Eric Grivel is a Professor at the University of Bordeaux affiliated with the IMS Bordeaux (Integration Laboratory from Materials to Systems). His research focuses on Signal and Image Processing Spectral Analysis Stochastic Process Modeling His work spans theoretical contributions to signal processing and practical applications in radar systems and biomedical signal analysis. Key trends in his recent publications include Optimization of Detrended Fluctuation Analysis (DFA) for Hurst exponent estimation Development of divergence metrics for comparing ARMA and Gaussian processes Waveform design in MIMO OFDM DFRC (Dual Function Radar-Communication) systems Integration of AI tools like ChatGPT in educational signal processing projects Collaborations and industrial partnerships evident in his publications involve institutions such as Indian Institute of Science Thales Airborne Systems STMicroelectronics CEA Leti Slb (Schlumberger)
Giorgio Grisetti is a Full Professor at Sapienza University of Rome within the Department of Systems and Computer Science, maintaining active research roles in the RoCoCo lab at Sapienza since November 2010 and the Autonomous Intelligent Systems Lab at Freiburg University where he previously served as a Post Doc under Wolfram Burgard starting in 2006. His educational background includes a M.Sc. in Computer Engineering from the University of Rome (2001) and a Ph.D. from Sapienza University of Rome's Intelligent Systems Lab (2006), supervised by Daniele Nardi. His doctoral thesis focused on SLAM using Rao-Blackwellized particle filters. Dr. Grisetti's research centers on mobile robotics with emphasis on robust solutions for autonomous navigation systems. His work spans theoretical and practical advancements in Simultaneous Localization and Mapping (SLAM), robot localization, path planning, and sensor fusion, particularly leveraging LiDAR and multi-sensor configurations. Recent publications demonstrate strong focus on optimization techniques, sensor calibration, and real-time performance for autonomous systems operating in complex environments. His publication trends reveal deep specialization in LiDAR-based SLAM (7 of 15 recent articles), bundle adjustment methods (4 articles), and sensor calibration/perception (3 articles), with consistent contributions to top robotics venues like IEEE Robotics and Automation Letters and ICRA. Key recognitions include: Nomination for the best IROS paper award (2010) Open Source achievement award from Willow Garage (2010) Best paper award at the International Conference and Exhibition on Unmanned Areal Vehicles (2010) Best Paper award at ICRA 2009 (2009) His research is conducted through the RoCoCo lab at Sapienza University of Rome and the Autonomous Intelligent Systems Lab at Freiburg University, focusing on developing foundational algorithms for mobile robot autonomy. Current projects emphasize robust perception systems, optimization frameworks for sensor fusion, and practical implementations for real-world navigation challenges.
Prof. Dr. Amelie Hagelauer holds a professorship in Micro- and Nanosystem Technology at the TUM School of Computation, Information and Technology, Technical University of Munich. Her work focuses on advanced electronics and systems integration across quantum computing hardware, resistive memory technologies, and high-frequency RF systems. She has contributed to innovations in superconducting qubit readout architectures, multi-level RRAM designs, and 3D-integrated CMOS-compatible quantum devices. Research interests span quantum hardware design, nanoelectronic devices, RF front-end systems, and emerging memory technologies. Her work emphasizes practical implementation challenges such as low-power operation, high-voltage handling in RF switches, and wafer-scale fabrication processes. Recent projects include D-band radar systems, energy-efficient 60 GHz transceivers, and antenna tuning solutions for 5G applications. Publications from 2023-2025 showcase advancements in resistive switching device characterization, mitigation of TLS losses in superconducting qubits, and reconfigurable AI accelerators using RRAM-based digital twins. Her work bridges theoretical device physics with practical integrated circuit design, addressing scalability and reliability in next-gen electronics. Awards and grants: None explicitly listed in provided texts. Active collaborations include EU-funded projects on quantum computing platforms and TUM's Electronic Photonic Integration initiatives. Leads research teams in microsystem technology with emphasis on cross-disciplinary approaches combining CMOS processes, MEMS, and quantum engineering.
Christian Enz is a Full Professor at École Polytechnique Fédérale de Lausanne (EPFL), where he serves as Director of the Institute of Microengineering and Head of the Integrated Circuits Laboratory. With M.S. and Ph.D. degrees in electrical engineering from EPFL (1984 and 1989), he has established himself as a leading researcher in low-power analog circuit design and semiconductor device modeling. His research interests focus on very low-power analog and RF IC design , semiconductor device modeling , and increasingly on cryogenic electronics for quantum computing applications . Professor Enz is particularly known for his work on FDSOI MOSFET behavior at cryogenic temperatures, developing comprehensive models that address challenges in subthreshold swing saturation, threshold voltage shifts, and self-heating effects. As a Life Fellow of IEEE with 282 publications and over 7,400 citations, Professor Enz has made significant contributions to the field. His recent work demonstrates how the $G_{m}/I_{D}$ design methodology remains effective in advanced technology nodes and can be extended to cryogenic temperature operation. His research bridges fundamental semiconductor physics with practical circuit design considerations for quantum computing interfaces. Life Fellow, IEEE Director of the Institute of Microengineering, EPFL Head of the Integrated Circuits Laboratory 282 publications with 7,400+ citations Specialist in cryogenic CMOS for quantum computing Professor Enz's work on cryogenic electronics addresses critical challenges for quantum computing scalability. By developing accurate models for transistor behavior at temperatures as low as 3.3K, his research enables the design of specialized control electronics that can operate inside dilution refrigerators, potentially solving major wiring constraints that currently limit quantum computer scaling. His laboratory continues to advance the understanding of semiconductor device physics at cryogenic temperatures while developing practical circuit design methodologies for this emerging application domain.
