应网络与交换技术国家重点实验邱雪松教授邀请，美国弗罗里达大学电子和计算机工程系李晓林教授将于7月21日（星期五）来北京邮电大学作学术报告。欢迎校内广大师生踊跃参加。
讲座题目：Creating Intelligence via Big Learning
主讲人：李晓林教授
主持人：邱雪松
时间：2017年7月21日（星期五）下午2:00—3:00
地点：新科研楼610会议室
Abstract:
The tremendous big data generated from natural systems, engineered systems, and life demand new capabilities in algorithms and systems to explore insights and make decisions through high performance big data platforms. To address these challenges, we propose a two-pronged solution: data-driven self-programmed big learning models and self-programmed big systems, collectively called intelligent platforms. In particular, self-programmed systems feature large-scale software-defined computing; self-programmed big learning models feature deep hierarchical learning representation and data-driven self-programming. Our preliminary prototype DeepCloud is one of the first attempts to realize such a coherently symbiotic intelligent platforms. Case studies on enabling intelligence in business, health, and science will be highlighted.
Bio:
Dr. Xiaolin Andy Li is a Professor and University Term Professor in the Department of Electrical and Computer Engineering at the University of Florida. He is the founding director of National Science Foundation Center for Big Learning along with CMU, Oregon, and UMKC, the first NSF center on large-scale deep learning. He is also the director of Large-scale Intelligent Systems Laboratory. His research interests include cloud computing, big data, deep learning, and intelligent platforms. He has published over 100 peer-reviewed papers in journals and conference proceedings, 5 books, and 4 patents. His research has been sponsored by National Science Foundation (NSF), National Institutes of Health (NIH), Department of Homeland Security (DHS), Department of Energy (DOE), and others. He received a PhD degree in Computer Engineering from Rutgers University. He is a recipient of the National Science Foundation CAREER Award in 2010, the Internet2 Innovative Application Award in 2013, NSF I-Corps Top Team Award in 2015, the CAGI Challenge on Detecting Bipolar Disorder Top Team Award (DeepBipolar) in 2016, and best paper awards (IEEE ICMLA 2016, IEEE SECON 2016, ACM CAC 2013 and IEEE UbiSafe 2007).
