2023 IEEE Symposium Series on Computational Intelligence

DECEMBER 5 – 8, 2023 |Mexico City, Mexico

Workshop Session 1: Trustworthy AI through Model risk Management

Abstract: The workshop will focus on model risk management (MRM) for trustworthy AI which is an open and emerging area of research in data science, mathematics, and statistics. In particular, development of AI/ML models without understanding the underlying risk and uncertainty, particularly where pathological bias exists, can be detrimental to our society. As more and more complex and critical systems decision making relies on ML for applications ranging from financial to biological to defense. It is crucial to develop rigorous scientific techniques for decision making under risk and uncertainty using ML. The workshop will introduce the new center established at UNC Charlotte called Center for TAIMing AI and invite speakers to provide overview of the current state as well as help identify future directions of the emerging area of identification and management of risks when adopting AI.

SPEAKERS

Taufiquar Khan, University of North Carolina at Charlotte
Jake Lee, University of North Carolina at Charlotte
Andrew Pangia, University of North Carolina at Charlotte
Michael Pokojovy, Old Dominion University
Yuekai Sun, University of Michigan

Speaker Bios:

Dr. Taufiquar Khan is the PI for the research Center for TAIMing AI and Affiliate of the School of Data Science at the University of North Carolina at Charlotte (UNC Charlotte). He is currently a Professor and the Chair of the Department of Mathematics and Statistics. He was a Professor and an Associate Director of Graduate Studies of the School of Mathematical and Statistical Sciences, Clemson University, Clemson, SC, USA, before joining UNC Charlotte in 2020. He is a recipient of the Humboldt Fellowship from Germany. His research interests include machine learning, applied analysis, mathematical modeling, simulation, and coefficient inverse problems involving ordinary and partial differential equations.

Dr. Yuekai Sun is an associate professor of statistics at the University of Michigan. His research leverages statistical science to make AI more safe and reliable in the real world. Some topics of recent interest include AI alignment & safety, algorithmic fairness, learning under distribution shifts. Before coming to Michigan, Yuekai obtained his PhD in computational mathematics from Stanford University, where he worked with Michael Saunders and Jonathan Taylor, and his BA (also in computational math) from Rice University.

Dr. Jake Lee is an assistant professor of the Department of Computer Science and School of Data Science at the University of North Carolina at Charlotte. He received a PhD from Colorado State University in 2017. He is a Co-lead of the research Center for TAIMing AI and codirecting the Charlotte Machine Learning Lab (CharMLab). His research interests are in the knowledge acquisition and transfer for AI (reinforcement learning) agents, human-AI interactions, and trustworthy AI without sacrificing the efficiency of learning.

Dr. Michael Pokojovy is an Associate Professor of Data Science and School of Data Science Statistics Fellow at Old Dominion University, Norfolk, VA. He holds PhD and Dipl.-Math. degrees in Mathematical Sciences (with minor in Computer Science), both from the University of Konstanz, Germany. His research interests include Statistical & Machine Learning, Big Data Analytics, Scientific Computing, etc. In addition to numerous theoretical and methodological developments, he has a track record of applied and collaborative research in statistical process control, quantitative finance, engineering, biomedical sciences, rational mechanics, etc. He has authored/co-authored 40+ publications in various professional outlets and secured 10+ grants from NSF, DoEd, DHHS, DFG, etc.

Dr. Andrew Pangia is the inaugural industrial postdoc at the Center for TAIMing AI at UNC Charlotte. He received his PhD from the School of Mathematical Sciences in 2023. His research interest is in multi-criteria optimization and machine learning with applications to model risk management.

Agenda: