报告一：叶杰平

报告题目： AI在出行领域的机遇和挑战

报告人：  叶杰平 滴滴出行副总裁

主持人：  周爱民 副经理

报告时间： 2019年9月26日 周四15:00-16:00

报告地点： 中北校区理科大楼A504

报告摘要：

滴滴出行是中国最大的共享出行平台，为超过5.5亿用户提供出行服务。每天滴滴出行平台产生超过100TB的数据，处理超过400亿条路径规划请求以及超过150亿条定位请求。在这个讲座里，我将分享滴滴出行如何利用大数据和AI的技术来分析出行数据，并为亿级用户提供高效的出行服务.

报告人简介：

叶杰平博士是滴滴AI Labs负责人，滴滴出行副总裁。叶博士也是美国密歇根大学教授。他的专业方向为大数据、机器学习、数据挖掘、及其在出行和生物医药领域的应用。他是多个国际顶级AI会议的资深委员会会员、区域主席和委员会副主席，包括NIPS、ICML、KDD、AAA、IIJCAI、ICDM和SDM等。他也是多个顶级AI期刊的副主编，包括DMKD, IEEE TKDE和IEEE TPAMI等。他于2010年获得美国国家自然科学基金会生涯奖。他的研究成果被选为顶级AI会议KDD和ICML的最佳论文。

报告二：Keith W Ross 

报告题目：Deep Reinforcement Learning

报告人： Keith W Ross Chair Professor

主持人： 周爱民 副经理

报告时间： 2019年9月26日 周四16:00-17:00

报告地点：中北校区理科大楼A504

报告摘要：

Reinforcement learning is about sequentially interacting with an environment in order to maximize a return. Recent advances in deep reinforcement learning have led to breakthroughs in AI for playing Go, playing computer video games with raw pixel inputs, and learning how to control simulated robotic locomotion. At NYU Shanghai, Keith and his students have been developing new on-policy and off-policy deep reinforcement learning algorithms. During this talk, we will introduce the field of deep reinforcement learning, and survey his team's recent work in the area.

报告人简介：

Keith Ross is the Dean of Engineering and Computer Science at NYU Shanghai and the Leonard J. Shustek Chair Professor of Computer Science at NYU. He is an ACM Fellow and an IEEE Fellow. He is co-author (with James F. Kurose) of the popular textbook, Computer Networking: A Top-Down Approach Featuring the Internet, which has been translated into fourteen languages.His current research interests are in deep reinforcement learning. Previously he has worked in Internet privacy, peer-to-peer networking, Internet measurement, queuing theory, and Markov decision processes.