I am a fourth-year PhD student in the Department of Computer Science at the University of Rochester, advised by Prof. Chenliang Xu. Previously, I spent one wonderful year as a research assistant at the Chinese University of Hong Kong, working with Prof. Chi-Wing Fu on 3D vision. I received my B.Eng. from ESE Department, Nanjing University in 2019. In my undergrad, I worked with Prof. Zhan Ma on image compression.

I am broadly interested in developing machine learning models to understand how human perceive the surrounding scenes from multi-modal inputs and utilize the perception for action. Specifically, I am working on multimodal video understanding and generation.

Research opportunities: I am open to collaborating on research projects. Shoot me an email if you are insterested.

Email  /  CV  /  Google Scholar

We use pretrained text-guided diffusion models to scale up/down concepts in image/audio.

A new take on the audio-visual separation problem with the recent generative diffusion models.

We achieve joint audio-visual editing under language guidance.

Thinking of the equivalent of 3D Gaussian Splatting and volumetric primitives for the human body soundfield? Here, we introduce Acoustic Primitives.

A survey on the recent Large Language Models for video understanding.

We propose a novel method of synthesizing real-world audio-visual scenes at novel positions and directions.

We explore the problem of sound source visual localization in egocentric videos, propose a new localization method and establish a benchmark for evaluation.

A non-local attention based method for point cloud denoising in both synthetic and real scenes.

An image compression system under extreme condition, e.g., < 0.05 bits per pixel (bpp).

The template is based on Jon Barron's website.