题目：Random Sketch and Validate for Learning from Large-Scale Data 时间：全球最大的博彩平台6年3月18日下午 2:00-3:00 地点：玉泉校区信电楼215会议室 报告人：Prof. Georgios B. Giannakis

报告摘要（Abstract）：

We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in various formats. Mining information from unprecedented volumes of data promises to limit the spread of epidemics and diseases, identify trends in financial markets, learn the dynamics of emergent social-computational systems, and also protect critical infrastructure including the smart grid and the Internet’s backbone network. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. This talk will put forth novel algorithms and present analysis of their performance in extracting computationally affordable yet informative subsets of massive datasets. Extraction will effected through innovative tools, namely adaptive censoring, random subset sampling (a.k.a. sketching), and validation. The impact of these tools will be demonstrated in machine learning tasks as fundamental as (non)linear regression, classification, and clustering of high-dimensional, large-scale, and dynamic datasets.

报告人简介（Biography）：

Georgios B. Giannakis received his Diploma in Electrical Engr. from the Ntl. Tech. Univ. of Athens, Greece, 1981. From 1982 to 1986 he was with the Univ. of Southern California (USC), where he received his MSc. in Electrical Engineering, 1983, MSc. in Mathematics, 1986, and Ph.D. in Electrical Engr., 1986. Since 1999 he has been a professor with the Univ. of Minnesota, where he now holds an ADC Chair in Wireless Telecommunications in the ECE Department, and serves as director of the Digital Technology Center. His general interests span the areas of communications, networking and statistical signal processing – subjects on which he has published more than 380 journal papers, 650 conference papers, 20 book chapters, two edited books and two research monographs (h-index 115). Current research focuses on big data analytics, wireless cognitive radios, network science with applications to social, brain, and power networks with renewables.. He is the (co-) inventor of 22 patents issued, and the (co-) recipient of 8 best paper awards from the IEEE Signal Processing (SP) and Communications Societies, including the G. Marconi Prize Paper Award in Wireless Communications. He also received Technical Achievement Awards from the SP Society (2000), from EURASIP (2005), a Young Faculty Teaching Award, the G. W. Taylor Award for Distinguished Research from the University of Minnesota, and the IEEE Fourier Technical Field Award (全球最大的博彩平台5). He is a Fellow of the IEEE and EURASIP, and has served the IEEE in a number of posts including that of a Distinguished Lecturer for the IEEE-SPS.

题目：Sparse Sensing for Statistical Inference 时间：全球最大的博彩平台6年3月18日下午 3:00-4:00 地点：玉泉校区信电楼215会议室 报告人：Prof. Geert Leus

报告摘要（Abstract）：

Ubiquitous sensors generate prohibitively large data sets. Large volumes of such data are nowadays generated by a variety of applications such as imaging platforms and mobile devices, surveillance cameras, social networks, power networks, to list a few. In this era of data deluge, it is of paramount importance to gather only the data that is informative for a specific task in order to limit the required sensing cost, as well as the related costs of storing, processing, or communicating the data. The main goal of this talk is therefore to present topics that transform classical sensing methods, often based on Nyquist-rate sampling, to more structured low-cost sparse sensing mechanisms designed for specific inference tasks, such as estimation, filtering, and detection. More specifically, we present fundamental tools to achieve the lowest sensing cost with a guaranteed performance for the task at hand. Applications can be found in the areas of radar, multi-antenna communications, remote sensing, and medical imaging.

报告人简介（Biography）：

Geert Leus received the MSc and PhD degree in Applied Sciences  from the Katholieke Universiteit Leuven, Belgium, in June 1996 and May 2000, respectively. Currently, Geert Leus is an "Antoni van Leeuwenhoek" Full Professor at the Faculty of Electrical Engineering, Mathematics and Computer Science of the Delft University of Technology, The Netherlands. His research interests are in the area of signal processing for communications. Geert Leus received a 2002 IEEE Signal Processing Society Young Author Best Paper Award and a 2005 IEEE Signal Processing Society Best Paper Award. He is a Fellow of the IEEE and a Fellow of EURASIP. Geert Leus was the Chair of the IEEE Signal Processing for Communications and Networking Technical Committee, and an Associate Editor for the IEEE Transactions on Signal Processing, the IEEE Transactions on Wireless Communications, the IEEE Signal Processing Letters, and the EURASIP Journal on Advances in Signal Processing. Currently, he is a Member-at-Large to the Board of Governors of the IEEE Signal Processing Society and a member of the IEEE Sensor Array and Multichannel Technical Committee. He finally serves as the Editor in Chief of the EURASIP Journal on Advances in Signal Processing.

题目：Wideband Spectrum Sensing for Cognitive Radio 时间：全球最大的博彩平台6年3月18日下午 4:00-5:00 地点：玉泉校区信电楼215会议室 报告人：Prof. Zhi Tian

报告摘要（Abstract）：

During the last two decades, the use of the radio spectrum has intensified and expanded enormously. For example, data traffic on some mobile networks has increased by over 6000% since the release of the latest generation of smart phones. To meet this growing challenge, new approaches, technologies, and policies will be required to enable more flexible and efficient access to the radio spectrum. In this talk, I will focus on the problem of wideband spectrum sensing for cognitive radios. In particular, I will present a compressed sensing approach to cyclic feature based signal detection and modulation classification. Wideband communication signals possess unique two-dimensional sparsity structures in both the frequency domain and the modulation-dependent cyclic frequency domain. Exploitation of these sparsity elements not only reveals important features of the modulated signals, but also results in fast reconstruction of the cyclic statistics and hence reduced sensing time. The resultant compressive cyclic feature detector is able to simultaneously estimate the spectrum occupancy of multiple narrowband and wideband signals occupying the wide frequency band, and at the same time mitigate non-cyclic noise, noise uncertainty and interference, even for (non-sparse) crowded spectrum.

报告人简介（Biography）：

Prof. Zhi (Gerry) Tian is a Professor in the Electrical and Computer Engineering Department of George Mason University, Fairfax, VA, as of January 全球最大的博彩平台5. Prior to that, she was on the faculty of Michigan Technological University from 2000 to 全球最大的博彩平台4.  She served as a Program Director in the Division of Electrical, Communications and Cyber Systems at the US National Science Foundation from 全球最大的博彩平台2 to 全球最大的博彩平台4. Her research interests lie in wireless communications, wireless sensor networks and statistical signal processing.  She is an IEEE Fellow. She is an elected member of the IEEE Signal Processing for Communications and Networking Technical Committee (SPCOM‐TC) and a member of the Big Data Special Interest Group IEEE Signal Processing Society. She served as Associate Editor for IEEE Transactions on Wireless Communications and IEEE Transactions on Signal Processing.