Speakers (Alphabetical order by last name)
Prof. Xiaoming Fu (Georg-August-University of Goettingen) - https://user.informatik.uni-goettingen.de/~fu/
Networking Implications of Video Analytics
With the tremendous growth of Internet videos and their analytics techniques, various challenges emerge particularly those concerning mobility and analytics efficiency. How to mitigate these challenges and how can we embrace the opportunities in this era? In this talk, I will introduce some of our recent efforts and outline a vision towards integrating sensing, networking and data analytics in a supporting infrastructure in the digital world.
Prof. Xiaoming Fu received his Ph.D. in computer science from Tsinghua University, Beijing, China in 2000. He was then a research staff at the Technical University Berlin until joining the University of Göttingen, Germany in 2002 as assistant professor, where he has been a full professor of computer science and heading the Computer Networks Group since April 2007. He has spent research visits at universities of Cambridge, Columbia, UCLA, Tsinghua, Nanjing, Fudan, Uppsala, UPMC, and Sydney universities.
Prof. Fu's research interests include network architectures, protocols, and applications. He is an IEEE Fellow, an ACM Distinguished Member, and a member of Academia Europaea, and is currently an editorial board member of IEEE Network and IEEE Transactions on Network and Service Management, and co-editor-in-chief of the Journal of Social Computing. He has served on committee conferences of leading conferences such as SIGCOMM, CoNEXT, MOBICOM, MOBIHOC, INFOCOM, ICNP, ICDCS, IWQoS and COSN. He has served as secretary (2008-2010) and vice chair (2010-2012) of the IEEE Communications Society Technical Committee on Computer Communications (TCCC), and chair (2011-2013) of the Internet Technical Committee (ITC) of the IEEE Communications Society and the Internet Society. He has been a PI in the EU FP6 ENABLE, VIDIOS, Daidalos-II, MING-T, FP7 GreenICN, H2020 COSAFE, HE CODECO and COVER projects, as well as the coordinator of EU FP7 MobileCloud, GreenICN and CleanSky projects and H2020 ICN2020 project.
Prof. Wenjing Lou - https://www.cnsr.ictas.vt.edu/WJLou.html
Strengthening Machine Learning-based Intrusion Detection Systems in Adversarial Environments
Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks. In this talk, we will discuss technologies designed to strengthen ML-based IDS. On one hand, we explore methods for enhancing the performance and robustness of IDS. We introduce a contrastive learning-based approach that builds highly discriminative IDS, enabling more effective differentiation between normal and malicious activities. On the other hand, we develop efficient security mechanisms to counter common adversarial attacks. This includes an adversarial example (AE) detector designed to identify suspicious inputs at the model testing time, filtering out potential AEs, and a robust model evaluation method that leverages latent space representations to build resiliency in model aggregation against model poisoning attacks in federated learning. This talk will share the outcomes of our research in fortifying ML-based IDS, shedding light on the advancements and techniques that can enhance the security and robustness of these systems in adversarial environments.
Wenjing Lou is the W. C. English Endowed Professor of Computer Science at Virginia Tech and a Fellow of the IEEE. She holds a Ph.D. in Electrical and Computer Engineering from the University of Florida. Her research interests cover many topics in the cybersecurity field, with her current research interest focusing on wireless networks, blockchain systems, trustworthy machine learning systems, and security and privacy problems in the Internet of Things (IoT) systems. Prof. Lou is a highly cited researcher by the Web of Science Group. She received the Virginia Tech Alumni Award for Research Excellence in 2018, the highest university-level faculty research award. She received the INFOCOM Test-of-Time paper award in 2020. She is the TPC chair for IEEE INFOCOM 2019 and ACM WiSec 2020. She was the Steering Committee Chair for IEEE CNS conference from 2013 to 2020. She is currently a steering committee member of IEEE INFOCOM and IEEE CNS. She served as a program director at US National Science Foundation (NSF) from 2014 to 2017.
Prof. Yves Robert (ENS Lyon) - https://graal.ens-lyon.fr/~yrobert/
Future data and HPC centers are expected to have a highly variable computing potential: the total capacity, cost, and nature (green or brown) of available power will change over time by large fractions, with or without prior notice.
In this talk, we first review state-of-the art techniques and optimization metrics for resource management and job allocation. Then we formulate the new challenges and opportunities that come with scheduling on variable-capacity platforms. We survey several strategies, some old and some new, to address these challenges. RESIZING (changing the runtime of applications, and hence their scheduling profile) and REASSIGNING (changing the mapping of jobs to account for machine volatility) are key to improving efficiency. We also discuss the impact of workload flexibility and rate/magnitude of resource variation.
Yves Robert is a full professor in the computer science laboratory LIP at ENS Lyon.
He is the author of 7 books, 170 papers published in international journals, and 250 papers published in international conferences. He is the editor of 11 book proceedings and 15 journal special issues. He is the advisor of 38 PhD students. He is an IEEE Fellow and a Senior Member of Institut Universitaire de France.
He has been awarded the 2014 IEEE TCSC Award for Excellence in Scalable Computing, the 2016 IEEE TCPP Outstanding Service Award, and the 2020 IEEE CS Charles Babbage Award.
His main research interests are scheduling techniques and resilient algorithms for large-scale platforms. See https://graal.ens-lyon.fr/~yrobert/ for more information.
Prof. Jie Wu (Temple University) - https://cis.temple.edu/~wu/
Edge-Cloud Networks for Efficient AI/ML Implementations
Edge-cloud networks connect a wide range of systems and devices, ranging from large data centers to small IoT devices. The potential advantages of adopting edge and cloud networking include the quick adaptation of new technologies, including a faster rollout and adoption of software and feature updates, as well as better management of various resources, including network and edge devices. This talk provides an overview of some challenges in efficient support for running AI/ML algorithms in edge-cloud networks. Our focus is on low latency, connectivity, and local data processing while still achieving efficiency. We will look at two specific examples of AI/ML implementations: one is the optimal offloading of AI/ML code from IoT/edge devices to the cloud, and the other is exploring network topology and connectivity for efficient decentralized federated learning.
Jie Wu is Laura H. Carnell Professor at Temple University and the Director of the Center for Networked Computing (CNC). He served as Chair of the Department of Computer and Information Sciences from the summer of 2009 to the summer of 2016 and Associate Vice Provost for International Affairs from the fall of 2015 to the summer of 2017. Prior to joining Temple University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University, where he received his Ph.D. in 1989. His current research interests include mobile computing and wireless networks, routing protocols, network trust and security, distributed algorithms, applied machine learning, and cloud computing. Dr. Wu regularly published in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Service Computing and Journal of Computer Science and Technology. Dr. Wu is/was general chair/co-chair for IEEE DCOSS’09, IEEE ICDCS’13, ICPP’16, IEEE CNS’16, WiOpt’21, ICDCN’22, IEEE IPDPS'23, and ACM MobiHoc'23 as well as program chair/cochair for IEEE MASS’04, IEEE INFORCOM’11, CCF CNCC’13, and ICCCN’20. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a Fellow of the AAAS and a Fellow of the IEEE. He is the recipient of the 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award. He is a Member of the Academia Europaea (MAE).