Keynote Speakers



Sir Kostya Novoselov

(Nobel Laureate Professor, National University of Singapore)

Bio: Sir Konstantin Novoselov, Russian-British physicist was awarded the 2010 Nobel Prize for Physics for his experiments with graphene. He made it into a shortlist of scientists with multiple hot papers for the years 2007–2008 (shared second place with 13 hot papers) and 2009 (5th place with 12 hot papers). In 2014 Kostya Novoselov was included in the list of the most highly cited researchers. He was also named among the 17 hottest researchers worldwide—”individuals who have published the greatest number of hot papers during 2012–2013″. Novoselov joined the National University of Singapore’s Centre for Advanced 2D Materials in 2019, making him the first Nobel laureate to join a Singaporean university.

Kostya Novoselov’s research interests cover a wide range of topics from mesoscopic superconductivity and ferromagnetism to materials science and biophysics. He studied vortex structures in mesoscopic superconductors, observed atomic-scale movements of ferromagnetic walls, monitored heartbeats of individual bacteria and mimicked gecko’s adhesion mechanism. His breakthrough moment was the discovery of graphene. Novoselov is now widely recognised to be one of the pioneers in this field (as a number of international awards prove) and, together with Prof Geim FRS, leads research on various applications of this new material ranging from electronics, photonics, composite materials, chemistry, etc. Prof. Novoselov is strongly committed to disseminating science through public lectures and media interviews.


A. Aldo Faisal

(Professor, Imperial College London)

Bio: Professor Aldo Faisal (@FaisalLab) is the Professor of AI & Neuroscience at the Dept. of Computing and the Dept. of Bioengineering at Imperial College London (UK) and Chair of Digital Health at the University of Bayreuth (Germany). In 2021 he was awarded a prestigious 5-year UKRI Turing AI Fellowship. Since 2019, Aldo is the founding director of the £20Mio UKRI Centre for Doctoral Training in AI for Healthcare, and leads the Behaviour Analytics Lab at the Data Science Institute, London. Aldo works at the interface of Machine Learning, Medicine and translational Biomedical Rngineering to help people in diseases and health. He currently is one of the few engineers world-wide that lead their own clinical trials to validate their technology. In this space his works focusses on Digital Biomarkers and AI for medical intervention (Makin et al,Nat Biomed Eng; Komorowski et al, NatMed, 2018; Gottessmann et al NatMed, 2019). His work received a number of prizes and awards, including the $50,000 Research Discovery Prize by the Toyota Foundation.

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Marina Gavrilova

(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.

Randall Berry

(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.

Zeljko Zilic

Zeljko Zilic

(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.

Yixin Hu

(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.

Sophia Paleologou


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.

Aaron Striegel

(Professor, University of Notre Dame)

Bio: Prof. Aaron Striegel is currently a Professor of Computer Science and Engineering at the University of Notre Dame.  and also serves on the Executive Committee of the Wireless Institute at the University of Notre Dame. 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. 

Title for Talk: Network Measurement: Great or Good Enough?

Abstract: Network measurement and in particular end-to-end network measurement has received significant attention from the research community.  One of the key focal points has been to determine what the likely end-user performance will be by measuring various network characteristics including throughput, latency, loss, and jitter.  For many researchers, there has been a natural tendency to place a significant emphasis accuracy, namely how close is my measurement technique to the actual ground truth?  The goal of my keynote is to challenge that view and to ask whether accurate but expensive measurements are worth it?  Rather, do measurements simply need to be good enough for the task at hand?  I will present why the dramatic variations in speed associated with the latest wireless access technologies make absolute accuracy less important and that such an absolute focus on fidelity poses tremendous challenges for addressing issues such as broadband inequity and consistency of reasonable network access quality.  Iterating on this view, I will outline several open research challenges and opportunities in this space that are ripe for researchers to address.  

Roberto Calandra

(Research Scientist at Facebook AI Research)

Bio: Roberto Calandra is a Research Scientist at Meta AI (formerly Facebook AI Research). Previously, he was a Postdoctoral Scholar at the University of California, Berkeley (US) in the Berkeley Artificial Intelligence Research Laboratory (BAIR). His education includes a Ph.D. from TU Darmstadt (Germany), 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. He served the scientific community, among other, by being Program Chair for AISTATS 2020, Guest Editor for JMLR, and co-organizing over 17 workshops at international conferences (NeurIPS, ICML, ICLR, ICRA, IROS, RSS). He also led the development of DIGIT -- the first commercial compact high-resolution tactile sensor.

Title for Talk: Towards Digital Touch Sensing

Abstract:  Touch is a crucial sensor modality in both humans and robots. Recent advances in tactile sensing hardware have resulted -- for the first time -- in the availability of mass-produced, high-resolution, inexpensive, and reliable tactile sensors. In this talk, I will talk about the opportunities created by the availability of this hardware, towards the digitalization of touch as a sensing modality. Following, I will argue for the importance of creating a new computational field of "Touch processing" dedicated to the processing and understanding of touch, similarly to what computer vision is for vision. This new field will present significant challenges both in terms of research and engineering, but also new exciting opportunities in a wide range of real-world applications.

Danijela Cabric

(Professor, University of California, Los Angeles)

Bio: Danijela Cabric is Associate Professor in the Electrical and Computer Engineering Department at the University of California, Los Angeles.

Title for Talk: UAV swarm enabled spectrally and energy efficient communications

Abstract:  Multi-UAV deployments create new opportunities for wireless communications. By coordinating the UAVs, they can act as a virtual-antenna-array and use multi antenna communication schemes like distributed MIMO and distributed beamforming (BF). Distributed MIMO enables a swarm of UAVs to transmit multiple data streams simultaneously to a multi-antenna ground station (GS), thus improving the spectral efficiency. Due to the line-of-sight propagation between the swarm and the GS, the MIMO channel is highly correlated, leading to limited multiplexing gains. By optimizing the UAV positions, the swarm can attain the maximum capacity given by the single-user-bound. To achieve this capacity, we propose a centralized approach using block coordinate descent and distributed iterative approach using linear controllers. Distributed BF can extend the communication range of a remotely deployed swarm, avoiding energy waste in travel towards the destination radio. In order to beamform, the UAVs typically rely on the destination feedback, however, noisy feedback degrades the BF gains. To limit the degradation, an analytical framework to predict the BF gains at a given SNR is developed and used to optimize the signaling with the destination. The proposed framework was verified experimentally in the lab and using UAV-mounted software-defined-radios (SDR). We also developed a feedback-free BF approach that eliminates the need for destination feedback entirely in a LOS channel. In this approach, one BF radio acts as a guide and moves to point the beam of the remaining radios towards the destination. This approach tolerates localization error and was demonstrated using SDRs. 

Important Deadlines

Full Paper Submission:23rd August 2023
Acceptance Notification: 10th September 2023
Final Paper Submission:20th September 2023
Early Bird Registration
21st September 2023
Presentation Submission: 30th September 2023
Conference: 12 -14 October 2023

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• Best Paper Award will be given for each track.

• Conference Record no- 54665