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                  当前位置: 培养工作 >  学术动态

                  7月人工智能相关讲座

                  [ 2019-07-05 ]

                  Internet of Video Things (IoVT): Next Generation IoT with Visual Sensors


                  Professor Chang Wen Chen
                  The Chinese University of Hong Kong, Shenzhen, China
                  & State University of New York at Buffalo, USA


                  时间:2019年7月8日 上午10:00

                  地点:上海交通大学闵行校区软件大楼5楼 人工智能研究院 500会议室


                  摘要:

                  The worldwide flourishing of the Internet of Things (IoT) in the past decade has enabled numerous new applications through the internetworking of a wide variety of devices and sensors. More recently, visual sensors has seen their considerable booming because they usually capable of providing richer and more versatile information. Internetworking of large scale visual sensors has been named Internet of Video Things (IoVT). IoVT has its own unique characteristics in sensing, transmission, storage, and analysis, which are essentially different from conventional IoT. These new characteristics of IoVT are expected to impose significant challenges to existing technical infrastructures. In this talk, an overview of recent advances in various fronts of IoVT will be introduced and a broad range of technological and system challenges will be presented.


                  个人简介:

                  Chang Wen Chen is currently Dean of School of Science and Engineering at the Chinese University of Hong Kong, Shenzhen. He is also an Empire Innovation Professor of Computer Science and Engineering at the University at Buffalo, State University of New York since 2008. He was Allen Henry Endow Chair Professor at the Florida Institute of Technology from July 2003 to December 2007. He was on the faculty of Electrical and Computer Engineering at the University of Rochester from 1992 to 1996 and on the faculty of Electrical and Computer Engineering at the University of Missouri-Columbia from 1996 to 2003.

                  He has been the Editor-in-Chief for IEEE Trans. Multimedia from January 2014 to December 2016. He has also served as the Editor-in-Chief for IEEE Trans. Circuits and Systems for Video Technology from January 2006 to December 2009. He has been an Editor for several other major IEEE Transactions and Journals, including the Proceedings of IEEE, IEEE Journal of Selected Areas in Communications, and IEEE Journal of Emerging and Selected Topics in Circuits and Systems. He has served as Conference Chair for several major IEEE, ACM and SPIE conferences related to multimedia video communications and signal processing. His research is supported by NSF, DARPA, Air Force, NASA, Whitaker Foundation, Microsoft, Intel, Kodak, Huawei, and Technicolor.

                  He received his BS from University of Science and Technology of China in 1983, MSEE from University of Southern California in 1986, and Ph.D. from University of Illinois at Urbana-Champaign in 1992. He and his students have received nine (9) Best Paper Awards or Best Student Paper Awards over the past two decades. He has also received several research and professional achievement awards, including the Sigma Xi Excellence in Graduate Research Mentoring Award in 2003, Alexander von Humboldt Research Award in 2009, the University at Buffalo Exceptional Scholar – Sustained Achievement Award in 2012, and the State University of New York System Chancellor’s Award for Excellence in Scholarship and Creative Activities in 2016. He is an IEEE Fellow since 2004 and an SPIE Fellow since 2007.



                  Entropic Analysis of Network Structure


                  报告人:Edwin R. Hancock

                  主持人:严骏驰


                  时间:2019年7月10日 下午14:00-16:00

                  地点:上海交通大学闵行校区软件大楼5楼 人工智能研究院 500会议室


                  摘要:

                  Computing the entropy of a network has proved to be an elusive problem, with potentially enormous impact on the fields of machine learning, complex systems  and big data. In this talk I will present an overview of recent work that has shown how ideas from spectral graph theory and statistical physics can be brought to bare on the problem, yielding simple methods for computing network entropy. The topics covered include detecting anomalies in network time series, modelling the time evolution of networks and decomposing networks into frequently occurring substructures, referred to as motifs. I will furnish examples from the financial and medical domains to illustrate the application of these techniques.


                  个人简介:


                  Edwin R. Hancock holds a BSc degree in physics (1977), a PhD degree in high-energy physics (1981) and a D.Sc. degree (2008) from the University of Durham, and a doctorate Honoris Causa from the University of Alicante in 2015. From 1981-1991 he worked as a researcher in the fields of high-energy nuclear physics and pattern recognition at the Rutherford-Appleton Laboratory (now the Central Research Laboratory of the Research Councils). During this period,  he worked on high energy physics experiments at the Stanford Linear Accelarator Center (SLAC) providing the first measurements of charmed particle lifetimes. He also held adjunct teaching posts at the University of Surrey and the Open University. In 1991, he moved to the University of York as a lecturer in the Department of Computer Science, where he has held a chair in Computer Vision since 1998. He leads a group of some 25 faculty, research staff, and PhD students working in the areas of computer vision and pattern recognition. His main research interests are in the use of optimization and probabilistic methods for high and intermediate level vision. He is also interested in the methodology of structural and statistical and pattern recognition. He is currently working on graph matching, shape-from-X, image data.




