Michael McAlpine is a Professor in the Mechanical Engineering department at the University of Minnesota . He also holds affiliations with the Biomedical Engineering and Electrical and Computer Engineering departments. His research focuses on 3D printing functional materials & devices , Nanoscale inks , Biomedical devices , Bioelectronics , and Flexible Microsystems . Research Interests : 3D Printing, Biomedical Engineering, Nanotechnology, Flexible Electronics, Microfluidics Labs : ME 361/363 Contact : mcalpine@umn.edu , (612) 626-3303, ME 117 Recent Research Trends include 3D Printed Biomedical Devices , Flexible Electronics , and Bioprinting Applications . His work spans from Spinal Organoid Formation to Programmable Drug Release Capsules . Scientific Award : Circulation Research 2020 Best Manuscript Award
Dr. Ismail Guvenc is a Full Professor in the Department of Electrical and Computer Engineering at North Carolina State University. He holds an IEEE Fellowship and is a Senior Member of the U.S. National Academy of Inventors. His research focuses on 5G/6G wireless systems, UAV communications, heterogeneous networks, and spectrum management. He leads the NSF AERPAW project and serves as the NC State Site Director for the NSF BWAC I/UCRC center. Dr. Guvenc has authored/co-edited four books and holds over 30 U.S. patents. Education: Ph.D. in Electrical Engineering, University of South Florida (2006) Master's in Electrical and Computer Engineering, University of New Mexico (2003) Bachelor's in Electrical Engineering, Bilkent University, Turkey (2001) Research Interests: Dr. Guvenc’s work spans UAV communication systems, 5G/6G networks, spectrum sensing, and machine learning for network optimization. He emphasizes experimental validation through platforms like AERPAW, focusing on drone-based networks, radio dynamic zones, and testbed-driven innovation. His contributions address challenges in UAV localization, interference management, and energy-efficient networks. Grants & Leadership: PI and Director of NSF AERPAW (2019–Present) NC State Site Director for NSF BWAC I/UCRC (2019–Present) Recipient of NSF CAREER Award (2015) and NC State Faculty Scholar Award (2021) Publications & Awards: He has authored over 130 peer-reviewed papers and contributed to standardization efforts. His awards include the R. Ray Bennett Faculty Fellow Award (2019) and the USF Outstanding Dissertation Award (2006). Labs & Teams: Dr. Guvenc directs research in the ECE’s wireless communications group, leveraging AERPAW’s experimental capabilities to advance UAV networks and 6G technologies. His team collaborates with industry and government on spectrum policy and autonomous systems.
Shiyu Su is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Waterloo. His research focuses on high-speed data converters, wireless transceivers, digital phase-locked loops (PLL), and AI-assisted analog/mixed-signal design automation. He holds a Ph.D. from the University of Southern California (2019) and teaches courses such as ECE 340 (Electronic Circuits 2) and ECE 432 (Radio Frequency Integrated Devices and Circuits). Education: B.S. from Beijing University of Post and Telecommunication (China) and Queen Mary, University of London (UK), 2011; M.S. and Ph.D. from USC, 2013 and 2019, all in electrical engineering. Research Interests: High-speed ADCs/DACs RF/mm-wave transceivers Time-approximation filters (TAF) Analog/mixed-signal design automation Memristor-based computing Biomedical interfaces Key Awards: IEEE SSCS Predoctoral Achievement Award (2017–2018) Best Student Paper Award at IEEE RFIC (2022) Ming Hsieh Institute Scholar (2019–2020) Lab Focus: The Shiyu Su Lab develops integrated circuits for communications, sensing, and computing, with a focus on AI-driven methodologies and digital-analog co-design. Collaborations include work with Prof. Wei Wu (USC) on memristor-based systems.
