报告题目：Automated Algorithm Design with Optimisation and Learning on Combinatorial Optimisaiton Problems

报告人：Yaochu Jin

报告时间： 2022年9月30日 15:00

报告地点：Tecent meeting: 456 755 951

报告摘要：Neural architecture search is a useful step towards automated machine learning in that it is able design deep neural networks through a learning or optimization process. However, not much work has been done on search of optimal neural architectures when the training data are distributed on multiple sites and subject to privacy protection. This talk introduces two recent algorithms that perform neural architecture search in a federated environment using multi-objective evolutionary algorithms. We focus on the efficient representation of deep neural architectures, handling multiple objectives in learning, and reducing computational complexity of the neural architecture search process.   

报告人简介：

Yaochu Jin is 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 Surrey Distinguished Chair, Professor in Computational Intelligence, Department of Computer Science, University of Surrey, Guildford, U.K. He was a “Finland Distinguished Professor” of University of Jyväskylä, Finland, “Changjiang Distinguished Visiting Professor”, Northeastern University, China, and “Distinguished Visiting Scholar”, University of Technology Sydney, Australia. His main research interests include evolutionary optimization and learning, trustworthy machine learning and optimization, and evolutionary developmental AI.

Prof Jin is presently the Editor-in-Chief of Complex & Intelligent Systems. He was the Editor-in-Chief of the IEEE Transactions on Cognitive and Developmental Systems, an IEEE Distinguished Lecturer in 2013-2015 and 2017-2019, the Vice President for Technical Activities of the IEEE Computational Intelligence Society (2015-2016). He is the recipient of the 2018 and 2021 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Web of Science as “a Highly Cited Researcher” consecutively from 2019 to 2021. He is a Member of Academia Europaea and Fellow of IEEE.

金耀初教授简介：

欧洲科学院院士、IEEE Fellow。目前为德国比勒菲尔德大学工程学院“洪堡人工智能讲席教授”，兼任英国萨里大学计算机系“计算智能”杰出讲席教授。曾任教育部特聘教授，芬兰国家创新局“芬兰讲座教授”，澳大利亚悉尼科技大学“杰出访问学者”。2021年荣获德国联邦教育与研究部“洪堡人工智能教席奖”，是德国最高荣誉的科研奖励。

金耀初教授已发表学术论文400余篇，获美国、欧盟和日本专利9项。据Google Scholar, 其论文被引用总次数32,000余次，h-index 88，入选Web of Science 2019、2020、2021年度 “全球高被引科学家”。多次获“IEEE进化计算汇刊优秀论文奖”及“IEEE 计算智能杂志优秀论文奖”。目前担任《IEEE认知与发育系统汇刊》主编，《复杂与智能系统》主编，IEEE计算智能学会理事。曾任IEEE计算智能学会副理事长，两次任IEEE 杰出演讲人。2015年由于其在复杂系统进化优化领域的贡献入选IEEE Fellow.

金耀初教授长期从事人工智能与计算智能的理论、算法和工程应用研究，特别是数据驱动的复杂系统进化优化、进化多目标机器学习、联邦学习与安全机器学习、演化发育系统与形态发育机器人学等。应用领域包括喷气发动机设计、空中客车机体设计、高提升力机翼系统、车辆空气动力学优化、混合电动车控制器设计、多机器人自组织及模块机器人自重构，医学图像处理，疫苗预测，抗生素生产过程基因调控重构等。曾获得欧盟第七框架研究计划，英国工程和自然科学研究会，英国皇家学会，以及包括本田研究院及本田研发公司、博世、华为、英国国家物理实验室、Pirbright等多家国际著名企业、研究机构的资助。