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Huan-ang Gao (高焕昂)

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Education

Bachelor of Engineering

2020 - 2024 | Computer Science | Tsinghua University

GPA 3.98 / 4.00. Ranked #1 out of 204 in the Department of CS.

Proud to have received the highest distinction for undergraduates at Tsinghua in 2023 and the SenseTime Scholarship in 2024.

Ph.D. Student

2024 - Present | Computer Science | Tsinghua University

Advisor: Prof. Ya-Qin Zhang, Dean of Institute for AI Industry Research (AIR), THU.

Research Interests

Let’s build LLM-based agents that free humanity from hands-on manual interaction, moving toward a minds-off future where agents operate completely independently! Most recently, I am working with SIALab on building agents that set our mind free for daily tasks. Topics I am interested in:

If you’re interested in related topics and would like to collaborate, feel free to reach out with my email (which you can find in the hyperlink on the right panel).

Research Experience

Generative Simulation for Embodied AI

Problem Identification

The development and iteration of autonomous driving and robotics policies are limited by the high costs, low efficiency, and safety risks of real-world testing. The rise of Generative AI offers a potential breakthrough by enabling high-fidelity, interactive, and editable simulation testing.

Technical Approach

My research focused on building "World Models" to drive simulation with generative methods. Technically, I explored two core directions: 1) High-fidelity Scene Reconstruction: Building "digital twins" of real scenes using technologies like NeRF or Gaussian splatting. 2) Controllable Content Generation: On the basis of reconstructed scenes, leveraging the generative priors of diffusion models to provide endless, controllable scene variations and edge cases.

Selected Publications

indicates first or co-first author.
  • PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model, CVPR 2025.
  • Ctrl-U: Robust Conditional Image Generation Via Uncertainty-aware Reward Modeling, ICLR 2025.
  • SCP-Diff: Spatial-Categorical Joint Prior for Diffusion Based Semantic Image Synthesis, ECCV 2024.
Data Efficient Scene Parsing

Problem Identification

2D/3D perception is fundamental to embodied intelligence, but the extremely high cost of data annotation severely restricts the development of perception models.

Technical Approach

My early research focused on data-efficient perception learning algorithms, particularly semi-supervised learning and domain adaptation. In my first ICCV paper, DQS3D, I proposed a single-stage, densely-matched semi-supervised learning framework for 3D object detection, addressing the issue of insufficient training signals caused by sparse matching in previous methods. I also explored various levels of perception tasks such as self-supervised depth estimation, indoor layout estimation, and HD map generation, mastering task-oriented neural network and representation design methods.

Publications

indicates first or co-first author.
  • DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection, ICCV 2023.
  • From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds, ICRA 2023.
  • Training-Free Model Merging for Multi-target Domain Adaptation, ECCV 2024.

Services

Co-Founder @ Lumina-Embodied.AI (2025.4-Now)
  • Building community for embodied AI research and applications
  • Bridging academic research with industry implementations
  • Focus on AI systems that learn through physical interaction
Reviewer @ Academic Conferences & Journals
  • CVPR (2025), ICCV (2025), WACV (2024), 3DV (2025), TPAMI
  • NeurIPS (2025), ICLR (2025)
  • ICRA (2025), IROS (2024, 2025), CoRL (2025)
  • AAAI (2024), ICME (2025)
Teaching Assistant @ CS, THU
  • (30240163) Software Engineering. Compulsory course in CS, THU. (23Spring, 23Fall, 24Spring, 24Fall, 25Spring, 25Fall)
  • (30240551) Digital Logic Experimentation. Compulsory course in CS, THU. (24Spring, 25Spring)
  • (40240354) Computer Organization and Design. Compulsory course in CS, THU. (23Fall)
清华大学计算机系 科创辅导员 (2024.9-Now)
  • Technical training & competition guidance for undergraduates
  • Research & internship opportunity integration
清华大学计算机系 学生科协主席 (2023.5-2024.6)