报告题目：
<!--[if !supportLists]-->1. <!--[endif]-->Big-Data Analytics for Cloud, IoT, and Cognitive Computing (Kai Hwang教授)
<!--[if !supportLists]-->2. <!--[endif]-->AIWAC:Affective Interaction through Wide-Learning And Cognitive Computing (陈敏教授)
报告人：Kai Hwang教授（美国南加州大学）
陈敏教授（华中科技大学）
报告时间：2018年1月10日(周三) 9:30
报告地点：华南理工大学五山校区10号楼406会议室
热烈欢迎广大师生前往！
机械与汽车工程学院
2018年1月3日
报告人简介：
As a Life Fellow of the IEEE Computer Society, he is still working actively at frontier research area. He continues teaching at USC on high-performance computer systems, big-data analytics for cloud and cognitive computing, innovative mobile, social and IoT applications. Visit his home page: http://GridSec.usc.edu/Hwang.html for much details.
In particular, his work has helped the design and upgrade of today’s supercomputers, massive datacenters, or cloud platforms. Over the years, his teams at Purdue and USC have developed scalable computer architectures, built multicomputer clusters, tested public clouds, and grid security and defense systems with primarily NSF funding support.
Hwang earned the Ph.D. in EECS from U.C. Berkeley in 1972. Prior to joining USC in 1985, he was a Professor of Computer Engineering at Purdue University. He has taught a sequence of courses on principles of computer engineering, data structures, digital arithmetic, computer architecture, parallel processing, wireless Internet, cluster computing, compute/storage clouds, IoT sensing, and high-performance computing at Purdue and USC. A sample course syllabus and a recent presentation abstract are attached to reflect some of his current academic/research activities.
Min Chen is a full professor in School of Computer Science and Technology at Huazhong University of Science and Technology (HUST) since Feb. 2012. He is the director of Embedded and Pervasive Computing (EPIC) Lab at HUST. He is Chair of IEEE Computer Society (CS) Special Technical Communities (STC) on Big Data. He was an assistant professor in School of Computer Science and Engineering at Seoul National University (SNU). He worked as a Post-Doctoral Fellow in Department of Electrical and Computer Engineering at University of British Columbia (UBC) for three years. Before joining UBC, he was a Post-Doctoral Fellow at SNU for one and half years.
He received Best Paper Award from QShine 2008, IEEE ICC 2012, ICST IndustrialIoT 2016, and IEEE IWCMC 2016. He serves as editor or associate editor for Information Sciences, Information Fusion, and IEEE Access, etc. He is a Guest Editor for IEEE Network, IEEE Wireless Communications, and IEEE Trans. Service Computing, etc. He is Co-Chair of IEEE ICC 2012-Communications Theory Symposium, and Co-Chair of IEEE ICC 2013-Wireless Networks Symposium. He is General Co-Chair for IEEE CIT-2012, Tridentcom 2014, Mobimedia 2015, and Tridentcom 2017.
He is Keynote Speaker for CyberC 2012, Mobiquitous 2012, Cloudcomp 2015, IndustrialIoT 2016, and The 7th Brainstorming Workshop on 5G Wireless. He has more than 300 paper publications, including 200+ SCI papers, 80+ IEEE Trans./Journal papers, 16 ISI highly cited papers and 8 hot papers. He has published four books: OPNET IoT Simulation (2015), Big Data Inspiration (2015), 5G Software Defined Networks (2016) and Introduction to Cognitive Computing (2017) with HUST Presss, a book on big data: Big Data Related Technologies (2014) and a book on 5G: Cloud Based 5G Wireless Networks (2016) with Springer Series in Computer Science. His latest book (co-authored with Prof. Kai Hwang), entitled Big Data Analytics for Cloud/IoT and Cognitive Computing (Wiley, U.K.) appears in May 2017. His Google Scholars Citations reached 10,800+ with an h-index of 52. His top paper was cited 1080+ times.
He is an IEEE Senior Member since 2009. He got IEEE Communications Society Fred W. Ellersick Prize in 2017. His research focuses on Cyber Physical Systems, IoT Sensing, 5G Networks, Mobile Cloud Computing, SDN, Healthcare Big Data, Medica Cloud Privacy and Security, Body Area Networks, Emotion Communications and Robotics, etc.
报告摘要：
Title：Big-Data Analytics for Cloud, IoT, and Cognitive Computing (Kai Hwang教授)
Abstract: In this keynote speech, Dr. Hwang will address innovative applications of machine learning and big-data analytics on smart clouds, social networks, intelligent robots, and IoT platforms. He will assess machine/deep learning models and open-source software tools to advance the cognitive service industry now heavily pursued by Google, Baidu, Alibaba, Microsoft, Apple, IBM, Huawei, etc. The ultimate goal is to achieve enhanced agility, mobility, security, and scalability of public clouds, IoT platforms, and social-media networks. His presentation will assess current AI programs and brain projects at Google X-Lab, TensorFlow, DeepMind AlphaGo, Nvidia Digits 5 for using GPU in deep learning, and CAS/ICT Camericon project, etc. Some hidden R/D opportunities are revealed for building smart machines and self-driving cars. In particular, we will present some new ideas in using blockchains for securing cloud-based transactions in IoT crowdsensing environments; cognitive and AI applications in 5G health-care, desease detection; and large-scale social graph analysis for containing the mental disorder problems.
Title: AIWAC:Affective Interaction through Wide-Learning And Cognitive Computing (陈敏教授)
Abstract: With the development of the 5th generation wireless systems (5G), the wireless world is to be interconnected without barriers. The new technology is giving rise to more challenging applications, and people expect more personalized and interactive services with resource-limited mobile terminals. Fortunately, Mobile Cloud Computing (MCC) implemented in the context of 5G can overcome this bottleneck, which makes it possible to enable many resource-intensive services for mobile users with the support of mobile big data delivery and cloud-assisted computing. In this talk, a novel Emotion-aware Mobile Cloud Computing (EMC) framework in 5G will be given, which provides personalized emotion-aware services by MCC and affective computing. Meanwhile, artificial intelligence (AI) and cognitive computing are significant for emotion-aware computing and emotion detection to meet various technical challenges. Especially, the role of wide learning is emphasized, which integrates multiple deep nets models and multidimensional data collections through IoT and 5G technologies. The practical testbed named Affective Interaction through Wide-Learning And Cognitive Computing (AIWAC) will be introduced.
