Jiahao Zhan 「詹佳豪」

I am a Ph.D. student at CUHK MMLab starting from Fall 2026, advised by Tianfan Xue. I am supported by the Hong Kong PhD Fellowship.

I obtained my B.E. in Artificial Intelligence from the College of Computer Science and Artificial Intelligence at Fudan University in 2026.

Before starting PhD, I had the pleasure of collaborating with Dequan Wang at Shanghai Jiao Tong University, Hang Zhao at Tsinghua University, Qifeng Chen at HKUST, and Jiajun Wu at Stanford University.

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Research

I am interested in how AIGC can create more compelling user experiences and be applied to real-world applications. Some papers are highlighted.

A hybrid generative simulator enabling long-horizon, action-conditioned 4D scene generation from a single image via a unified representation that links physical state and visual primitives.

An efficient reward modeling framework inspired by GANs that eliminates manual preference annotation, training through discrimination between target samples and model-generated outputs.

A reinforcement learning framework that synthesizes verifiable multimodal game tasks from game code, enabling VLMs trained on GameQA to improve general reasoning across diverse vision-language benchmarks.

A scalable motion planner using ViT encoder and MoE causal Transformer that generalizes better across different driving scenarios.

A live benchmark leveraging 25,000+ image-text pairs from top-tier journals to evaluate MLLMs' cross-modal scientific reasoning.

Research Experiences

Services

• ECCV 2026 Reviewer

Honors

• Hong Kong PhD Fellowship, 2026 - 2030

• CUHK Vice-Chancellor Scholarship, 2026

• National Scholarship, 2023, 2024

• Shanghai Outstanding Graduate

• Third Prize, Shape Completion and Reconstruction of Sweet Peppers Challenge (ECCV Workshop)

• Second Prize, Intel LLM-based Application Innovation Contest (Team Leader)

Website template from Jon Barron.