Keynote and Plenary Sessions Speakers

MONCEF GABBOUJ received his BS degree in 1985 from Oklahoma State University, and his MS and PhD degrees from Purdue University, in 1986 and 1989, respectively, all in electrical engineering. Dr. Gabbouj is a Professor of Information Technology at the Department of Computing Sciences, Tampere University, Tampere, Finland. He was Academy of Finland Professor during 2011-2015. His research interests include Big Data analytics, multimedia content-based analysis, indexing and retrieval, artificial intelligence, machine learning, pattern recognition, nonlinear signal and image processing and analysis, voice conversion, and video processing and coding. Dr. Gabbouj is a Fellow of the IEEE and member of the Academia Europaea and the Finnish Academy of Science and Letters. He is the past Chairman of the IEEE CAS TC on DSP and committee member of the IEEE Fourier Award for Signal Processing. He served as associate editor and guest editor of many IEEE, and international journals and Distinguished Lecturer for the IEEE CASS. Dr. Gabbouj served as General Co-Chair of IEEE ISCAS 2019, ICIP 2020, ICIP 2024 and ICME 2021. Gabbouj is Finland Site Director of the USA NSF IUCRC funded Center for Visual and Decision Informatics (CVDI) and led the Artificial Intelligence Research Task Force of Finland’s Ministry of Economic Affairs and Employment funded Research Alliance on Autonomous Systems (RAAS).
Prof. Moncef Gabbouj (PLENARY SPEAKER)
Tampere, University, Tampere, Finland

The Super Neuron Model – A new generation of ANN-based Machine Learning and Applications

Operational Neural Networks (ONNs) are new generation network models targeting to address two major drawbacks of conventional Convolutional Neural Networks (CNNs): the homogenous network configuration and the “linear” neuron model that can only perform linear transformations over previous layer outputs. ONNs can perform any linear or non-linear transformation with a proper combination of “nodal” and “pool” operators. This is a great leap towards expanding the neuron’s learning capacity in CNNs, which thus far required the use of a single nodal operator for all synaptic connections for each neuron. This restriction has recently been lifted by introducing a superior neuron called the “generative neuron” where each nodal operator can be customized during the training in order to maximize learning. As a result, the network is able to self-organize the nodal operators of its neurons’ connections. Self-Organized ONNs (Self-ONNs) equipped with superior generative neurons can achieve diversity even with a compact configuration. We shall explore several signal processing applications of neural network models equipped with the superior neuron.
Jorge Rodas (S'08--M'12--SM'19) was born in Asuncion, Paraguay, in 1984. He received his Engineer degree in electronic engineering from the Universidad Nacional de Asuncion (UNA), Paraguay, in 2009. He received his M.Sc. degrees from the Universidad de Vigo, Spain, in 2012 and from the Universidad de Sevilla, Spain, in 2013, and his joint-university Ph.D. degree between the Universidad Nacional de Asunción and the Universidad de Sevilla in 2016.   In 2011, he joined the Laboratory of Power and Control Systems, Faculty of Engineering, Universidad Nacional de Asuncion, where he currently serves as a Professor and Research Director. He was visiting professor at the Power Electronics and Industrial Control Research Group of École de Technologie Supérieure (Montreal, Canada) and the Center of Technological Innovation in Static Converters and Drives (CITCEA) of Polytechnic University of Catalonia, (Barcelona, Spain), in 2017 and 2022, respectively. He serves as an Associate Editor of Elsevier Alexandria Engineering Journal (AEJ) and Guest Editor in the IEEE Journal of Emerging and Selected Topics in Power Electronics, and MDPI World Electric Vehicle Journal. He served as Guest Editor of MDPI Energies and frontiers in Energy Research. In 2020, Prof. Rodas received the Paraguayan National Science Award. He has authored and co-authored two book chapters and more than 100 journal and conference papers. His research interest focuses on applications of advanced control to real-world problems. Current research activities include applying finite control set model predictive control and nonlinear control to power electronic converters, renewable energy conversion systems, electric motor drives, and robotic systems (especially unmanned aerial vehicles).
PROF. JORGE RODAS (Keynote Speaker_ SAC)
National University of Asunción

Finite-Control-Set Model Predictive Control Techniques of Multiphase Electric Drives

With more than three phases, multiphase machines recently captured high-power, high-reliability applications such as electric vehicles, ship propulsion and wind energy conversion systems. Its innate fault-tolerant ability without needing extra hardware is still considered its most practical benefit. Moreover, its additional degrees of freedom opened the window for miscellaneous nontraditional objectives at the expense of the need for more advanced control strategies. For that reason, numerous papers are now available regarding implementing control techniques for multiphase machines, moving towards classic control techniques: field-oriented control and direct torque control,  to more sophisticated ones: sliding mode control and finite-control-set model predictive control (FCS-MPC). Thus, this presentation discusses the latest developments in FCS-MPC of two of the most popular multiphase electric drive configurations, five-phase and six-phase.
Kaan Ozbay joined Civil and Urban Engineering at NYU Tandon School of Engineering and Center for Urban Science and Progress (CUSP) as a tenured full Professor at in 2013.  He is currently the founding Director of the C2SMART Center (Tier 1 UTC funded by USDOT).  Prior to that Professor Ozbay was a tenured full Professor at Rutgers University’s Department of Civil and Environmental Engineering where he joined as an Assistant Professor in July 1996. In 2008, he was a visiting scholar at the Operations Research and Financial Engineering (ORFE) Department at, Princeton University. Dr. Ozbay is the recipient of the prestigious National Science Foundation (NSF) CAREER award. His research interests in transportation cover a wide range of topics including data analytics for advanced technology and sensing applications in smart cities, development of simulation models of large scale complex transportation systems, modeling and evaluation of traffic incident and emergency management, feedback based on-line real-time traffic control techniques, traffic safety, application of operations research techniques in network optimization and humanitarian inventory control, and transportation economics. He has co-authored 4 books and published approximately 400 refereed papers in scholarly journals and conference proceedings. Prof. Ozbay is also an Associate Editor of the ITS journal. Dr. Ozbay is the co-editor of a book titled “Dynamic Traffic Control & Guidance” published by Springer Verlag’s "Complex Social, Economic and Engineered Networks" series in 2013. He also serves as the Associate Editor of Networks and Spatial Economic journal and Transportmetrica B: Transportation Dynamics journal. Since 1994, Dr. Ozbay, has been the Principal Investigator and Co-Principal Investigator of more than 100 projects funded at a level of more than $25,00,000 by National Science Foundation, NJDOT, NYMTC, NY State DOT, New Jersey Highway Authority, USDOT, FHWA, VDOT, CUNY University Transportation Research Center (UTRC), Department of Homeland Security, USDOT ITS Research Center of Excellence.
Department of Civil and Urban Engineering & C2SMART Center Tandon School of Engineering New York University, U.S.A.

Traffic Control for Efficient and Safe Transportation Systems in the Era of Connected and Automated Vehicles 

In the last several years,  there have been number of novel approaches to improve traffic operations and safety especially in highly congested urban areas.  Most of the innovation in these research and deployment efforts are fueled by the advances in connected & autonomous vehicles (CAV) as well as ubiquitous mobile devices, sensors and cameras deployed throughout these urban areas. In this talk, we will first provide a high-level state of the traffic control research and deployment as a result of advances in connected and automated vehicles as well as V2X communication technologies.  We will then discuss research on pro-active traffic management and control approaches with a focus on operations and safety in tandem.  A case study from the recently completed NY City connected vehicle pilot test where NY University C2SMART researchers participated as one of the University partners will be presented to describe the “soft control” approach in terms of in-vehicle warnings given to drivers. Second, the role and importance of a real-world cyber-physical test bed in these research efforts will be discussed using a learning-based headway control algorithm for transit buses tested in microscopic simulation. The talk will be concluded with a discussion of opportunities and challenges in traffic control research, development, and deployment in complex urban environments such as NYC and Washington D.C.
Ahmed Chemori received his M.Sc. and Ph.D. degrees, both in automatic control from the Polytechnic Institute of Grenoble, France, in 2001 and 2005 respectively. During the year 2004/2005 he has been a Research and Teaching Assistant at Laboratoire de Signaux et Systèmes (LSS - Centrale Supelec) and University Paris 11. Then he joined Gipsa-Lab (Former LAG) as a CNRS postdoctoral researcher. He is currently a senior researcher in Automatic Control and Robotics for the French National Center for Scientific Research (CNRS), at the Montpellier Laboratory of Computer Science, Robotics and Microelectronics (LIRMM). His research interests include nonlinear (robust, adaptive, and predictive) control and their real-time applications in different fields of robotics (underactuated robotics, parallel robotics, underwater robotics, humanoid robotics, and wearable robotics). He is the author of more than 150 scientific publications, including international journals, patents, books, book chapters, and international conferences. He served as Associate Editor for various journals. He has been a TPC/IPC member and associate editor for different international conferences. He organized different scientific events, including summer schools, workshops, and international conferences. He has also delivered various plenary/keynote lectures at international conferences.
Prof. Ahmed Chemori (plenary speaker)
LIRMM, University of Montpellier, CNRS Montpellier, France

