Keynotes
Speaker: Prof. Sri Krishnan, Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada
Title of Talk: Signal Analysis for Remote Health Monitoring
Biography: Sri Krishnan received the B.E. degree in Electronics and Communication Engineering from the College of Engineering, Guindy, Anna University, India, in 1993, and the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering from the University of Calgary, Calgary, Alberta, Canada, in 1996 and 1999 respectively. He joined the Department of Electrical, Computer, and Biomedical Engineering, Ryerson University, Toronto, Ontario, Canada in July 1999, and currently he is a Professor in the Department. Since July 2011, he is an Associate Dean (Research and Development) for the Faculty of Engineering and Architectural Science. He is also the Founding Co-Director of the Institute for Biomedical Engineering, Science and Technology (iBEST) and an Affiliate Scientist at the Keenan Research Centre in St. Michael's Hospital, Toronto. iBEST is a research and innovation partnership between Ryerson University and St. Michael's Hospital which includes more than 37 scientists/engineers and 110 students/trainees from both the institutions with the mandate of bench to bedside discovery research to translational outcomes.
Sri Krishnan held the Canada Research Chair position (2007-2017) in Biomedical Signal Analysis. He serves in the editorial boards of Biomedical Signal Processing and Control journal and Sensors journal. Sri Krishnan is also a technical committee member of Biomedical Signal Processing in the IEEE Engineering in Medicine and Biology Society. He is a Fellow of the Canadian Academy of Engineering and a registered professional engineer in the Province of Ontario.
Talk description: In this talk, the contextual topic of remote health monitoring using wearable devices, information and communication technology, signal processing and machine learning will be covered. Home-based remote monitoring of vital health signals will not only benefit the current pandemic situation but also long term healthcare needs such as telemedicine, digital health and rehabilitation. The talk will also cover the current research done in the area of connected healthcare and wearable computing in the Signal Analysis Research Group at Ryerson University, Canada.
Speaker: Dr. Domenico Ciuonzo,DIETI, University of Naples, Federico II, Italy
Title of Talk: Toward effective Network Traffic Classification via Deep Learning
Biography: Domenico Ciuonzo is an Assistant Professor at DIETI, University of Naples, Federico II, Italy. He received the B.Sc. and M.Sc. (summa) degrees in computer engineering and the Ph.D. degree from the University of Campania "L. Vanvitelli", Aversa, Italy, in 2007, 2009, and 2013, respectively. Since 2011, he has held several visiting appointments: NATO CMRE, IT (2011); ECE Department, University of Connecticut, US (2012); Department of Electronics and Telecommunications, NTNU, Trondheim, NOR (2015 and 2016); Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Castelldefels, ES (2018). His reviewing activities were recognized by the IEEE Communications Letters (in '13, '17 and '19), IEEE Trans. on Communications (in '14), IEEE Trans. on Instrumentation and Measurement (in '16), IEEE Transactions on Wireless Communications (in '17 and '18) and MDPI Sensors (in '17), which nominated him Exemplary Reviewer. He also received a similar recognition (“Top Reviewers” Award) for the whole MDPI publisher in 2017. Furthermore, his editorial activities were recognized by the IEEE Communications Letters, which nominated him Best Editor in '18 and '19, respectively. Since ‘14 he has served as Associate Editor for several IET, Elsevier and IEEE journals. Currently, he is also an Area Editor for the IEEE Transactions on Aerospace and Electronic Systems and the IEEE Communications Letters. His research interests fall within the areas of data fusion, network traffic analysis, statistical signal processing, IoT and wireless sensor networks and wireless communications. Domenico Ciuonzo has co-authored 85+ journal and conference publications within highly-reputed venues. Since 2016 he is an IEEE Senior Member. In '19, he received the Best Paper Award at 4th IEEE ICCCS. In '19, he was the recipient of the "Exceptional Service Award", from IEEE Aerospace and Electronic Systems Society (AESS). In '20 he received the "Technical Achievement Award", from IEEE Sensors Council for the area Sensor Systems or Networks (early career). In the same year, he received Best Paper Award from Elsevier Computer Networks. He is co-author of the book “Data Fusion in Wireless Sensor Networks: A Statistical Perspective”, published by the IET (Apr. 2019). D. Ciuonzo has served and serves as independent reviewer/evaluator of research and implementation projects and project proposals co-funded by many EU and non-EU parties.
Talk description: In recent years operators have experienced the tremendous growth of the traffic to be managed in their networks, whose heterogeneous composition (e.g. mobile/IoT devices, anonymity tools), dynamicity, and increasing encryption is posing new challenges toward actionable network traffic analytics. In this talk, the topic of network traffic classification will be covered, due to its applications in network management, user-tailored experience, and privacy. First, the reasoned use of the Deep Learning umbrella will be introduced and explained in such a context. Hence, lessons learned and common pitfalls will be highlighted. Subsequently, the adoption of sophisticated multi-modal multi-task architectures will be put forward. The talk will also cover the current research done in the area of AI-based network traffic analysis at the TRAFFIC group of the University of Naples Federico II, Italy.