Shu Hu

Courtesy Appointment as an Assistant Professor

Contact Info

765-495-7711

ET 201K

Indianapolis

I am an assistant professor in the School of Applied and Creative Computing and the Director of the Purdue Machine Learning and Media Forensics (M2) Lab at Purdue University, USA.

I was an assistant professor at Indiana University-Purdue University Indianapolis from 2023 to 2024. Before that, I was a Post-Doctoral Fellow at the Heinz College of Carnegie Mellon University from 2022 to 2023. I received my Ph.D. degree in Computer Science and Engineering from University at Buffalo (UBuffalo), SUNY in 2022. I spend my first and second years of Ph.D. at University at Albany (UAlbany), SUNY. I received my Master of Arts (Mathematics) degree from University at Albany, SUNY in 2020 and I also received my Master of Engineering degree from University of Science and Technology of China (USTC) in 2016. I was previously a visiting student (2014-2015) at University of South Australia (UniSA). My bachelor's degree in Electronic Information Engineering was obtained from North China Institute of Science and Technology in 2013.

I am the recipient of the National AI Research Resource (NAIRR) Pilot award (2024), the National Science Foundation CRII Award (2024), the Machine Intelligence Research Outstanding Reviewer Award (2023), and SUNY Buffalo's CSE Best PhD Dissertation Award (2022).

Personal Webpage: https://web.ics.purdue.edu/~hu968/

Google Scholar: https://scholar.google.com/citations?user=q4qu28QAAAAJ&hl=en

Research Interests

Machine Learning
Digital Media Forensics
Computer Vision

Education History

Ph.D., Computer Science, University at Buffalo, SUNY, 06/2022
M.A., Mathematics, University at Albany, SUNY, 06/2020
M.Eng., Software Engineering, University of Science and Technology of China, 04/2016

Honors and Awards

Outstanding Reviewer Award, Machine Intelligence Research, 2023
CSE Best PhD Dissertation (with a prize of $500), University at Buffalo, 2022

Publications and Presentations

Selected Publications

Google Scholar Profile
[IEEE TPAMI] Shu Hu, Xin Wang, Siwei Lyu. Rank-based Decomposable Losses in Machine Learning: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2023, 7. (Impact Factor: 23.6).
[JMLR] Shu Hu, Yiming Ying, Xin Wang, and Siwei Lyu. Sum of Ranked Range Loss for Supervised Learning. Journal of Machine Learning Research. 2022, 4. (Impact Factor: 3.654).
[ICCV] Shu Hu, Lipeng Ke, Xin Wang, and Siwei Lyu. TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning. International Conference on Computer Vision, Montreal, Canada, 2021, 10. (Acceptance rate 1617/6236≈25.9%).
[ICASSP] Shu Hu, Yuezun Li, Siwei Lyu. Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights. 46th IEEE International Conference on Acoustics, Speech, and Signal Processing, Toronto, Ontario, Canada, 2021, 1. (Acceptance rate 1734/3610≈48%).
[NeurIPS] Shu Hu, Yiming Ying, Xin Wang, and Siwei Lyu. Learning by minimizing the sum of ranked range. Thirty-fourth Conference on Neural Information Processing Systems, Montreal, QC, Canada, 2020, 9. (Acceptance rate 1900/9454≈20.07%).