Recent Advances in Motion Control of Parallel Robots for High-Speed Industrial Applications

Serial robotic manipulators consist of a set of sequentially connected links, forming an open kinematic chain. These robots are mainly characterized by their large workspace and their high dexterity. However, despite these advantages, in order to perform tasks requiring high speeds/accelerations and/or high precision; such robots are not always recommended because of their lack of stiffness and accuracy. Indeed, parallel kinematic manipulators (PKMs) are more suitable for such tasks. The main idea of their mechanical structure consists in using at least two kinematic chains linking the fixed base to the travelling plate, where each of these chains contains at least one actuator. This may allow a good distribution of the load between the chains. PKMs have important advantages with respect to their serial counterparts in terms of stiffness, speed, accuracy and payload. However, these robots are characterized by their high nonlinear dynamics, kinematic redundancy, uncertainties, actuation redundancy, singularities, etc. Besides, when interested in high-speed robotized repetitive tasks, such as food packaging and waste sorting applications, the key idea lies in looking for short cycle times. This means obviously to look for short motion and short stabilization times while guaranteeing the robustness and performance with respect to disturbances and changes/uncertainties in the operational conditions. Consequently, if we are interested in control of such robots, all these issues should be taken into account, which makes it a bit challenging task. This talk will give an overview of some proposed advanced control solutions for high-speed industrial applications of PKMs in food packaging, waste sorting, and machining tasks. The proposed solutions are mainly borrowed from nonlinear robust and adaptive control techniques and have been validated through real-time experiments on different PKM prototypes.
Dr Ashammakhi is leading translational research in biomaterials and tissue engineering with focus on advancing frontiers that integrate fields of science to develop microphysiological systems (MPSs), three-dimensional bioprinting (3DBP), and smart implants for disease modeling, tissue repair and regenerative medicine, leveraging his long-standing experience with biodegradable biomaterials, regenerative therapeutics and drug releasing devices. Previously, he led the development of innovative multifunctional drug-releasing biodegradable implants for reparative and regenerative medicine. He assumed various leading positions in different Universities before he relocated to the University of California Los Angeles (UCLA), as a Full Professor of Biomaterials Technology and Vice Director of the Institute of Biomaterials at Tampere University of Technology, Finland, Chair (Full Professor) of Regenerative Medicine at Keele University, UK and Adjunct Professor at Oulu University, Finland. At UCLA Dr. Ashammakhi has been an Associate Director of the Center for Minimally Invasive Therapeutics (C-MIT), Associate Adjunct Professor, Department of Radiological Sciences, David Geffen School of Medicine, UCLA, and Affiliate Faculty at the Department of Biomedical Engineering, Henry Samueli School of Engineering, UCLA. In Michigan State University, he has been an Associate Adjunct Professor, Department of Biomedical Engineering and Institute for Quantitative Health Science and Engineering (IQ), where he is currently a Senior Specialist. He has been leading various externally funded collaborative multidisciplinary projects in multifunctional biomaterials, tissue engineering, 3DBP and MPS technology (Funds from the NIH, AHA, EU and Industry). He has published hundreds of papers, patent/patent applications, book chapters and conference abstracts (H Index = 54) and succeeded in cofounding a startup and translating technologies to the clinic. To advance the field, he has been leading special sessions and panels in major conferences on translational 3DBP and MPS technologies. For up-to-date and relevant publications, see: Mail: Institute for Quantitative Health Science and Engineering (IQ), Department of Biomedical Engineering, College of Engineering, Michigan State University, 775 Woodlot Dr., East Lansing, MI 48824. Email:,
Prof. Nureddin Ashammakhi (Plenary Speaker)
Department of Biomedical Engineering and Institute for Quantitative Health Science and Engineering (IQ), Michigan State University, Michigan, USA

