Hyunin Lee

Ph.D at UC Berkeley

Contact: hyunin(at)berkeley(dot)edu

Research Interests: Reinforcment Learning

About

I am Hyunin Lee, a fourth-year Ph.D. student at UC Berkeley. I am interested in developing reinforcement learning algorithms and exploring their key concepts. I am fortunate to work with Somayeh Sojoudi. Prior to joining Berkeley, I received my B.S. degree from Seoul National University where I have worked on research projects within neuroscience and machine learning with Yong-Lae Park.

Besides my research, I am interested in AI education 📚👩🏻‍💻. I cofound and currently advise OUTTA.

Research Area

• Reinforcement Learning, Multi-Modal Learning

Blog

• An Orthogonal Alignment Phenomenon in Cross-Attention

News

• [2025.10] New paper on Multi-Modal Learning (work done during Meta internship): Orthogonal Alignment and its blog post: An Orthogonal Alignment Phenomenon in Cross-Attention
• [2025.06] Extension of Black swan paper: Antifragile will be presented in ICML2025.
• [2025.05] Joining Meta this summer as a research scientist intern (Ranking team)!
• [2025.04] Black Swan Hypothesis 🦢 as oral presentation at ICLR25 workshop: Financial AI
• [2025.04] Invited to AI safety panel at Appen
• [2025.03] Started research associate with OpenAI
• [2025.02] New paper on proposing the necessity of antifragility in AI safety : Antifragile
• [2024.02] Serving as a reviewer for ICML, RLC, and Neurips 2025.
• [2025.01] New paper proposing a novel AI safety perspective, Black Swan Hypothesis 🦢, in ICLR2025 (& workshop: Financial AI 25): A Black Swan Hypothesis: The Role of Human Irrationality in AI Safety
• [2024.09] Serving as a reviewer for ICLR, AISTATS 2025.
• [2024.07] Attending RLC.
• [2024.07] Serving on the program chair committee for AAAI 2025.
  
• [2024.05] New paper on Safe reinforcement Learning: Policy-based Primal-Dual Methods for Concave CMDP with Variance Reduction
• [2024.05] New paper on optimal early stopping of non-stationary Reinforcement Learning in ICML 2024 (Oral, top 1%): Pausing Policy Learning in Non-stationary Reinforcement Learning. [Talk/codes]
• [2024.04] Received a Berkeley research fellowship.
• [2024.03] Served as a reviewer for ICML 2024.
• [2024.02] New paper on proposing new policy gradient estimator in reinforcement Learning in IEEE TAC: Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization.
• [2023.10] Received a NeurIPS scholar award
• [2023.08] OUTTA has launched a new spinoff team, AI PLAYGROUND, dedicated to creating intuitive AI educational materials for elementary education! I lead the team.
• [2023.05] New paper on proposing Tempo in Non-stationary Reinforcement Learning in NeurIPS 2023: Tempo Adaption in Non-stationary Reinforcement Learning. [slides] [codes]
• [2023.01] New paper on Causal Machine Learning in CDC 2023: Initial State Interventions for Deconfounded Imitation Learning.
• [2022.08] I started Ph.D. at UC Berkeley.
• [2022.05] Received the Kwanjeong Education Foundation Scholarship.
• [2022.03] New paper on Neuroscience and Machine Learning in IEEE TNSRE: Explainable Deep Learning Model for EMG-Based Finger Angle Estimation using Attention. [slides] [videos] [codes]
• [2022] I have co-founded 🚀OUTTA🚀 with Haeun (MIT), Chankyo (UMich)

Education

• Ph.D., University of California, Berkeley, 2022.08 - current
• B.S., Seoul National University, 2015.03 - 2022.02
  (Served military service in Korean Combat Traning Center, 2017.07 - 2019.03)