cv
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Basics
| Name | Yun Zhang |
| yun666@g.ucla.edu | |
| Url | https://HandsomeYun.github.io/ |
Education
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2025.09 - Present United States
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2021.09 - 2025.06 United States
Undergraduate
University of California, Los Angeles (UCLA)
B.S. in Mathematics in Computer Science, B.S. in Statistics and Data Science
- Cumulative GPA: 3.823/4.0
- Dean's Honors List (Fall 2021, Winter/Spring/Fall 2022, Winter/Spring 2023)
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2015.09 - 2021.06 Athens, Greece
High School
American Community School of Athens (ACS Athens)
- Weighted Cumulative GPA: 4.886/4.0
- Final IB Score: 44/45
Publications
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2026.03.09 TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments
International Conference on Machine Learning (ICML)
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2025.09.01 MIC-BEV: Multi-Infrastructure Camera Bird's-Eye-View Transformer with Relation-Aware Fusion for 3D Object Detection
Under Review, Best Paper Award for DriveX Workshop at ICCV 2025
Research
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2024.05 - 2024.08 Researcher
HKU Summer Research Program
Leveraged Large Language Models (MiniGPT-4) for multi-modality brain tumor segmentation, integrating four distinct MRI modalities (T1c, T1w, T2c, and FLAIR) onto a common space to enhance segmentation accuracy.
- Awarded Best Presenter and received a PhD offer with a Presidential Scholarship.
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2023.02 - 2025.09 Research Assistant
Mobility Lab, UCLA
Contributed to multi-agent perception, sensor fusion, and infrastructure-aware autonomous driving, co-authoring five papers on multi-modal sensor placement (InSPE), misalignment adaptation in cooperative perception (AgentAlign), class-aware map construction (RelMap), spatio-temporal fusion for V2X perception (V2XPnP), and real-world cooperative perception datasets (V2X-ReaLO).
- Participating in the U.S. DOT Intersection Safety Challenge and won $750,000
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2023.01 - 2024.12 Research Assistant
Vwani Roychowdhury's Lab, UCLA
Contributed to the implementation and deep learning models of Hilbert (HIL) detector of PyHFO, a multi-window desktop application providing time-efficient HFO detection algorithms for artifact and HFO with spike classification
- Reduced the detection run-time by 50 times compared to state-of-the-art with comparative study to ensure correctness.