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I am a fourth-year Computer Science Ph.D. student at Purdue University, advised by Prof. Raymond A. Yeh. I hold a Master of Science in Computer Science degree from University of Illinois at Urbana-Champaign, where I worked with Mr. Yunze Man and Prof. Liangyan Gui . I received my B.S.E. degree in Data Science at University of Michigan with a minor in Mathematics. I was fortunate to work with Dr. Junming Zhang and Prof. Matthew Johnson-Roberson at the UM Ford Center for Autonomous Vehicles (FCAV). I also hold a B.E. degree in Electrical and Computer Engineering at Shanghai Jiao Tong University (SJTU) (Dual Degree Program). Email / CV / Google Scholar / Github / Twitter |
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| May, 2026 | Returned to Mitsubishi Electric Research Laboratories as a research intern in Summer 2026. |
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| May, 2026 | Selected as one of the Gold Reviewers of ICML 2026. |
| May, 2025 | Joined Mitsubishi Electric Research Laboratories as a research intern in Summer 2025. |
| Nov, 2024 | Selected as one of the Top Reviewers of NeurIPS 2024. |
| Sep, 2024 | D-LISA accepted to NeurIPS 2024. |
| Aug, 2023 | Started as a Computer Science Ph.D. student at Purdue University. |
| Feb, 2023 | HyperPC accepted to CVPR 2023. |
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I am broadly interested in computer vision, machine learning, and robotics, with a focus on 3D spatial intelligence, multimodal foundation models, and self-improving learning. My research aims to develop generalizable visual perception systems that can learn and adapt with minimal human supervision. My long-term goal is to advance visual perception and reasoning for real-world embodied agents and autonomous systems, enabling them to operate effectively in complex and dynamic environments. |
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Haomeng Zhang, Raymond A. Yeh arXiv preprint, 2026 arXiv We study Zero-shot 2D Grounding with Novel Affordance Types, where models localize affordances unseen during training. We introduce new benchmarks and propose AffordAnything and AffordAnything+ to generalize affordance grounding to novel affordance types. |
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Haomeng Zhang, Kuan-Chuan Peng, Suhas Lohit, Raymond A. Yeh arXiv preprint, 2025 arXiv We propose to study Auto-Vocabulary 3D Object Detection (AV3DOD), where the classes are automatically generated for the detected objects without any user input. |
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Haomeng Zhang, Chiao-An Yang, Raymond A. Yeh NeurIPS 2024 arXiv / Project Page / Code Our proposed model D-LISA has a novel vision module that allows for a dynamic number of proposal boxes and extracts features from dynamic viewpoints per scene. Furthermore, we propose a fusion module that is spatially aware with explicit language conditioning. |
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Junming Zhang, Haomeng Zhang, Ram Vasudevan, Matthew Johnson-Roberson CVPR 2023 arXiv / Project Page / Code We propose a hyperspherical module which could be inserted into any existing encoder-decoder structure and consistently improve the point cloud completion result in both single-task and multi-task learning. |
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Conference Reviewer: CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML.
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