two new preprints: Hard2Verify, a step-level verification benchmark for frontier math, and Synthesizing Agentic Data for Web Agents.
- Oct 2025Spoke at Dreamforce 2025: “How to Build Flexible Deep Research Agents” — recording on YouTube.
- Sep 2025New technical report: SFR-DeepResearch — reinforcement learning for autonomously reasoning single agents.
- Aug 2025MedHallu is accepted at EMNLP 2025 (main conference).
- Jun 2025New preprint: EgoVLM — policy optimization for egocentric video understanding.
- May 2025Joined Salesforce AI Research as a Research Scientist, and graduated with an M.S. in Computer Science from UT Austin.
- May 2025New preprint: Teaching with Lies — curriculum DPO on synthetic negatives for hallucination detection.
Older news
- Feb 2025New preprint: MedHallu — a comprehensive benchmark for detecting medical hallucinations in LLMs.
- Jan 2025FaithEval is accepted at ICLR 2025.
- Sep 2024Technical report: SFR-RAG — towards contextually faithful LLMs.
- Jun 2024Joined Salesforce AI Research in Palo Alto as a research intern.
- May 2024AdaPT is accepted at NAACL 2024 (Findings).
- Aug 2023Started my M.S. in Computer Science at UT Austin.
- May 2023Our study on model compression and fairness is accepted at ACL 2023.
- Jan 2023Selected for Google Research Week; started a project at APPCAIR Lab in collaboration with TCS Research.
- Jul 2022Joined Microsoft Research, Bengaluru as a research intern.
- May 2022CIAug is accepted at NAACL 2022 and DMix at ACL 2022.
- May 2022Selected as a Google Summer of Code contributor with Red Hen Lab.
Publications
Also on Google Scholar.
2026
2025
2024
2023
2022
2020
Experience
Salesforce AI Research, Palo Alto, CA
May 2025 – presentResearch Scientist
- Research on reasoning and reliability in LLMs: step-level verification of mathematical reasoning (Hard2Verify, ACL 2026), autonomous deep-research agents (SFR-DeepResearch), and agentic data synthesis for web agents.
- Work on efficient large-scale training and inference of mixture-of-experts models (Least-Loaded Expert Parallelism, ICML 2026; Mixture-of-Parallelisms).
- Presented “How to Build Flexible Deep Research Agents” at Dreamforce 2025, and named on U.S. Patent Application 19/043,190, “Systems and Methods for Building Artificial Intelligence Agents.”
AI Health Lab, University of Texas at Austin
Aug 2023 – May 2025Graduate Researcher · advised by Dr. Ying Ding and Dr. Greg Durrett
- Introduced MedHallu (EMNLP 2025), the first benchmark designed specifically for detecting medical hallucinations in LLMs, built on 10,000 curated question–answer pairs with stratified difficulty levels.
- Showed that hallucination detection remains difficult for semantically subtle cases, and that domain knowledge and an explicit “not sure” option substantially improve detection.
- Developed Teaching with Lies, which trains hallucination detectors on synthetic negative examples using curriculum direct preference optimization.
- With Dr. Greg Durrett, worked on efficient debugging of LLM-generated programs using feedback and error traces, and co-developed CodeUpdateArena, a benchmark for knowledge editing in code LLMs under synthetic API updates.
Salesforce AI Research, Palo Alto, CA
Jun 2024 – Aug 2024Research Intern
- Worked with Dr. Shafiq Joty on SFR-RAG, a 9B retrieval-augmented LLM that outperformed models 10× its size on RAG benchmarks at the time of release.
- Curated a faithfulness-focused evaluation set that tests whether a model stays grounded in the context it is given, later released as part of FaithEval (ICLR 2025).
- Developed a “thought / observation” strategy for SFR-RAG that significantly improved multi-hop question answering.
Microsoft Research, Bengaluru, India
Jul 2022 – Jan 2023Research Intern
- Worked with Dr. Sunayana Sitaram on compressing large language models with adapters, balancing computational efficiency against bias.
- Evaluated the zero-shot performance of compressed massive multilingual models, and deployed compressed variants for downstream use.
- Studied the effect of compression on fairness with both intrinsic and extrinsic measures (ACL 2023).
Google Summer of Code, Red Hen Lab
Jun 2022 – Sep 2022Contributor
- Built an end-to-end multimodal vision transformer to detect hand gestures co-occurring with time expressions in TV news, supporting accessible captioning.
- Framed the task as classification of body-keypoint trajectories, as part of Red Hen Lab’s work on multimodal analysis of TV news.
- Wrote up the approach and results in a blog post.
Princeton NLP
Jun 2021 – May 2022Research Collaborator
- Worked with Ameet Deshpande and Karthik Narasimhan on interpolative data augmentation (MixUp) for low-resource multilingual NLP.
- Studied whether interpolating between training examples improves transformer models on languages with little labeled data.