模式识别学术大讲堂

Advanced Lecture Series in Pattern Recognition

题    目 (TITLE)：Big MM Opportunities

讲 座 人 (SPEAKER)：Prof. Ramesh Jain (University of California)
主 持 人 (CHAIR)：Prof. Changsheng Xu

时    间 (TIME)：14:30pm, September 11 (Tuesday), 2018

地    点 (VENUE)：No.2 Conference Room (3rd floor), Intelligence Building

报告摘要（ABSTRACT）：

Big opportunities come with big challenges.  Big Data is usually multimodal and comprises streams.  This brings challenges in combining heterogeneous data streams coming from innumerable diverse sources providing information about people, their health, behavior, environment, social interactions, and beliefs.  People do not exist in isolation so all this data should be analyzed in the context of environmental, climatic, and other situations that must be recognized from several other diverse data streams.  In this talk, we will discuss how the current BigMM is growing faster than any projections.  We will also discuss research challenges in dealing with this tsunami of data and the unprecedented opportunities this offers to revolutionize the quality of life for the humanity in all parts of world.报告人简介（BIOGRAPHY）：

Ramesh Jain is an entrepreneur, researcher, and educator.  He is a Donald Bren Professor in Information & Computer Sciences at University of California, Irvine. His current research passion is in addressing health issues using cybernetic principles building on the progress in sensors, mobile, processing, and storage technologies.  He is founding director of the Institute for Future Health at UCI. Earlier he served on faculty of Georgia Tech, University of California at San Diego, The university of Michigan, Ann Arbor, Wayne State University, and Indian Institute of Technology, Kharagpur.  He is a Fellow of AAAS, ACM, IEEE, AAAI, IAPR, and SPIE. Ramesh co-founded several companies, managed them in initial stages, and then turned them over to professional management.  He enjoys new challenges and likes to use technology to solve them.  He is participating in addressing the biggest challenge for us all: how to live long in good health.