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Atharv Naphade I am an undergrad studying Computer Science (AI track) at Carnegie Mellon University, where I maintain a 4.00 GPA. My research spans large language models, AI safety, post-training methods, and continual learning. I have published work at venues including ICLR and Nature Scientific Reports, and I am currently a Research Fellow at SPAR working on AI safety and jailbreaks, and a researcher in the Machine Learning Department at CMU. Previously, I interned as a Research Scientist at the CMU Robotics Department (scaling RL post-training for vision-language models in collaboration with NVIDIA), and as a Research Engineer at Refactor (YC S24). Email / GitHub / LinkedIn / Twitter / X / CV |
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ResearchMy work focuses on understanding and improving large language models: their introspective capabilities, decision-making dynamics under uncertainty, and post-training alignment. I am especially interested in AI safety and making reasoning models more reliable. |
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Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering
Atharv Naphade ACL 2026 |
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Me, Myself, and π: Evaluating and Explaining LLM Introspection
Atharv Naphade, Samarth Bhargav, Sean Lim, McNair Shah ICLR 2026 HCAIR Workshop |
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Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors
Abhishek M. Dedhe, Aakash A. Chowkase, Niramay V. Gogate, Manas M. Kshirsagar, Rohan Naphade, Atharv Naphade, et al. Nature Scientific Reports, 2024 |
Experience |
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Software Engineer Intern — Roblox
Summer 2026 (Incoming) |
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Jane Street FTTP
Spring 2026 Highly selective 1-week Trading and Technology Program. 1 of 60 invitees out of thousands of applicants. |
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Research Fellow — SPAR (sparai.org)
Spring 2026 Working on jailbreaks for the AI Safety stream. |
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Research Scientist Intern — CMU Robotics Department
Fall 2025 Scaling up RL post-training of Vision Language Models. Collaboration with NVIDIA Researchers. |
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Research Engineer — Refactor (YCombinator S24)
Summer 2025 Improved robustness of Lowe’s AI at scale by deploying novel RLVR environments. Implemented 11+ full-stack infrastructure features in SQL, Redis, and Next.js for scalable LLM evaluation including multi-turn evals, error tracking & mitigation, and efficient guardrails. First hire. |
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Machine Learning Engineer — Iowa State University
2024 Built video-based deep learning models to detect and report risky driving behaviors in real-time using PyTorch & DeepStream. Algorithm deployed on 260+ highway cameras under Professor Anuj Sharma. |
Awards & Honors
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OutreachI create educational content explaining AI research for a general audience on social media (@agi_atharv). 17k followers, 1M+ views. |
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Template adapted from Jon Barron. |