Zimo Wang

Hi, I am Zimo Wang 王子墨, a Data Science Ph.D. student at University of California, San Diego. I’m very lucky to be advised by Professor Tzu-Mao Li, who is both supportive and has a great sense of humor. Together, we explore problems in computer graphics, vision, and beyond, inspired by statistical methods and elegant mathematical ideas.

I am excited about conducting research that combines useful math with real-world applications. We are also open to exploring new directions and connecting dots that others overlooked. Feel free to reach out if you’re interested in my research or would like to chat!

Experience

2025 Summer: Research Intern at Adobe Research, fortunately advised by Yuting Yang and Jiawen Chen. Worked on generating color mappings from image pairs.

2023 - Now: Data Science PhD Student, University of California San Diego

2019 - 2023: Majoring in Geographical Information Science, Statistics, Zhejiang University

2019 - 2023: Minoring in Computer Science and Technology, Zhejiang University

Research

HotSpot: Signed Distance Function Optimization with an Asymptotically Sufficient Condition

Project Page | Paper | Advisor: Tzu-Mao Li

Zimo Wang, Cheng Wang, Taiki Yoshino, Sirui Tao, Ziyang Fu, Tzu-Mao Li. * indicates equal contribution.
Conference on Computer Vision and Pattern Recognition (Highlight). 2025.

tl;dr: Conventional SDF loss functions are insufficient constraints and fail to ensure convergence to a true distance function, whereas our loss does.

DPS-Net: Deep Polarimetric Stereo Depth Estimation

Paper | Advisor: Zhaopeng Cui

Chaoran Tian, Weihong Pan, Zimo Wang, Mao Mao, Guofeng Zhang, Hujun Bao, Ping Tan, Zhaopeng Cui.
International Conference on Computer Vision. 2023.

tl;dr: We propose a novel deep learning framework called DPS-Net for depth estimation from polarimetric stereo images.

A House Price Valuation Model Based on Geographically Neural Network Weighted Regression

Paper | Advisor: Sensen Wu and Zhenhong Du

Zimo Wang, Yicheng Wang, Sensen Wu, Zhenhong Du.
ISPRS International Journal of Geo-Information 11, no. 8: 450. 2022.

tl;dr: Neural networks can be powerful for learning kernel functions in a spatial weighted regression model to predict house prices.

Hobbies

As a member of the UCSD table tennis team, I don’t mind playing against most people using just my phone screen. I always bring my racket🏓 to academic venues. Email me for a friendly match!

I like stargazing. / In the final of the schoolwide freshmen debate competition of Zhejiang University, my team got the second place.

I had my rock&roll band in the high school!

Gallery

After an incredible journey around the world, I'm excited to share some breathtaking views with you. Join me in exploring these beautiful moments captured through my lens!

This website is modified from this source code. No. Visitor Since January 2023.