2003网站太阳集团将于2019年6月27日周四舉行兩場學術報告，敬請光臨！

    報告題目（一）：Network Resource Management in Wireless Networked Control Systems
    報告人： 美國聖母大學  胡曉波  教授  IEEE Fellow
    報告時間：2019年6月27日 周四 下午 15:00
    報告地點：逸夫樓2樓201會議室

報告摘要：Wireless networked control systems (WNCSs) are fundamental to many Internet-of-Things (IoT) applications that must work under real-time constraints in order to ensure timely collection of environmental data and proper delivery of control decisions. The Quality of Service (QoS) offered by a WNCS is thus often measured by how well it satisfies the end-to-end deadlines of the real-time tasks executed in the WNCS. Network resource management in WNCSs plays a critical role in achieving the desired QoS. Unexpected internal and external disturbances that may appear in WNCSs concurrently make resource management inherently challenging. The explosive growth of IoT applications especially in terms of their scale and complexity further exacerbate the level of difficulty in network resource management.
        In this talk, I first give a general introduction of WNCSs and the challenges that they present to network resource management. In particular, I will discuss the complications due to external disturbances and the need for dynamic data-link layer scheduling. I then highlight our recent work that aims at tackling this challenge. Our work balances the scheduling effort between a gateway (or access points) and the rest of the nodes in a network. It paves the way towards decentralized network resource management in order to achieve scalability. Experimental implementation on a wireless test bed further validates the applicability of our proposed research. I will end the talk outlining our on-going effort in this exciting and growing area of research.

報告人簡介： 胡曉波，美國聖母大學計算機科學與工程系教授，IEEE Fellow，ACM SIGDA主席。主要學術方向為低功耗系統設計、基于新興技術的電路和架構設計、軟硬件協同設計及嵌入式系統等，已發表論文300餘篇，并獲得Design Automation Conference，ACM/IEEE International Symposium on Low Power Electronics and Design 最佳論文獎，NSF傑出成就獎（NSF CAREER Award）。她參與多項政府-企業聯合資助的研究中心級别項目，包括擔任NSF/SRC E2CDA項目的負責人。她還擔任2018年度ACM/IEEE Design Automation Conference大會主席，2015年度DAC TPC主席；IEEE Transactions on VLSI、ACM Transaction on Design Automation of Electronic Systems、ACM Transactions on Embedded Computing Systems、ACM Transactions on Cyber-Physical Systems 等學術期刊的副主編。

    報告題目（二）：Intelligent Computing, Big Data, and Modern Medicine and Healthcare
    報告人： 美國聖母大學  陳子儀  教授  IEEE Fellow
    報告時間：2019年6月27日 周四 下午  16:00
    報告地點：逸夫樓2樓201會議室

報告摘要：Computer technology plays a crucial role in modern medicine, healthcare, and life sciences, especially in medical imaging, human genome study, clinical diagnosis and prognosis, treatment planning and optimization, treatment response evaluation and monitoring, and medical data management and analysis. As computer technology rapidly evolves, computer science solutions will inevitably become an integral part of modern medicine and healthcare. Computational research and applications on modeling, formulating, solving, and analyzing core problems in medicine and healthcare are not only critical, but are actually indispensable!
        Recently emerging deep learning (DL) techniques have achieved remarkably high quality results for many computer vision tasks, such as image classification, object detection, and semantic segmentation, largely outperforming traditional image processing methods. In this talk, we first discuss some development trends in the area of intelligent medicine and healthcare. We then present new approaches based on DL techniques for solving a set of medical imaging problems, such as segmentation and analysis of glial cells, analysis of the relations between glial cells and brain tumors, segmentation of neuron cells, and new training strategies for deep learning using sparsely annotated medical image data. We develop new deep learning models, based on fully convolutional networks (FCN), recurrent neural networks (RNN), and active learning, to effectively tackle the target medical imaging problems. For example, we combine FCN and RNN for 3D biomedical image segmentation; we propose a new complete bipartite network model for neuron cell segmentation. Further, we show that simply applying DL techniques alone is often insufficient to solve medical imaging problems. Hence, we construct other new methods to complement and work with DL techniques. For example, we devise a new cell cutting method based on k-terminal cut in geometric graphs, which complements the voxel-level segmentation of FCN to produce object-level segmentation of 3D glial cells. We show how to combine a set of FCNs with an approximation algorithm for the maximum k-set cover problem to form a new training strategy that takes significantly less annotation data. A key point we make is that DL is often used as one main step in our approaches, which is complemented by other main steps. We also show experimental data and results to illustrate the practical applications of our new DL approaches.

報告人簡介：陳子儀博士1985年獲得美國舊金山大學計算機科學和數學學士學位，并分别于1988年和1992年獲得美國普渡大學西拉法葉分校的計算機科學碩士和博士學位，他自1992年以來一直在美國聖母大學計算機科學與工程系任教，現任教授。陳教授的主要研究興趣是計算生物醫學，生物醫學成像，計算幾何，算法和數據結構，機器學習，數據挖掘和VLSI。他在這些領域發表了130多篇期刊論文和210多篇經過同行評審的會議論文，并擁有5項美國計算機科學與工程和生物醫學應用技術開發專利。他于1996年獲得NSF CAREER獎，2011年獲得計算機世界榮譽計劃的榮譽獎，用于開發“弧度調制放射治療”（一種新的放射性癌症治療方法）及2017年獲美國國家科學院的PNAS Cozzarelli獎。他是IEEE Fellow和ACM傑出科學家。