Abstract: Training deep neural networks requires an exorbitant amount of computation resources, including a heterogeneous mix of GPU and CPU devices. It is critical to place operations in a neural network on these devices in an optimal way, so that the training process can complete within the shortest amount of time. The state-of-the-art in the literature uses a deep reinforcement learning method based on policy gradients to solve this problem, but we believe that there remains ample room for further improvements. In this talk, I will present our recent work published in ICML 2018 and NeurIPS 2018 that uses proximal policy optimization (PPO) and cross-entropy minimization to achieve significantly better performance than the state-of-the-art. Our experiments with several popular neural network training benchmarks have demonstrated clear evidence of superior performance: with the same amount of learning time, our algorithm leads to placements that have training times up to 60% shorter. This talk will be targeting a general audience, and will therefore include a brief tutorial on basic ideas in reinforcement learning algorithms.

Bio: Baochun Li received his B.Engr. degree from the Department of Computer Science and Technology, Tsinghua University, China, in 1995 and his M.S. and Ph.D. degrees from the Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, in 1997 and 2000. Since 2000, he has been with the Department of Electrical and Computer Engineering at the University of Toronto, where he is currently a Professor. He holds the Bell Canada Endowed Chair in Computer Engineering since August 2005. His research interests include cloud computing, distributed systems, datacenter networking, and wireless systems.
    Dr. Li has co-authored more than 360 research papers, with a total of over 17000 citations, an H-index of 77 and an i10-index of 233, according to Google Scholar Citations. He was the recipient of the IEEE Communications Society Leonard G. Abraham Award in the Field of Communications Systems in 2000. In 2009, he was a recipient of the Multimedia Communications Best Paper Award from the IEEE Communications Society, and a recipient of the University of Toronto McLean Award. He is a Fellow of IEEE.

Abstract: Sensing and communication are two essential modules for IoT world. In this talk, I will share with you some of the recent research progress related to ubiquitous communication and ubiquitous sensing, respectively. In the field of ubiquitous communication, some efforts related to battery-free device communication and secure IoT communication will be covered. In the field of ubiquitous sensing, contact-less sensing leveraging wireless radio and visible/invisible light will be briefly discussed. Finally I will share with you an exciting direction which target at enabling sensing and communication simultaneously.

Bio: Dr. Zhang joined Hong Kong University of Science and Technology (HKUST) in Sept. 2005 where she is now Tencent Professor of Engineering and Chair Professor of the Department of Computer Science and Engineering. She is also serving as the co-director of Huawei-HKUST innovation lab and the director of digital life research center of HKUST. Before that, she was in Microsoft Research Asia, Beijing, from July 1999, where she was the research manager of the Wireless and Networking Group. Dr. Zhang has published more than 400 refereed papers in international leading journals and key conferences, with a total of over 21000 citations, an H-index of 78 according to Google Scholar Citations. She is the inventor of more than 50 granted and 20 pending International patents. Her current research interests include Internet of Things (IoT), smart health, mobile computing and sensing, wireless networking, as well as cyber security. Her research style is interdisciplinary in general.
    She is a Fellow of IEEE for "contribution to the mobility and spectrum management of wireless networks and mobile communications". Dr. Zhang has received MIT TR100 (MIT Technology Review) world’s top young innovator award. She received several Best Paper Awards in international conferences. She received the Oversea Young Investigator Award from the National Natural Science Foundation of China (NSFC) in 2006. She held the Cheung Kong Chair Professor (长江讲座教授) in Huazhong University of Science and Technology (2012-2015).

Abstract: We have entered the era of big data. However, the performance improvement of disk-based storage systems has been much slower than that of memory and network, creating a significant I/O performance gap. To reduce the performance gap, storage subsystems are under extensive changes, adopting new technologies and adding more layers into the memory/storage hierarchy. With a deeper memory hierarchy, the data movement complexity is increased significantly, making it harder to utilize the potential of the deep memory-storage hierarchy (DMSH) architecture. In this talk, we first show that storage data access is the most concerned performance bottleneck, even in a distributed mobile environment. Then, we present the development of Hermes, an intelligent, multi-tiered, dynamic, and distributed I/O caching system that utilizes DMSH to significantly accelerate I/O performance. Hermes is a US NSF supported large software development project. It extends HPC I/O stacks to integrated memory and parallel I/O systems, extends the widely used Data Format (HDF) and HDF5 library to understand users’ need and to achieve application-aware optimization in a DMSH environment. We will introduce the Hermes’ design and implementation; discuss its uniqueness and challenges; and present some initial design and implementation results.