Marco Di Renzo is a CNRS Research Director (Professor) and Head of the iPhyCom group at the Laboratory of Signals and Systems (L2S) at Paris-Saclay University, France. He is affiliated with both CNRS and CentraleSupélec. His roles include: Member of L2S Management Committee and Board Council Member of the Ph.D. School Admission Committee Academic Vice Chair, ETSI Industry Specification Group on RIS Editorial and leadership roles in IEEE communications journals Education: Laurea (cum laude) and Ph.D. in Electrical Engineering from University of L’Aquila (2003, 2007) Habilitation à Diriger des Recherches from Paris-Saclay University (2013) Research focuses on 6G networks , reconfigurable intelligent surfaces (RIS) , and integrated sensing and communications (ISAC) . He explores electromagnetic theory, machine learning applications, and energy-efficient wireless systems. His work addresses challenges in near-field communications, multi-user MIMO, and holographic beamforming. He advocates for physics-driven design principles in next-gen networks. Key awards include IEEE/IEE Fellowships, the Michel Monpetit Prize, and multiple IEEE Best Paper Awards. He holds international visiting professorships at institutions like the University of Oulu (Finland) and Nanyang Technological University (Singapore). Active in standardization via ETSI ISG RIS and contributes to global initiatives like the ITU 6G Vision. His research bridges theoretical models with practical implementations, emphasizing interdisciplinary collaboration between physics, signal processing, and AI.
Prof. Bruno Clerckx is a Professor of Wireless Communications and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering, Faculty of Engineering. He leads the Communications and Signal Processing Group and the Wireless Communications and Signal Processing Lab. Education: M.Sc. and Ph.D. in Electrical Engineering from Université Catholique de Louvain, Belgium Doctor of Science (DSc) from Imperial College London Research Interests: Focuses on wireless communications and signal processing for next-generation networks, including MIMO systems, reconfigurable intelligent surfaces (RIS), rate-splitting multiple access (RSMA), and integrated sensing and communications (ISAC). His work emphasizes 6G technologies, full-duplex systems, and energy-efficient architectures. Key contributions include pioneering research on beyond-diagonal RIS and RSMA prototyping. Awards: 2021 Blondel Medal (France) 2021 Adolphe Wetrems Prize (Royal Academy of Belgium) Fellowships from IEEE and IET IEEE Communications Society Distinguished Lecturer (2021-2023) Labs & Teams: Heads the Wireless Communications and Signal Processing Lab, and is affiliated with the IEEE Special Interest Groups on RSMA and BD-RIS. Collaborates with global institutions including Stanford University, Tsinghua University, and Samsung Electronics. Industry Experience: Former CTO of Silicon Austria Labs and contributor to 4G/5G standards at Samsung. Holds 80+ patents and authored two books on MIMO systems.
David Gesbert serves as Professor and Director of EURECOM, a leading research institution in Sophia Antipolis, France, specializing in digital sciences. Previously heading the Communications Systems Department, he now directs EURECOM while leading the Foundations & Algorithms research group within the Communication Systems Department. His leadership spans institutional administration and cutting-edge research supervision. Dr. Gesbert's research portfolio demonstrates exceptional depth across communications theory and wireless networking: Communication theory and information theory fundamentals Signal processing for wireless networks with emphasis on robustness Machine learning applications for decentralized network optimization Connected robotics and UAV-enabled flying radio access networks 6G wireless architecture with focus on AI integration Distributed decision making under information uncertainties His publication trajectory reveals a strategic evolution from classical communication theory toward AI-integrated wireless systems, particularly focusing on UAV-aided networks and connected robotics for 6G. Recent work emphasizes learning-based approaches for network optimization under asymmetric information conditions, with growing emphasis on sustainability and green communications. Dr. Gesbert's scientific recognition includes: Fellow of IEEE (2011) and Asia Pacific Artificial Intelligence Association (2021) Thomson-Reuters List of Highly Cited Researchers in Computer Science Multiple Best Paper Awards at IEEE conferences (EW 2017, ICC 2019) ERC Advanced Grant recipient for the PERFUME project on Smart Device Communications 3IA Chair funding for AI for future IoT Networks 2019 Winner of 'Fundamental research project of the year' by French SCS He actively mentors PhD students and researchers, welcoming collaboration in his research domains. His group secures substantial research funding including ERC grants, 3IA Chairs, and participation in major European projects like the WINDMILL ITN Marie Curie project on Machine Learning in Wireless Communications. Dr. Gesbert serves on editorial boards and regularly delivers keynotes at premier international conferences, establishing himself as a thought leader in next-generation wireless communications.
