Kyungmin Kim
kyungk7 [at] uci.edu
kyungk7 [at] uci.edu
Welcome! I’m a Ph.D. candidate in the Intelligent Dynamics Lab at the University of California, Irvine, mentored by Prof. Roy Fox. My research bridges the exciting worlds of Reinforcement Learning and Computer Vision, focusing on building intelligent systems that can learn and perform visual control tasks.
In the fall of 2019, I had the incredible opportunity to intern with the Mobile Vision team at Facebook Reality Labs, collaborating with Peter Vajda on cutting-edge projects that push the boundaries of what’s possible in AR/VR.
I’m driven by a passion for innovation and a belief in the transformative potential of AI to solve complex, real-world problems. Let’s explore this journey together!
CV | Google Scholar | Github
[06/2025] Start working at Zoox as ML intern
[09/2021] Joined Indylab @UCI as a Ph.D. student.
[05/2020] Start working as a contingent worker for research collaboration at Facebook.
[09/2019] Off to Facebook @ Menlo Park, CA for internship.
Sep. 2021 - Present Ph.D. in Computer Science, University of California, Irvine (GPA: 4.0/4.0)
Mar. 2017 - Feb. 2022 M.S. in Computer Science, Yonsei University (GPA: 4.3/4.3)
Mar. 2013 - Feb. 2017 B.S. in Computer Science and Engineering, Yonsei University (GPA: 4.2/4.3, Rank: 2/91)
June 2025 - September 2025 Machine Learning Intern, Zoox @ Foster City, CA, USA
May 2020 - July 2021 Contingent Worker for Research Collaboration, Facebook
Sep. 2019 - Jan. 2020 Research Intern, Facebook @ Menlo Park, CA, USA
Jun. 2016 - Dec. 2016 (Part-time) Research Intern, Korea Institute of Science and Technology (KIST)
Adapting World Models with Latent-State Dynamics Residuals
JB Lanier, Kyungmin Kim, Armin Karamzade, Yifei Liu, Ankita sinha, Kathleen He, Davide Corsi, Roy FoxUnder review for a conference[Paper]Model-Based Reinforcement Learning under Random Observation Delays
Armin Karamzade, Kyungmin Kim, JB Lanier, Davide Corsi, Roy FoxUnder review for a conference[Paper]Realizable Continuous-Space Shields for Safe Reinforcement Learning
Kyungmin Kim*, Davide Corsi*, Andoni Rodriguez*, JB Lanier, Benjami Parellada, Pierre Baldi, César Sánchez, Roy FoxL4DC 2025[Paper]Make the Pertinent Salient: Task-Relevant Reconstruction for Visual Control with Distractions
Kyungmin Kim, Charless Fowlkes, Roy FoxRLC 2025RLC Workshop (TAFM) 2024[Paper]Reinforcement Learning from Delayed Observations via World Models
Armin Karamzade, Kyungmin Kim, Montek Kalsi, Roy FoxRLC 2024[Paper] [Code]Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Kolby Nottingham, Yasaman Razeghi, Kyungmin Kim, JB Lanier, Pierre Baldi, Roy Fox, Sameer SinghNAACL 2024[Paper] [Project Page]An Investigation on Hardware-Aware Vision Transformer Scaling
Chaojian Li, Kyungmin Kim, Bichen Wu, Peizhao Zhang, Hang Zhang, Xiaoliang Dai, Peter Vajda, Yingyan LinACM Transactions on Embedded Computing Systems 2024[Paper]CAG-QIL: Context-Aware Actionness Grouping via Q Imitation Learning for Online Temporal Action Localization
Hyolim Kang, Kyungmin Kim, Yumin Ko, Seon Joo KimICCV 2021[Paper] [Code]Rethinking the Self-Attention in Vision Transformers
Kyungmin Kim, Bichen Wu, Xiaoliang Dai, Peizhao Zhang, Zhicheng Yan, Peter Vajda, Seon Joo KimCVPR Workshop (ECV) 2021[Paper]Winning the CVPR’2021 Kinetics-GEBD Challenge: Contrastive Learning Approach
Hyolim Kang, Jinwoo Kim, Kyungmin Kim, Taehyun Kim, Seon Joo KimCVPR Workshop (LOVEU) 2021Ranked #1[Paper] [Code]Teaching machines to understand baseball games: Large-scale baseball video database for multiple video understanding tasks
Kyungmin Kim*, Minho Shim*, Young Hwi Kim*, Seon Joo KimECCV 2018[Paper] [Project Page]Last updated on October. 13th, 2025