Bio: Dr. Xian-He Sun is a University Distinguished Professor of Computer Science at the Department of Computer Science in the Illinois Institute of Technology (IIT). He is the director of the Scalable Computing Software laboratory at IIT and a guest faculty in the Mathematics and Computer Science Division at the Argonne National Laboratory. Before joining IIT, he worked at DoE Ames National Laboratory, at ICASE, NASA Langley Research Center, at Louisiana State University, Baton Rouge, and was an ASEE fellow at Navy Research Laboratories. Dr. Sun is an IEEE fellow and is known for his memory-bounded speedup model, also called Sun-Ni’s Law, for scalable computing. His research interests include data-intensive high-performance computing, memory and I/O systems, software system for big data applications, and performance evaluation and optimization. He has over 250 publications and 6 patents in these areas. He is the Associate Chief Editor of IEEE Transactions on Parallel and Distributed Systems, a Golden Core member of the IEEE CS society, a former vice chair of the IEEE Technical Committee on Scalable Computing, the past chair of the Computer Science Department at IIT, and is serving and served on the editorial board of leading professional journals in the field of parallel processing. Dr. Sun received the Overseas Outstanding Contributions Award from the China Computer Federation (CCF) in 2018. More information about Dr. Sun can be found at his web site www.cs.iit.edu/~sun/.

Abstract: With the advancement of mobile sensing and pervasive computing, extensive research is being carried out in various application domains such as Internet of Things (IoT), smart healthcare, connected vehicles, and their security issues. My research work explores the power of pervasive sensing technologies to benefit people’s daily lives and make impacts on the society advancement, especially in two emerging areas: IoT security and smart healthcare. Particularly, my group studies how to conduct user authentication on any solid surface for IoT applications and how to perform vital signs monitoring during sleep towards smart healthcare. The first part of my talk introduces the idea of extending user authentication beyond traditional touch screens to any solid surface for smart access systems (e.g., access to apartments, vehicles or smart homes). The system builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. It integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide enhanced security for many IoT applications. The second part of my talk describes how to track human vital signs of breathing and heart rates during sleep, which serve as critical inputs for assessing the general physical health of a person and providing useful clues for diagnosing possible diseases. Different from previous work, our system re-uses existing WiFi network for tracking vital signs of breathing and heart rates concurrently without dedicated/wearable sensors or additional wireless infrastructure (e.g., USRP). The system exploits the fine-grained channel state information of WiFi signals to capture the minute body movements caused by breathing and heartbeats. The proposed system thus has the potential to be widely deployed in home environments and perform continuous long-term monitoring at a low-cost. Finally, I will share with you some new research directions I would like to pursue with the aim of influencing the future of smart homes and smart cities.

Bio: Yingying (Jennifer) Chen is the Associate Director of Wireless Information Network Laboratory (WINLAB) and a Professor of Electrical and Computer Engineering at Rutgers University. She also leads the Data Analysis and Information Security (DAISY) Lab. Her research interests include smart healthcare, cyber security and privacy, Internet of Things, and mobile computing and sensing. She has co-authored three books, published over 150 journals and referred conference papers and obtained 8 patents. Her background is a combination of Computer Science, Computer Engineering and Physics. Prior to joining Rutgers, she was a tenured professor at Stevens Institute of Technology and had extensive industry experiences at Nokia (previously Alcatel-Lucent). She is the recipient of the NSF CAREER Award and Google Faculty Research Award. She also received NJ Inventors Hall of Fame Innovator Award. She is the recipient of multiple Best Paper Awards from IEEE CNS 2018, IEEE SECON 2017, ACM AsiaCCS 2016, IEEE CNS 2014 and ACM MobiCom 2011. She is the recipient of IEEE Region 1 Technological Innovation in Academic Award 2017; she also received the IEEE Outstanding Contribution Award from IEEE New Jersey Coast Section each year 2005 - 2009. Her research has been reported in numerous media outlets including MIT Technology Review, CNN, Fox News Channel, Wall Street Journal, National Public Radio and IEEE Spectrum. She has been serving/served on the editorial boards of IEEE Transactions on Mobile Computing (IEEE TMC), IEEE Transactions on Wireless Communications (IEEE TWireless), IEEE/ACM Transactions on Networking (IEEE/ACM ToN) and ACM Transactions on Privacy and Security.