                  Point Cloud Compression and Communication

                  Zhu Li

                  University of Missouri, Kansas City

                  web:http://l.web.umkc.edu/lizhu

                  email: lizhu@umkc.edu

                  时间:2019年7月11日 上午10:00-11:30

                  地点:上海交通大学闵行校区软件大楼5楼 人工智能研究院 500会议室

                  Abstract:

                  Point cloud data arise from depth sensing and capturing for both auto driving/navigation/smart city, as well as content capture and VR/AR playback applications. Recent advances in sensor technology and algorithms, especially 77Ghz MIMO radar systems, and high resolution structured light in conjunction with very high resolution RGB camera arrays, have made point cloud capture getting closer to real world applications. In this talk I will overview the related research at the Multimedia Computing & Communication Lab at UMKC, in conjunction with our industry partners for the auto driving thrusts at the new NSF Center for Big Learning, and discuss the main technical challenges in point cloud capture, compression and communication for auto driving and smart city applications,  especially the static and dynamic geometry compression, as well as attributes compression problems, and the new graph signal processing tools that can bring new coding efficiency. Some initial results will be presented and discussed, as well as the MPEG Point Cloud Compression call for proposal and results.

                  Short Bio:


                  Zhu Li is an associated professor with the Dept of CSEE, University of Missouri, Kansas City, USA, directs  the new NSF I/UCRC Center for Big Learning at UMKC.  He received his PhD from Electrical & Computer Engineering from Northwestern University in 2004, and was AFRL Summer Visiting Faculty at the US Air Force Academy, UAV Research Center, 2016, 2017 and 2018, Sr. Staff Researcher/Sr. Manager with Samsung Research America's Multimedia Core Standards Research Lab in Dallas, from 2012-2015, Sr.Staff Researcher with FutureWei(Huawei)'s Media Lab in Bridgewater, NJ, from 2010-2012,  Assistant Professor with the Dept of Computing, The HongKong Polytechnic University from 2008 to 2010, and a Principal Staff Research Engineer with the Multimedia Research Lab (MRL), Motorola Labs, Schaumburg, Illinois, from 2000 to 2008. His research interests include image/video analysis, compression, and communication and associated optimization and machine learning tools. He has 46 issued or pending patents, 100+ publications in book chapters, journals, conference proceedings and standards contributions in these areas. He received a Best Paper Award from IEEE Int'l Conf on Multimedia & Expo (ICME) at Toronto, 2006, and a Best Paper Award from IEEE Int'l Conf on Image Processing (ICIP) at San Antonio, 2007.


                  Interface of statistics, computing, and data science


                  Xiaoming Huo,

                  Georgia Institute of Technology

                  时间:2019年7月12日上午10:00--11:30

                  地点:上海交通大学闵行校区软件大楼5楼 人工智能研究院 500会议室


                  Abstract:

                  Inference (aka predictive modeling) is in the core of many data science problems. Traditional approaches could be either statistically or computationally efficient, however not necessarily both. The existing principles in deriving these models – such as the maximal likelihood estimation principle - may have been developed decades ago, and do not take into account the new aspects of the data, such as their large volume, variety, velocity and veracity. On the other hand, many existing empirical algorithms are doing extremely well in a wide spectrum of applications, such as the deep learning framework; however they do not have the theoretical guarantee like these classical methods. We aim to develop new algorithms that are both computationally efficient and statistically optimal. Such a work is fundamental in nature, however will have significant impacts in all data science problems that one may encounter in the society. Following the aforementioned spirit, I will describe a set of my past and current projects including L1-based relaxation, fast nonlinear correlation, optimality of detectability, and nonconvex regularization. All of them integrates statistical and computational considerations to develop data analysis tools.


                  About the speaker

                  Xiaoming Huo is an A. Russell Chandler III Professor at the Stewart School of Industrial & Systems Engineering at Georgia Tech. Huo received a Ph.D. degree in statistics from Stanford University, Stanford, CA, in 1999. In August 1999, he joined the School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA, where he advanced through the ranks and became a chair professor. From 2013 to 2015, he was a program director at the National Science Foundation, managing data science related programs. He is the director of the Transdisciplinary Research Institute for Advancing Data Science at Georgia Institute of Technology (https://triad.gatech.edu/). He is a fellow of the American Statistical Association and a senior member of the IEEE.

                  Huo’s research interests include statistics and data science. He has made numerous contributions on topics such as sparse representation, wavelets, and statistical detectability. His papers appeared in top journals, and some of them are highly cited.

                  Huo won the Georgia Tech’s Sigma Xi Young Faculty Award in 2005. His work has led to an interview by Emerging Research Fronts in June 2006 in the field of Mathematics — every two months, one paper is selected. He participated in the 30th International Mathematical Olympiad (IMO), which was held in Braunschweig, Germany, in 1989, and received a golden prize.



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