Min-max optimization without gradients: Convergence and applications to black-box evasion and poisoning attacks
- Sijia Liu
- Songtao Lu
- et al.
- 2020
- ICML 2020
Songtao Lu is a Senior Research Scientist in the Mathematics and Theoretical Computer Science Department at the IBM Thomas J. Waston Research Center in Yorktown Heights, NY. Additionally, he serves as a principal investigator at the MIT-IBM Watson AI Lab in Cambridge, MA. He obtained his Ph.D. from the Department of Electrical and Computer Engineering at Iowa State University in August 2018. He was a Postdoc Associate with the Department of Electrical and Computer Engineering at the University of Minnesota from Sept. 2018 to Sept. 2019, and AI resident with the Mathematics of AI group at the IBM Thomas J. Watson Research Center from Sept. 2019 to Aug. 2020. His research primarily focuses on foundational machine learning models and algorithms, with applications in trustworthy learning, meta-learning, and distributed learning. He received the Best Paper Runner-Up Award at UAI in 2022, an Outstanding Paper Award from FL-NeurIPS in 2022, an IBM Entrepreneur Award in 2023, and multiple IBM Research Accomplishment Awards.
Please see my personal website for more details: https://songtaogithub.github.io