Integrating technologies to develop smarter implants and improved health care

Biomaterials have evolved from inert to biodegradable, bioactive and multifunctional. With advances in innovative processing techniques, it was possible to build strong yet biodegradable multifunctional implants that made an impact on clinical care of many patients worldwide. Advances in tissue engineering made it possible to accommodate cells in biodegradable scaffolds and develop living implants. To mimic tissue structure, nanofiber-based constructs were then developed. With the advent of three-dimensional (3D) bioprinting, cell containing bioinks were developed and control over cell distribution in engineered tissue constructs was achieved. To further leverage the advantages biodegradable materials offer, biodegradable sensors were developed to allow temporary monitoring of certain functions and parameters in the body. Further, it was possible to develop sensor-integrating implants that can sense changes in their microenvironment before these changes evolve into irreversible problems that lead to implant failure and necessitate surgical removal. There are already major developments that include developing electroconductive, self-healing and four-dimensional (4D) biomaterials. In future, combined approaches and technologies merge will enable the development of implants with self-awareness, actuation, self-correction/healing, and behavior mimicking that of native tissues.
Mohsen Guizani (Fellow, IEEE) received the BS (with distinction), MS and PhD degrees in Electrical and Computer engineering from Syracuse University, Syracuse, NY, USA in 1985, 1987 and 1990, respectively. He is currently a Professor of Machine Learning and the Associate Provost at Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE. Previously, he worked in different institutions in the USA. His research interests include applied machine learning and artificial intelligence, Internet of Things (IoT), intelligent autonomous systems, smart city, and cybersecurity. He was elevated to the IEEE Fellow in 2009 and was listed as a Clarivate Analytics Highly Cited Researcher in Computer Science in 2019, 2020 and 2021. Dr. Guizani has won several research awards including the “2015 IEEE Communications Society Best Survey Paper Award”, the Best ComSoc Journal Paper Award in 2021 as well five Best Paper Awards from ICC and Globecom Conferences. He is the author of ten books and more than 800 publications. He is also the recipient of the 2017 IEEE Communications Society Wireless Technical Committee (WTC) Recognition Award, the 2018 AdHoc Technical Committee Recognition Award, and the 2019 IEEE Communications and Information Security Technical Recognition (CISTC) Award. He served as the Editor-in-Chief of IEEE Network and is currently serving on the Editorial Boards of many IEEE Transactions and Magazines. He was the Chair of the IEEE Communications Society Wireless Technical Committee and the Chair of the TAOS Technical Committee. He served as the IEEE Computer Society Distinguished Speaker and is currently the IEEE ComSoc Distinguished Lecturer.
Prof. Mohsen Guizani (plenary speaker)
Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi, UAE.

Smart City Applications with Pervasive AI

Abstract: Internet of Things (IoT) systems have expanded the role of Artificial intelligence (AI) in many applications. On the other hand, AI has witnessed a substantial usage in different IoT applications and services, spanning the smart city systems and speech processing applications to robotics control and military surveillance. This is driven by the easier access to sensed data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams. Designing accurate models using such data streams to predict future insights and revolutionize the decision-taking process, inaugurates pervasive AI systems as a worthy paradigm to achieve better predictions which can lead to a better quality-of-life. The confluence of pervasive computing and artificial intelligence (Pervasive AI) expanded the role of ubiquitous IoT systems from mainly data collection to executing distributed computations with a promising alternative to centralized learning, presenting various challenges, including privacy concerns, scalability, and latency requirements. In this context, a wise cooperation and resource scheduling should be envisaged for a smart city using IoT devices (e.g., smartphones, smart healthcare, and smart vehicles) and infrastructure (e.g., edge nodes and base stations) to avoid communication and computation overheads and ensure maximum performance and efficient accuracy. In this talk, a quick review of the recent techniques and strategies developed to overcome these resource challenges in pervasive AI systems will be given. Specifically, a description of pervasive computing, its architecture, and its intersection with artificial intelligence is presented. Then, we review the background, applications, and performance metrics of AI, particularly Federated Learning (FL), running in a ubiquitous system. Next, we present some communication-efficient techniques of distributed inference, training and learning tasks across a plethora of IoT devices, edge devices and cloud servers. Finally, we discuss future directions in this area and provide some research challenges.