Shuhan Tan

Hi there! I am currently a PhD student at The University of Texas at Austin, advised by Philipp Krähenbühl.

My research focus on simulate realistic human behavior and environment evolument. Specifically, my current research focuses on content generation for autonomous driving simulation systems, which aims to make autonomous driving safe and easily accessible for everyone.

My long-term goal is to develop realistic world modeling and simulation systems that can be used to train and test inteligent agents in a variety of domains.

Email  /  CV  /  Scholar  /  LinkedIn

The University of Texas at Austin
PhD in Computer Science • Aug. 2021 -

Sun Yat-Sen University
B.E. in Computer Science • Sep. 2016 - Jun. 2021
Ranking: 2/189

Waymo Research
Research Intern • Jun. 2024 -
With Chiyu "Max" Jiang

NVIDIA Research
Autonomous Vehicle Research Group
Research Intern • Aug. 2023 - Jun. 2024
With Boris Ivanovic , Xinshuo Weng , Yuxiao Chen ,
            Boyi Li , Yulong Cao , Marco Pavone

The University of Texas at Austin
Research Assistant • Jan. 2023 -
PhD Advisor: Philipp Krähenbühl
Research Assistant • Aug. 2021 - Dec. 2022
With Kristen Grauman

The Chinese University of Hong Kong
Research Assistant • Sep. 2020 - Mar. 2021
With Bolei Zhou

Uber ATG Toronto
Research Intern • Sep. 2019 - Aug. 2020
With Raquel Urtasun, Shenlong Wang

Boston University
Research Assistant • July 2019 - Sep. 2019
With Kate Saenko, Xingchao Peng

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

[09/2024] ProSim got accepted to CoRL 2024!

[06/2024] I started my internship at Waymo Research. Let's catch up in Bay Area!

[06/2024] I finished my fatastic internship at NVIDIA Research, shoutout to my great mentors!

[05/2024] I gave a 30-min intivated talk at Long-term Human Motion Prediction Workshop at ICRA 2024. [Slides]

Slides / Workshop

Paper / Project page / Code / Video / Colab Demo / Dataset

Simulate multi-agent interactions with multimodal prompts.

Paper / Project page / Code / Video / Colab Demo

Traffic scene generation with language condition using LLM.

Paper / Project page

Efficient egocentric video understanding with head motion data from IMU.

Paper / Project page/ Video

Synthesis new traffic scenario and replay in simulation.

Paper / Project page / Colab

Mitigate biases of GAN models without retraining.

Paper / Video / Bibtex

Generate realistic traffic scences automatically.

Paper / Bibtex

Align feature distributions across domains while the label distributions of the two domains are also different.

Paper / Supplement / Bibtex

Realistic sensor simulation for LiDAR.

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.

Paper / Bibtex

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.

Paper / 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.

Distinguished Graduate Thesis, Sun Yat-sen University

SenseTime Scholarship (21 out of all AI-focus undergraduate students in China)

Systems and Methods for Simulating Traffic Scenes. US Patent App. 17/528,277.

Shuhan Tan, Kelvin Ka Wing Wong, Shenlong Wang, Sivabalan Manivasagam, Mengye Ren, Raquel Urtasun

Reviewer: CVPR, ECCV, ICCV, ICRA, RA-L, TCSVT, TMI.

Instagram / Wiki

My little colleague, who always sleeps on my desk,
put his paw on the F5 key to prevent vscode debugging.