Tariq Iqbal is an Assistant Professor at the University of Virginia , with joint appointments in the Department of Systems and Information Engineering and Department of Computer Science . He leads the Collaborative Robotics Lab (CRL) , specializing in human-robot teams and embodied AI . Previously, he was a Postdoctoral Associate at MIT's CSAIL , advised by Prof. Julie Shah , and earned his Ph.D. in Computer Science from University of California San Diego (UCSD) under Prof. Laurel Riek . Ph.D. in Computer Science, University of California San Diego (2017) M.S. in Computer Science, University of Texas at El Paso (2012) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2007) His research lies at the intersection of artificial intelligence and robotics , focusing on human-robot collaboration in dynamic environments. Key areas include motion prediction , multimodal fusion , trust modeling , and collaborative learning . His work integrates cognitive science and deep learning to enhance robotic fluency in naturalistic settings. Recent publications (2023–2025) highlight advancements in human-robot team dynamics , multimodal dataset creation , and motion prediction algorithms . Notable works include Energy-Based Transformers for scalable AI, PoseTron for motion prediction, and Accessible Navigation Mapping for assistive robotics. These contributions span trust modeling , cloud robotic infrastructure , and safety in close-proximity collaboration . National Science Foundation (NSF) CAREER Award Air Force Office of Scientific Research (AFOSR) Young Investigator Program (YIP) Award Commonwealth Center for Advanced Manufacturing (CCAM) Innovation Award As faculty, he has secured grants from NSF and AFOSR , mentored research students, and taught courses like Stochastic Modeling I (SYS 6005) and Robots and Humans (SYS 4582/6465, ECE 4502/6465, CS 6465) . His prior industry roles at IBM Watson Lab and Grameenphone Ltd. inform his applied research in telecom infrastructure and cognitive robotics . He leads the Collaborative Robotics Lab (CRL) at UVA, which develops multimodal datasets , real-time coordination algorithms , and adaptive pathfinding systems . Current projects explore human motion prediction , team synchrony , and embodied question-answering , reflecting his commitment to advancing human-robot fluency and contextual AI .
Xingwang Li is an active researcher affiliated with the School of Physics and Electronic Information Engineering at Henan Polytechnic University in Jiaozuo, China. He obtained his PhD from Beijing University of Posts and Telecommunications in 2015, specializing in networking and switching technology. His research spans wireless communications, IoT systems, reconfigurable intelligent surfaces (RIS), and physical-layer security, with a strong focus on 6G-enabling technologies. Dr. Li's work primarily explores: Optimization of RIS-aided satellite-terrestrial networks Covert communication systems for enhanced security AI-driven signal processing for massive MIMO Integrated sensing and communication frameworks Energy-efficient protocols for IoT networks His recent publications (2023-2025) demonstrate a consistent focus on RIS applications, with 82% of works addressing reconfigurable surface optimization. Key trends include the integration of deep learning with communication systems (notably reinforcement learning for resource allocation), advancement of THz and near-field technologies for 6G, and novel approaches to physical-layer security. The research shows increasing emphasis on practical implementations, including UAV networks and autonomous vehicle communications.
Ebrahim Bedeer Mohamed is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan. He joined in July 2019, following roles as an Assistant Professor (Lecturer) at Ulster University, UK, and postdoctoral fellowships at Carleton University and the University of British Columbia. He holds a Ph.D. (Distinction) from Memorial University of Newfoundland (2014), with expertise in signal processing and wireless communications. His research focuses on optimizing communication systems through advanced signal processing techniques, including faster-than-Nyquist signaling, IoT network design, AI integration, and energy-efficient protocols. Key areas include next-generation communication networks, non-orthogonal modulation, and MIMO systems. Notable contributions include work on channel estimation for FTN signaling, RIS-aided wireless systems, and LR-FHSS protocols in IoT. His publications span spectral efficiency, interference minimization, and energy management in 5G/6G contexts. He actively seeks Ph.D. students with strong backgrounds in signal processing fundamentals. Awards and grants are not explicitly listed in the provided texts. His work emphasizes practical applications, such as UAV trajectory optimization for IoT data collection and energy-efficient caching strategies in dynamic networks.
Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.
Truong Q. Nguyen is a Professor in the Electrical and Computer Engineering (ECE) Department at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds positions at the Center for Wireless Communications and the California Institute for Telecommunications and Information Technology. His research focuses on image/video processing, wavelets, 3D video technology, and applications in healthcare and robotics. He has authored influential textbooks like Wavelets & Filter Banks and pioneered low-power video processing algorithms for mobile devices. Nguyen earned his B.S., M.S., and Ph.D. in Electrical Engineering from the California Institute of Technology (1985–1989). He held roles at MIT Lincoln Laboratory and Boston University before joining UCSD in 1998. His honors include the IEEE Signal Processing Paper Award (1992), NSF Career Award (1995), IEEE Fellow (2005), and UCSD’s Distinguished Teaching Award (2019). His research interests span 3D video processing, machine learning for health monitoring, and biomedical imaging. Notable contributions include wavelet-based compression techniques and AI-driven medical image analysis. He leads the UCSD Video Processing Lab, exploring computer vision, robotics, and generative AI applications. Nguyen is committed to educational innovation, co-creating programs like the Hands-on Curriculum, Summer Research Internship Program (SRIP), and Project-in-a-Box (PIB) for K-12 students. Nguyen’s work bridges academia and industry, with patents in wavelet design and signal analysis. Recent projects include NSF-funded initiatives to develop inclusive engineering curricula and collaborate on graduate pathways programs through the Inclusive Engineering Consortium (IEC).
Marco Di Renzo is a CNRS Professor (Directeur de Recherche Titulaire) at University of Paris-Saclay, affiliated with CentraleSupelec and the Signals and Systems Laboratory (L2S). He serves as Coordinator of the Communications Networks Area at the DigiCosme Laboratory of Excellence and Editor-in-Chief of IEEE Communications Letters. His academic leadership includes membership in the Ph.D. School on ICT Admission Committee at Paris-Saclay University. His educational background includes a Laurea (cum laude) and Ph.D. in Electrical Engineering from University of L'Aquila, Italy (2003, 2007), and a Habilitation à Diriger des Recherches from University Paris-Sud (2013). Laurea (cum laude), Electrical Engineering, University of L'Aquila (2003) Ph.D., Electrical Engineering, University of L'Aquila (2007) Habilitation à Diriger des Recherches, University Paris-Sud (2013) Di Renzo's research focuses on next-generation wireless communications, particularly reconfigurable intelligent surfaces (RIS), 6G technologies, and stochastic geometry modeling. His work bridges theoretical communication theory with practical implementations in cellular networks, millimeter-wave communications, and ultra-wide band systems. Recent publications demonstrate leadership in holographic metasurfaces, integrated sensing and communication (ISAC), and AI-empowered network design, establishing him as a pioneer in electromagnetic wave manipulation for future networks. His award-winning publications span RIS-aided communications, channel modeling, and security frameworks. Analysis of his recent work reveals consistent focus on three pillars: (1) fundamental electromagnetic theory for wave manipulation, (2) practical RIS implementations across frequency bands, and (3) integration with AI for network optimization. His articles frequently address industrial applications including factory automation and space-air-ground networks. Di Renzo's scientific recognition includes: IEEE Fellow (2020) and IET Fellow (2020) Highly Cited Researcher (Web of Science, 2019) SEE-IEEE Alain Glavieux Award (2017) Multiple Best Paper Awards (IEEE ICC, EURASIP) Nokia Foundation Visiting Professorship (2020) As Principal Investigator for CNRS, he coordinates multiple Horizon 2020 projects including SURFER, PathFinder, and MetaWireless. His leadership extends to serving as Project Coordinator for H2020 5Gwireless, 5Gaura, MAPNET, and REDESIGN. With over 350 publications, 17,000+ citations, and h-index of 66+, his research group maintains strong industry partnerships with Nokia and other telecommunications leaders. Di Renzo directs the Signals and Systems Laboratory (L2S) at Paris-Saclay and coordinates the DigiCosme Excellence Lab's Communications Networks Area. His team specializes in electromagnetic modeling for wireless networks and has pioneered the European Telecommunications Standards Institute (ETSI) Industry Specification Group on RIS. The group maintains active collaborations with Aalto University (Finland), University of Technology Sydney (Australia), and University of L'Aquila (Italy).
