KEYNOTE TALK SERIES
A. Aldo Faisal
(Professor, Imperial College London)
Bio: Professor Aldo Faisal is the Professor of AI & Neuroscience at the Dept. of Computing and the Dept. of Bioengineering at Imperial College London. He was awarded a UKRI Turing AI Fellowship. Aldo is the Founding Director of the £20Mio. UKRI Centre for Doctoral Training in AI for Healthcare. He is the Elected Speaker of the Cross-Faculty Network in Artificial Intelligence representing AI in College on behalf of over 200 academic members.
At his two departments, Aldo leads the Brain & Behaviour Lab focussing on AI & Neuroscience and the Behaviour Analytics Lab at the Data Science Institute. He is Associate Investigator at the MRC London Institute of Medical Sciences and is affiliated faculty at the Gatsby Computational Neuroscience Unit (University College London).
Aldo serves as an Associate Editor for Nature Scientific Data and PLOS Computational Biology and has acted as conference chair, program/area chair, chair in key conferences in the field (e.g. Neurotechnix, KDD, NIPS, IEEE BSN). In 2016 he was elected into the Global Futures Council of the World Economic Forum.
(Professor, University of Calgary)
Bio: Marina L. Gavrilova is a Full Professor in the Department of Computer Science, University of Calgary, and a head of the Biometric Technologies Laboratory. Her publications include over 200 journal and conference papers, books and book chapters in the areas of pattern recognition, machine learning, biometric and online security. Dr. Gavrilova gave over 50 invited lectures and keynotes at major scientific gatherings, including Stanford University, SERIAS Center at Purdue, Microsoft Research USA, Oxford University UK, Samsung Research South Korea among others. She has founded ICCSA – an international conference series, and is serving as a Founding Editor-in-Chief of LNCS Transactions on Computational Science Springer and an Editor-in-Chief of the International Journal of Digital Human, Inderscience.
Title for Talk: Emergence of AI and DL in Biometric Systems and Cybersecurity
Abstract: Human identity recognition is one of the key mechanisms of ensuring proper asset and information access to individuals, which is the base of many government, social services, consumer, financial and recreational activities in the society. Biometrics are also increasingly used in a cybersecurity context to mitigate vulnerabilities and to ensure protection against an unauthorized access or estimate risk level.
This keynote will discuss how deep learning methods can enhance biometric recognition accuracy in a variety of settings: unimodal and multi-modal systems, emotion, activity recognition and risk assessment. The Keynote will discuss the design of biometric systems, some recently developed the deep learning architectures, and will conclude with some practical case studies and discussion of open problems.
(Professor, Northwestern University)
Bio: Randall Berry joined Northwestern University in 2000, where he is currently the Chair and John A. Dever Professor in the Department of Electrical and Computer Engineering. His research interests span topics in wireless communications, computer networking, network economics, and information theory. Dr. Berry received the M.S. and PhD degrees in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology in 1996 and 2000, respectively, where he was part of the Laboratory for Information and Decision Systems. His undergraduate education was at the University of Missouri-Rolla, where he received the B.S. degree in Electrical Engineering in 1993. In 1998 he was on the technical staff at MIT Lincoln Laboratory in the Advanced Networks Group. Dr. Berry is the recipient of a 2003 NSF CAREER award and an IEEE Fellow. With his co-authors, he has received best paper awards at the IEEE Workshop on Smart Data Pricing in 2015 and 2017 and at the 2016 WiOpt conference. He has served as an Editor for the IEEE Transactions on Wireless Communications and the IEEE Transactions on Information Theory and is currently a division editor for the Journal of Communications and Networks and an Area editor for the IEEE Open Journal of the Communications Society.
Title for Talk: Spectrum Sharing for 5G and beyond: A Network Economics View
Abstract: The evolution of commercial wireless networks to 5G and beyond will continue to increase the demands for wireless spectrum. Traditionally, commercial wireless service providers have utilized spectrum that is exclusively licensed to them. Moving forward, these networks will increasingly operate in spectrum that is shared including utilizing unlicensed spectrum and the tiered sharing approach recently adopted for the Citizens Broadband Radio Service (CBRS) adopted for sharing the 3.5 GHZ band with incumbent users. The success of these approaches is in turn tightly coupled to the economic impact they have on the competition between wireless service providers. In this talk we will discuss a framework for gaining insight into these impacts based on game theoretic models for competition with congestible resources. We will utilize this framework to illustrate potential impacts of different emerging sharing scenarios.
(Professor, McGill University)
Bio: Z. Zilic is a Professor of Electrical and Computer Engineering at McGill University, researching and teaching Embedded Systems, Internet of Things and Blockchain. Prior to McGill, he was a Member of Technical Staff at Lucent Technologies, FPGA Group. He holds Ph. D. and M. Sc. degrees from University of Toronto.
