PhdFit
Resume-aware faculty matching

Find professors who actually fit you

Review faculty evidence in public, then use the workspace to turn your background into a shortlist, outreach, and meeting prep.

Profile-awarePaper evidenceSix agents
Bolei Zhou

Bolei Zhou

· Professor

University of California, Los Angeles · Computer Science

Active 1989–2026

h-index65
Citations38.0k
Papers260151 last 5y
Funding$245k1 active

Academic metrics are sourced from OpenAlex and public funding records; values may differ from Google Scholar.

Faculty page
See your match with Bolei Zhou — sign in to PhdFit.Sign in

About

Bolei Zhou is an Associate Professor in the Department of Computer Science at UCLA Samueli School of Engineering. His research focuses on computer vision, machine learning, and artificial intelligence. He has been recognized with notable awards including the NSF CAREER Award in 2024, Intel's Rising Star Faculty Award in 2023, and was named one of MIT Technology Review’s Innovators Under 35 (Asia-Pacific) in 2020. Dr. Zhou earned his PhD from MIT in 2018. His work has led to leadership roles such as leading the new Physical AI Research Lab at Coco Robotics, and he has been featured in the news for his contributions to AI learning and human-like driving capabilities.

Research topics

  • Artificial Intelligence
  • Computer Science
  • Machine Learning
  • Programming language
  • Sociology
  • Social Science
  • Theoretical computer science
  • Natural Language Processing
  • Engineering ethics
  • Cognitive science

Selected publications

  • Next Steps for Human-Centered Generative AI: A Technical Perspective

    arXiv (Cornell University) · 2023 · 14 citations

    Senior authorCorresponding

    Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays out future directions of Generative AI spanning three levels: aligning with human values; assimilating human intents; and augmenting human abilities. By identifying these next-steps, we intend to draw interdisciplinary research teams to pursue a coherent set of emergent ideas in HGAI, focusing on their interested topi…

    PublisherOA PDFDOI

  • Vid2Sim: Realistic and Interactive Simulation from Video for Urban Navigation

    2025-06-10 · 7 citations

    articleSenior author

    Sim-to-real gap has long posed a significant challenge for robot learning in simulation, preventing the deployment of learned models in the real world. Previous work has primarily focused on domain randomization and system identification to mitigate this gap. However, these methods are often limited by the realism of the simulator and graphics engines renderings. In this work, we propose Vid2Sim, a novel framework that effectively bridges the sim2real gap through a scalable and cost-efficient re…

    PublisherDOI

  • Embodied Scene Understanding for Vision Language Models via MetaVQA

    2025-06-10 · 2 citations

    articleSenior author

    Vision Language Models (VLMs) demonstrate significant potential as embodied AI agents for various mobility applications. However, a standardized, closed-loop benchmark for evaluating their spatial reasoning and sequential decision-making capabilities is lacking. To address this, we present MetaVQA: a comprehensive benchmark designed to assess and enhance VLMs’ understanding of spatial relationships and scene dynamics through Visual Question Answering (VQA) and closed-loop simulations. MetaVQA le…

    PublisherDOI

  • CooPre: Cooperative Pretraining for V2X Cooperative Perception

    2025-10-19 · 1 citations

    article

    Existing Vehicle-to-Everything (V2X) cooperative perception methods rely on accurate multi-agent 3D annotations. Nevertheless, it is time-consuming and expensive to collect and annotate real-world data, especially for V2X systems. In this paper, we present a self-supervised learning framwork for V2X cooperative perception, which utilizes the vast amount of unlabeled 3D V2X data to enhance the perception performance. Specifically, multi-agent sensing information is aggregated to form a holistic v…

    PublisherDOI

  • Learning from Active Human Involvement through Proxy Value Propagation

    ArXiv.org · 2025-02-05 · 1 citations

    preprintOpen accessSenior author

    Learning from active human involvement enables the human subject to actively intervene and demonstrate to the AI agent during training. The interaction and corrective feedback from human brings safety and AI alignment to the learning process. In this work, we propose a new reward-free active human involvement method called Proxy Value Propagation for policy optimization. Our key insight is that a proxy value function can be designed to express human intents, wherein state-action pairs in the hum…

    PublisherOA PDFDOI

Recent grants

Frequent coauthors

  • Yujun Shen

    40 shared

  • Yinghao Xu

    38 shared

  • Antonio Torralba

    38 shared

  • Ceyuan Yang

    38 shared

  • Aude Oliva

    Massachusetts Institute of Technology

    25 shared

  • Zhenghao Peng

    Heilongjiang Bayi Agricultural University

    22 shared

  • David Bau

    19 shared

  • Xiaogang Wang

    Harbin Institute of Technology

    19 shared

Education

  • PhD, EECS

    Massachusetts Institute of Technology

    2018

  • M.Phil, Information Engineering

    Chinese University of Hong Kong

    2012

  • Bachelor of Engineering, Biomedical Engineering

    Shanghai Jiao Tong University

    2010

Awards & honors

  • NSF CAREER Award 2024
  • Intel's Rising Star Faculty Award 2023
  • MIT Technology Review’s Innovators Under 35 (Asia-Pacific) 2…

Similar researchers at University of California, Los Angeles

  • Resume-aware match score
  • Save to shortlist
  • AI-drafted outreach

See your match with Bolei Zhou

PhdFit ranks faculty by your research interests, methods, and publications — grounded in their actual work, not templates.

Sign in to PhdFitHow it works
  • Free to start
  • No credit card
  • 30-second signup