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New Frontiers in Adversarial Machine Learning
Sijia Liu · Pin-Yu Chen · Dongxiao Zhu · Eric Wong · Kathrin Grosse · Hima Lakkaraju · Sanmi Koyejo

Fri Jul 22 05:50 AM -- 02:10 PM (PDT) @ Room 343 - 344
Event URL: https://advml-frontier.github.io/ »

Adversarial machine learning (AdvML), which aims at tricking ML models by providing deceptive inputs, has been identified as a powerful method to improve various trustworthiness metrics (e.g., adversarial robustness, explainability, and fairness) and to advance versatile ML paradigms (e.g., supervised and self-supervised learning, and static and continual learning). As a consequence of the proliferation of AdvML-inspired research works, the proposed workshop–New Frontiers in AdvML–aims to identify the challenges and limitations of current AdvML methods and explore new prospective and constructive views of AdvML across the full theory/algorithm/application stack. The workshop will explore the new frontiers of AdvML from the following new perspectives: (1) advances in foundational AdvML research, (2) principles and practice of scalable AdvML, and (3) AdvML for good. This will be a full-day workshop, which accepts full paper submissions (up to 6 pages) as well as “blue sky” extended abstract submissions (up to 2 pages).

Author Information

Sijia Liu (Michigan State University)
Pin-Yu Chen (IBM Research AI)
Dongxiao Zhu (Wayne State University)

Dongxiao Zhu is currently an Associate Professor at Department of Computer Science, Wayne State University. He received the B.S. from Shandong University (1996), the M.S. from Peking University (1999) and the Ph.D. from University of Michigan (2006). Dongxiao Zhu's recent research interests are in Machine Learning and Applications in health informatics, natural language processing, medical imaging and other data science domains. Dr. Zhu is the Director of Machine Learning and Predictive Analytics (MLPA) Lab and the Director of Computer Science Graduate Program at Wayne State University. He has published over 70 peer-reviewed publications and numerous book chapters and he served on several editorial boards of scientific journals. Dr. Zhu's research has been supported by NIH, NSF and private agencies and he has served on multiple NIH and NSF grant review panels. Dr. Zhu has advised numerous students at undergraduate, graduate and postdoctoral levels and his teaching interest lies in programming language, data structures and algorithms, machine learning and data science.

Eric Wong (MIT, UPenn)
Kathrin Grosse (University of Cagliari)
Hima Lakkaraju (Harvard)
Sanmi Koyejo (Google / Illinois)

Sanmi (Oluwasanmi) Koyejo an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. Koyejo's research interests are in the development and analysis of probabilistic and statistical machine learning techniques motivated by, and applied to various modern big data problems. He is particularly interested in the analysis of large scale neuroimaging data. Koyejo completed his Ph.D in Electrical Engineering at the University of Texas at Austin advised by Joydeep Ghosh, and completed postdoctoral research at Stanford University with a focus on developing Machine learning techniques for neuroimaging data. His postdoctoral research was primarily with Russell A. Poldrack and Pradeep Ravikumar. Koyejo has been the recipient of several awards including the outstanding NCE/ECE student award, a best student paper award from the conference on uncertainty in artificial intelligence (UAI) and a trainee award from the Organization for Human Brain Mapping (OHBM).

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