Arie E. Kaufman is a Distinguished Professor in the Department of Computer Science at Stony Brook University, serving as Chief Scientist of the Center of Excellence in Wireless and Information Technology (CEWIT) and Director of the Center of Visual Computing (CVC). He additionally holds a Distinguished Professorship in Radiology, with a 40+ year career at Stony Brook since joining in 1985 and chairing the CS department from 1999-2009. His seminal research spans computer graphics, visualization, and virtual reality with biomedical applications, pioneering breakthroughs including 3D Virtual Colonoscopy (FDA-approved colon cancer screening), Cube hardware architectures (commercialized as VolumePro), the Reality Deck (1.5 billion-pixel immersive display), and foundational work in volume visualization. His interests focus on real-time rendering, medical imaging, and immersive analytics, with recent work integrating machine learning for healthcare and environmental risk visualization. Recent publications demonstrate continued leadership in high-resolution immersive displays (Silo), XR analytics with LLMs, storm surge visualization, and neural reconstruction techniques. His work bridges theoretical innovation with practical applications, particularly in pancreatic cancer prognosis and disaster preparedness. Major honors include: IEEE Visualization Career Award (2005) Fellow of the National Academy of Inventors (2017) ACM Fellow (2009) IEEE Fellow (1998) Long Island Technology Hall of Fame (2013) European Academy of Sciences membership (2002) As PI on 100+ research grants, Kaufman's work has generated 300+ refereed papers, 40+ patents, and extensive media coverage (New York Times, Science, Wall Street Journal). He leads the Center of Visual Computing with focus on translational research, including VolVis software (5,000+ installations) and Reality Deck deployments for big data analytics. His lab develops cutting-edge visualization infrastructure for medical diagnostics and environmental modeling, with current projects advancing immersive storm surge analytics, neural structure extraction, and VR-based risk communication systems. Future work emphasizes AI-enhanced visualization for precision medicine and climate resilience planning.
Renny Edwin Fernandez is an Associate Professor in the Department of Engineering at Norfolk State University's College of Science, Engineering and Technology. His multidisciplinary research focuses on microsensing platforms for healthcare, pollution control, and agriculture applications. Education: PhD in Electrical Engineering (2010) from Indian Institute of Technology Madras His research integrates microfabrication, microfluidics, and machine learning to develop wearable biosensors, disposable electrodes, and IoT-enabled soil monitoring systems. Key trends in his recent publications include: Real-time health monitoring via flexible nanosensors Machine learning integration in agricultural IoT Plasma-aided printing of conductive nanomaterials Smart PPE systems with NFC technology Scientific Awards: Research Initiation Award (2020) for cognitive monitoring systems in extreme environments Dr. Fernandez mentors graduate and undergraduate researchers at NSU, with prior teaching experience at University of Indianapolis and Florida International University. He holds a patent for biosensor technology and has developed innovative solutions for: Salivary cortisol detection Soil nutrient analysis Cell viability assessment Smart irrigation systems
Simon Yang is a Professor in the School of Engineering at the University of Guelph, part of the College of Engineering and Physical Sciences. His research focuses on artificial intelligence, robotics, sensors, control systems, and bio-inspired intelligence. He has contributed to advanced robotics applications, including mobile robot navigation, underwater vehicle control, and agricultural automation. Dr. Yang holds editorial roles for journals such as the International Journal of Robotics and Automation and IEEE Transactions on Cybernetics . His work bridges theoretical advancements with practical implementations in areas like sensor networks, machine learning, and multi-agent systems. Recent projects include developing robust control frameworks for autonomous systems, digital twin applications, and bio-inspired neural network algorithms. His research emphasizes real-world challenges in robotics, environmental monitoring, and precision agriculture, with a focus on integrating AI-driven solutions for enhanced decision-making and system reliability. Professional contributions include advisory roles in multiple journals and conference committees, reflecting his leadership in the field.
