Speaker: Academician Yaochu Jin
Title: Data-Driven Optimization: From Optimality to Trustworthiness
Time: 14:30 pm, May 21, 2023 (Sunday)
Abstract:
Data-driven optimization has found abundant real-world applications, ranging from engineering optimization, drug design, to deep neural architecture search. After a brief review of data-driven optimization, this talk presents a few computationally efficient Bayesian evolutionary optimization algorithms for high-dimensional multi-objective optimization problems that exploit the synergies between machine learning, Bayesian optimization, and evolutionary algorithms. Then, we discuss security and privacy concerns in data-driven optimization and introduce recently developed ideas for secure and privacy-preserving federated data-driven optimization. We will conclude the talk with discussing fairness and preferences in data-driven optimization.
Personal Introduction:
Yaochu Jin is presently an Alexander von Humboldt Professor for Artificial Intelligence endowed by the German Federal Ministry of Education and Research, with the Faculty of Technology, Bielefeld University, Germany. He is also a Distinguished Chair in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor”, University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. Prof Jin is presently the Editor-in-Chief of Complex & Intelligent Systems. He was an IEEE Distinguished Lecturer, the Vice President for Technical Activities of the IEEE Computational Intelligence Society, and the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems. He was named by the Web of Science as “a Highly Cited Researcher” from 2019 to 2021 consecutively. He is a Member of Academia Europaea and Fellow of IEEE.
[Editor: Xiaohan Liu]