Tianshu Yu, 于天舒
Recruitment
Ph.D. student recruitment
I am always looking for highly self-motivated Ph.D. students with solid mathematical background and programming skill to join my group. Application for 2023 is open. Potential research topics includes (but not limited to):
- Machine learning for combinatorial problems
- Efficient sampling
- Learning for sequential data
- Generative model for discrete structure
Tuition and competitive stipend will be fully covered. Contact me by email if you are interested. Please attach your CV, all transcripts (undergraduate/graduate level) and research interest in your email.
You can refer to
PhD/MPhil in CS program for more details in terms of the application.
RA recruitment
I am also recruiting RAs starting anytime. Background on the following fields is particularly expected:
- Machine learning for discrete problems
- Optimization for combinatorial problems
- Learning with differential equations
Contact me by email if you are interested. Please attach your CV.
Short Bio
I joined SDS@CUHKSZ as an assistant professor on Sep, 2021. Previously I got my Ph.D. in computer science at Arizona State University, advised by professor Baoxin Li.
My research interest covers several aspects of machine learning and discrete optimization. Generally, I like investigating theory and applications on machine learning of combinatorial problem on graph, and seeking its interpretation under deep learning framework.
Supervision
Chenguang Wang (王晨光), PhD student, Sep 2022 -
Chaolong Ying (应潮龙), PhD student, Sep 2022 -
Xuanhao Pan (潘宣昊), PhD student, Sep 2022 -
Ruiying Liu (刘蕊颖), PhD student (co-supervised with Ruimao Zhang), Sep 2022 -
Bohan Zhuang (庄博涵), MPhil student, Sep 2022 -
Biaolin Wen (温标林), MSc student, July 2022 -
Zihan Zhou (周子涵), undergraduate, Oct 2021 -
Haoyu Zhang (张浩宇), RA, Aug 2021 -
Weihuang Wen (温伟煌), RA, Apr 2022 -
Teaching
CSC3100 (21fall, 22summer)
CSC1001 (21fall)
Publications
- Tianshu Yu, Runzhong Wang, Junchi Yan, Baoxin Li. Deep Latent Graph Matching. ICML2021
- Runzhong Wang, Tianqi Zhang, Tianshu Yu, Junchi Yan, Xiaokang Yang. Combinatorial Learning of Graph Edit Distance via Dynamic Embedding. CVPR2021
- Zhiyuan Fang, Shu Kong, Tianshu Yu, Yezhou Yang. Weakly Supervised Attention Learning for Textual Phrases Grounding. CVPR2018 workshop
- Hong Shao, Shuang Chen, Jieyi Zhao, Wencheng Cui, Tianshu Yu. Face recognition based on subset selection via metric learning on manifold. FITEE 16:1046-1058, 2015
- Hong Shao*, Tianshu Yu*, Mengjia Xu, Wencheng Cui. Image region duplication detection based on circular window expansion and phase correlation. Forensic Science International 222:71-82, 2012 (*equal contribution)
Experience
- May 2020 - August 2020, AWS - Seattle, applied scientist intern. My mentors include Hao Li and Gurumurthy Swaminathan. Working on some projects on AutoML.
- May 2019 - August 2019, Adobe - Seattle, research intern. My mentors include Eli Shechtman, Connelly Barnes and Sohrab Amirghodsi. I was conducting a research of image inpainting via understanding the contextual layout of the scene.
- May 2017 - August 2017, IBM Research China - Shanghai, research intern (Great mind project). My mentor is Dr. Junchi Yan and my research is about joint cuts and matching of graphs.
- July 2012 - April 2014, Philips Product Creation Center - Shenyang, full-time Algorithm & Physics Engineer. My responsibility is developping algorithms for CT imaging.
Services
- Conf PC/reviewer: ICLR, ICML, NIPS, CVPR, ICCV, ECCV, AAAI(SPC), IJCAI.
- Journal Reviewer: IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Image Processing (TIP), Pattern Recognition, Pattern Recognition Letters
Honors and Awards
- CIDSE Doctoral Fellowship, Arizona State University, Spring 2021
- Engineering Graduate Fellowship, Arizona State University, 2018-2021
- Alberta Innovates-Technology Futures Scholarship, University of Calgary, 2014-2016