Dingxi Zhang

Hi!! I'm Dingxi (Kristen) Zhang, a computer science Master student at ETH Zurich. Before that, I was studying at the University of Chinese Academy of Sciences (UCAS). My research focus on 3D vision, computer graphics and generative models. I'm fortunate to be working with Prof. Lin Gao and Prof. Shiguang Shan at Institute of Computing Technology, Chinese Academy of Sciences. I'm also currently an intern at VAST.

In my spare time, I love playing basketball, skiing, strumming the guitar, and constructing some Lego creations.

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Research

My research interests are 3D vision, graphics and machine learning. Specifically, my current focus centers on developing efficient neural representations and devising user-friendly and effective methods for interacting with 3D objects and scenes.

A novel approach in robot navigation combines LLMs with traditional methods.

A 3D neural style transfer framework with adaptable control over perceptual factors based on 3D Gaussian Splatting representation.

A novel stroke-based method for editing facial NeRF appearance. Our method outperforms existing 2D and 3D methods in both editing reality and geometry retention.

Using an weight-encoded Neural Network to approximate a grid of latent codes, while sharing the decoder across the entire category to achieve better reconstruction quality while using less memory per sample to store single geometry.

Experience

Other Interesting Projects

Besides research, I also enjoy coding various projects to explore how computer science can enhance our daily lives, as well as foster engaging interactions with diverse subjects.

An efficient and complete system for cervical spine prevention on common PC device, it can detect cervical posture in real-time, provide timely feedback to remind users to pose correctly and an interactive game to guide the user to exercise their cervical spine scientifically.

A highly compatible and robust platform with impressive performance to construct an artificially organized signaling pathway based on synthetic biology. We designed a NLP-based search tool Powered by Llama2 and BERT for iGEM standard parts, Ask NOX.

A system capable of producing nutritious, delicious, and highly user-free meal replacement foods by lactic acid bacteria modified by synthetic biology technology. We design a portable IoT machine to make meal replacement, an application for our machine & an entertaining science game and a convenient tool for creating wiki.

A comprehensive caffeine intake management system and provide users with scientific and proper caffeine intake recommendations. We design an application Caffeine-monitor to achieve this and an online education platform iGEM EduHub for better Synthetic Biology education.

Website Template: Jon Barron's website.