Title for Talk: From Body Area Networks to Blockchain in Healthcare
Abstract: In this talk, we overview the recent progress on networking and closer integration of lifestyle and health devices. We first summarize the development on the Body Area Network (BAN) adapters and show its integration with various mobile network settings, including the vehicular networks. Then, we present a case for the use of private blockchains for the health record management that are closely integrated with the BAN developments. We finalize with the specific blockchain enhancements, such as level-2 aggregation and the integration of the consensus schemes suited healthcare-related blockchains.
(Senior Graphics Researcher at Pixel Labs)
Bio: Yixin Hu is now a senior Graphics Researcher at Pixel Lab, Tencent America. She obtained her doctoral degree from Courant Institute of Mathematical Sciences, New York University where she joined Geometric Computing Lab and started working with professor Daniele Panozzo. Her research interests are Computer Graphics and 3D Geometry Processing. Her research has been recognized by Adobe Research Fellowship (2019), Jacob T. Schwartz Ph.D. Fellowship (2019), and Sandra Bleistein Prize (2021).
Title for Talk: WildMeshing: Unstructured Mesh Generation and Repairing in the Wild
Abstract: WildMeshing is a series of meshing algorithms I proposed during my PhD, which solved a long-standing yet fundamental problem in Geometry Processing: generating high-quality triangle/tetrahedral meshes and repairing imperfect geometries robustly and automatically. WildMeshing has been tested on over ten thousand real-world inputs and achieved a 100% success rate. The implementation of WildMeshing is open-source and is currently used in the released products of companies like nToppology, AREVO, Mechanical Finder, and Rhino3D. WildMeshing can also be used as a black-box for generating large collections of clean geometries for geometric deep learning.
Bio: Sophia Paleologou is a seasoned IT executive with over 20 years of experience developing software platforms for a wide spectrum of applications. She has been an integral part of the design and development of several large, enterprise-grade commercial products. She is currently the Chief Technology Officer of UNS, an identity and access management solution provider. Sophia holds a BS in
Mathematics from the University of Athens, Greece and a PhD in Computer Science from Yale University. She has also co-authored multiple patents in Cryptography and Error Correction.
Title for Talk: Enabling Better Medical Research through Privacy-Preserving Record Linking
Abstract: The outcomes of medical research heavily rely on the quality of the data it is based on. Although healthcare providers collect a vast amount of good-quality medical data, they are severely restricted in how they can share this data because of existing privacy regulations. Before patient data can be shared for research purposes, it has to be stripped from all information that could potentially be used to identify the associated patients. Once personal identifiable information (PII) is stripped, linking of medical data across healthcare providers becomes very challenging. In this work, we present a solution that uses a new cryptographic primitive to facilitate linking of medical data without using PII. We also present an implementation of our novel solution over a
network of security nodes that further bolsters the security and privacy characteristics of our solution as well as the results of a series of pilot tests engaging with a small number of providers and a state registry that explore potential integration and adoption challenges.
(Professor, University of Notre Dame)
Bio: Prof. Aaron Striegel is currently a Professor of Computer Science and Engineering at the University of Notre Dame. He also serves on the Executive Committee of the Wireless Institute at the University of Notre Dame and serves as the Bachelor of Arts in Computer Science Program Director. Prof. Striegel received his Ph.D in 2002 in Computer Engineering at Iowa State University under the direction of Dr. G. Manimaran. Prof. Striegel’s research interests focus on instrumenting the wireless networked ecosystem to gain insight with respect to user behavior and optimizing network performance. Flagship projects of Prof. Striegel include the NetSense, NetHealth, and Tesserae involving the instrumentation and analysis of data from hundreds of smartphones and wearables over a nearly seven year period of continuous data streaming. Further research interests of Prof. Striegel include heterogeneous network optimization (cellular, WiFi), content distribution via edge device pre-staging, and network security dynamics. Prof. Striegel has also successfully led undergraduate research utilizing low-cost gaming peripherals for education and rehabilitation.
(Research Scientist at Facebook AI Research)
Bio: Roberto Calandra is a Research Scientist at Facebook AI Research. Previously, he was a Postdoctoral Scholar at the University of California, Berkeley (US) in the Berkeley Artificial Intelligence Research Laboratory (BAIR) working with Sergey Levine. His education includes a Ph.D. from TU Darmstadt (Germany) under the supervision of Jan Peters and Marc Deisenroth, a M.Sc. in Machine Learning and Data Mining from the Aalto university (Finland), and a B.Sc. in Computer Science from the Università degli studi di Palermo (Italy). His scientific interests are broadly at the conjunction of Decision-making, Robotics and Machine Learning. Research topics that he is currently developing include Model-based Reinforcement Learning, Tactile Sensing, Morphology Adaptation, and Bayesian Optimization.
|Full Paper Submission:||23rd September 2022|
|Acceptance Notification:||6th October 2022|
|Final Paper Submission:||13th October 2022|
|Early Bird Registration||15th October 2022|
|Presentation Submission:||20th October 2022|
|Conference:||26 - 29 October 2022|
• Best Paper Award will be given for each track.