Shuhan Tan(谭书涵)

I am a junior undergraduate student in School of Data and Computer Science, Sun Yat-Sen University. My research interests lie on Machine Learning, Computer Vision and Medical Image Processing. Specifically, I have been working on Domain Adaptation, Person Re-identification and Biomarker Localization.

I am currently a research intern at Uber ATG, advised by Prof. Raquel Urtasun. Previously, I was very fortunate to have worked with Prof. Kate Saenko at Boston University and Prof. Wei-Shi Zheng at iSEE-SYSU.

Email  /  CV  /  LinkedIn


Sun Yat-Sen University
Bachelor of Engineering • Sep. 2016 - Present
Overall GPA: 91.74/100, Major GPA: 93.12/100
Ranking: 4/212

Research Experience

Uber ATG Toronto
Research Intern • Sep. 2019 - Present
Adviser: Professor Raquel Urtasun

Boston University
Research Assistant • July 2019 - Sep. 2019
Adviser: Professor Kate Saenko

Sun Yat-Sen University
Undergraduate Researcher • Sep. 2017 - June 2019
Adviser: Professor Wei-Shi Zheng


[10/2019] I started to work in Toronto.

[07/2019] I started to work in Boston.

[06/2019] Our paper is accepted by MICCAI 2019.

[03/2019] Our paper on Domain Adaptation is accepted by CVPR 2019.


Biomarker Localization by Combining CNN Classifier and Generative Adversarial Network
Rong Zhang, Shuhan Tan, Ruixuan Wang, Siyamalan Manivannan, Jingjing Chen, Haotian Lin, Wei-Shi Zheng.
MICCAI, 2019

We proposed a novel deep neural network architecture to effectively localize potential biomarkers in medical images, when only the image-level labels are available during model training.


Weakly Supervised Open-set Domain Adaptation by Dual-domain Collaboration
Shuhan Tan, Jiening Jiao, Wei-Shi Zheng
CVPR, 2019
arxiv / supplement / bibtex

We proposed a practical weakly supervised setting for open-set domain adaptation, where two scarcely-labeled domains collaboratively learn from each other.

Invited for presentation at WebVision 2019.

Updated Oct. 2019

Special thanks to Jon Barron for website template.