My research spans reinforcement learning (RL), LLM alignment, and robust machine learning. I study how to train models that learn efficiently from feedback and remain reliable when that feedback is imperfect. My work combines theoretical foundations with empirical LLM research and industry experience applying ML to robotics, conversational AI, and personalization.
I recently completed my PhD in the ECE Department at the University of Washington, advised by Prof. Maryam Fazel and Prof. Lillian Ratliff. During my PhD, I also collaborated closely with Prof. Jeff Bilmes and Prof. Kevin Jamieson. I am on the industry job market.
Prior to joining UW, I completed my master’s degree in 2020 in the BLISS Lab in the EECS Department at UC Berkeley, where I was advised by Prof. Anant Sahai. I completed my undergraduate studies at UC Berkeley with a major in EECS and a minor in Theater, Dance & Performance Studies.
* Equal contribution.
A. Narang*, A. Tajdini*, C. Zhang*, J. Morgenstern. “Defense via Consensus: Mitigating Subliminal Learning through Multi-Teacher Distillation.” Preprint, 2026. Adv-ML Frontiers Workshop COLM 2026; AI4Good Workshop NeurIPS 2026. (arXiv) (code)
M. Williams*, M. Carroll*, A. Narang, C. Weisser, B. Murphy, A. Dragan. “On Targeted Manipulation and Deception when Optimizing LLMs for User Feedback.” ICLR 2025 (arXiv)
Press coverage: New York Times · Washington Post
A. Narang, S. Dean, L. Ratliff, M. Fazel. “Dynamics of Learning under User Choice: Overspecialization and Peer-Model Probing.” UAI 2026 (arXiv)
A. Narang, A. Wagenmaker, L. Ratliff, K. Jamieson. “Sample Complexity Reduction via Policy Difference Estimation in Tabular Reinforcement Learning.” NeurIPS 2024 (Spotlight) (arXiv)
A. Narang, E. Faulkner, D. Drusvyatskiy, M. Fazel, L. Ratliff. “Multiplayer Performative Prediction: Learning in Decision-Dependent Games.” JMLR 2023 (arXiv)
