报告题目：Sensing-as-a-Service: A Cloud Computing System for Mobile Phone Sensing
报告人：美国雪城大学（Syracuse University） 唐剑博士
时间： 12月13日（周四）下午15：00 – 16：30
Dr. Jian TANG
Abstract: Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains such as environmental monitoring, social networking, healthcare, etc. In this talk, I will introduce a new concept, Sensing-as-a-Service, i.e., providing sensing services using mobile phones via a cloud computing system. A Sensing-as-a-Service cloud should meet the following requirements: 1)It must be able to support various mobile phone sensing applications on different smartphone platforms. 2) It must be energy-efficient. 3) It must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. I will identify unique challenges of designing and implementing a Sensing-as-a-Service cloud, review existing systems and methods, present viable solutions, and point out future research directions.
Speaker’s Bio: Dr. Jian Tang is currently an Assistant Professor in the Department of Electrical Engineering and Computer Science at Syracuse University. He earned his Ph.D degree in Computer Science from Arizona State University in 2006. His research interests lie in the areas of Cloud Computing, Big Data and Wireless Networking. Dr. Tang has published over 60 papers in premier journals and conferences. He received an NSF CAREER award in 2009. He has been an associate editor of IEEE Transactions on Vehicular Technology since 2010. He served as a co-chair for the Wireless Networking Symposium of IEEE Globecom’2010, ICNC’2012 and ICNC’2013. He also served as a TPC member for many conferences including IEEE Infocom 2010-2013, ICC 2006-2012 and Globecom 2006-2012.
报告人：百度技术副总监、多媒体部负责人 余凯 博士
时间： 11月14日（周三）下午14：30 – 16：30
Dr. Kai YU
Abstract: The quality of visual features is crucial for a wide range of computer vision topics, e.g., scene classification, object recognition, and object detection, which are very popular in recent computer vision venues. All these image classification tasks have traditionally relied on hand-crafted features to try to capture the essence of different visual patterns. Fundamentally, a long-term goal in AI research is to build intelligent systems that can automatically learn meaningful feature representations from a massive amount of image data.
The primary objective of this talk is to introduce a paradigm of feature learning from unlabeled images, with an emphasis on applications to supervised image classification. We provide a comprehensive coverage of recently developed algorithms for learning powerful sparse nonlinear features, and showcase their superior performance on a number of challenging image classification benchmarks, including Caltech101, PASCAL, and the recent large-scale problem ImageNet. Furthermore, we describe deep learning and a variety of deep learning algorithms, which learn rich feature hierarchies from unlabeled data and can capture complex invariance in visual patterns.
余凯博士任百度技术副总监，多媒体部负责人，主要负责公司在语音、图像、音频等领域面向互联网和移动应用的技术研发。加盟百度前，余凯博士在美国NEC研究院担任Media Analytics部门主管(Department Head)，领导团队在机器学习、图像识别、多媒体检索、视频监控，以及数据挖掘和人机交互等方面的产品技术研发。此前他曾在西门子公司任Senior Research Scientist。2011年曾在斯坦福大学计算机系客座主讲课程“CS121: 人工智能概论”。他在NIPS, ICML, CVPR, ICCV, ECCV，SIGIR, SIGKDD，TPAMI，TKDE等会议和杂志上发表了70多篇论文，引用超过2800次，H-index=28。曾担任机器学习国际会议ICML10, ICML11, NIPS11, NIPS12的Area Chair. 2012年他被评为中关村高端领军人才和北京市海聚计划高层次海外人才。余凯博士在南京大学获得学士和硕士学位，在德国慕尼黑大学获博士学位。