报告题目：Evidential learning: Towards reliable artificial intelligence

报告地点：英国正版365官方网站长安校区文津楼3427学术报告厅

报告时间：2024年8月16日14:00-16:00

报告人：周治国 教授

报告摘要：Artificial intelligence (AI) is changing this world. However, many current AI models is hard or even impossible to be applied into real world in many areas, especially in healthcare. Constructing the reliable AI model which realize balance, adaptation, credibility and interpretation in a unified way is a potential way to translate AI into real applications. A new evidential learning (EL) strategy which is developed based on evidential reasoning rule is a potential path to realize reliable AI. In this talk, the overall framework of EI, EI based learning methods as well as algorithms, and the applications in healthcare will be presented.

报告人介绍：Dr. Zhiguo Zhou is an Assistant Professor in Department of Biostatistics & Data Scienceandthe Director of Reliable Intelligence and Medical Innovation Laboratory (RIMI Lab) at University of Kansas Medical Center (KUMC). He is also an Associate Member at University of Kansas Cancer Center (KUCC)and a member ofUniversity of Kansas Alzheimer's Disease Research Center(KU ADRC). He received his B.S. and Ph.D. degrees at Xidian University (Xi’an, China) in 2008 and 2014, respectively. He was a visiting scholar at Leiden University (Leiden, the Netherlands) from May 2013 to May 2014. Since December 2014, he worked as Postdoctoral fellow at Department of Radiation Oncology, UT Southwestern Medical Center (Dallas, TX). Then he was promoted as Research Instructor in September 2017. Before moving to KUMC in May 2022, he worked as an Assistant Professor in School of Computer Science and Mathematics at University of Central Missouri (Warrensburg, MO) starting from August 2019. He has published more than100 journal or conference papers. He is the editor board member of 2 journals and guest associate editor of Medical Physics. He was the reviewer of more than 20 journals and session chair in multiple conferences. His research interests include reliable artificial intelligence, machine learning and deep learning,radiomics, treatment outcome prediction, clinical diagnostic support, medical image processing,pathological analysis,etc.