Ivan Selesnick is a Professor of Electrical and Computer Engineering at the NYU Tandon School of Engineering, with joint appointments in Biomedical Engineering and Radiology. He holds affiliations with the Center for Advanced Technology in Telecommunications (CATT) and leads the Selesnick Lab. His research focuses on signal and image processing, sparse signal models, wavelet analysis, and biomedical applications. He received his degrees from Rice University (BS, MEE, PhD in EE) and has been recognized with prestigious awards including the Alexander von Humboldt Fellowship (1997), NSF Career Award (1999), and IEEE Fellow (2016). Education: BS, MEE, and PhD in Electrical Engineering from Rice University (1990, 1991, 1996). He joined NYU Tandon in 1997 and served as a visiting professor at the University of Erlangen-Nuremberg in 1997. Research Interests: Signal Processing, Sparse Signal Models, Wavelet Analysis, Biomedical Signal Processing, and Optimization Techniques. His work emphasizes applications in medicine, imaging, and engineering systems. Awards: In addition to his fellowships, he received the Jacobs Excellence in Education Award (2003) and the Budd Award for Best Engineering Thesis (1996). He has held editorial roles at IEEE Transactions on Image Processing, Signal Processing Letters, and Computational Imaging. Teaching: Courses include Signals, Systems, and Transforms (EE 3054), Digital Signal Processing I/II (EL 6113/EL 7133), Wavelets and Filter Banks (EL 7163), and Biomedical Signal Processing (EL 9133). Labs and Affiliations: Director of the Selesnick Lab, involved in NYU Tandon Future Labs (business incubators) and CATT (telecommunications research). His research spans biomedical sensing, radar signal processing, and algorithm development for medical diagnostics.
Benjamin C. Flores is a Professor of Electrical and Computer Engineering at the University of Texas at El Paso (UTEP) , where he has built an internationally recognized career spanning advanced radar signal processing and large-scale STEM education initiatives. He directs the UT System Louis Stokes Alliance for Minority Participation (LSAMP) and the Bridge to the Doctorate Program , managing more than $40 million in funded projects aimed at increasing access and success for Hispanic and other under-represented students in STEM disciplines. Education: While specific degrees are not listed in the provided text, Dr. Flores’s faculty appointment and extensive technical expertise in radar and chaotic systems imply advanced training in electrical engineering. Research Interests: Radar & Signal Processing: high-resolution radar, inverse synthetic aperture radar (ISAR), range-Doppler processing, micro-Doppler analysis, bistatic radar, chaotic wideband signal design, neural-network-based classification of radar jamming signals. Antenna Engineering: fractal antennas, 3-D printed antenna prototyping, anechoic chamber measurements. STEM Education & Diversity: evidence-based retention strategies for non-traditional and Hispanic students, peer-led team learning, graduate mentoring, systemic change models for faculty diversity. Publication Trends: Dr. Flores’s recent articles (2021-2025) reveal two dominant thrusts—(1) cutting-edge radar/chaotic signal processing and joint radar-communication systems, and (2) rigorous, data-driven studies on broadening participation in STEM, with emphasis on mentoring, social networks, and program evaluation at Hispanic-Serving Institutions. Scientific Awards & Honors: Texas STAR Award – Texas Higher Education Coordinating Board (2005) ABET President’s Diversity Award (2006) Presidential Award for Excellence in Science, Mathematics, and Engineering Mentorship (2010) Grants & Leadership Roles: Principal Investigator & Project Director, Model Institutions for Excellence Initiative (1999-2007) Principal Investigator, UTEP PUENTES Program (US Dept. of Education, 2010-2015) Principal Investigator & Director, UT System LSAMP & Bridge to the Doctorate Program (since 2005) Laboratory & Facilities: Dr. Flores’s research group utilizes UTEP’s anechoic chamber and rapid-prototyping laboratories for antenna design and characterization, while also housing real-time radar test-beds and analog-computer platforms for chaotic oscillator experiments.
Professor Jinho Choi is a Chair and Professor in Radio Frequency at the School of Electrical and Mechanical Engineering, University of Adelaide, Australia. He holds a B.E. (magna cum laude) from Sogang University, and M.S.E. and Ph.D. degrees from KAIST. His research focuses on advancing wireless communication and sensing technologies, particularly in IoT, 5G/6G, non-terrestrial networks, and cognitive satellite systems. He authored three books and has been recognized with the 1999 EURASIP Best Paper Award, IEEE Fellowship, and inclusion in Stanford's Top 2% Scientists list since 2020. He currently serves as a Senior Editor of IEEE Wireless Communications Letters and editorial roles in multiple journals. Education: B.E. (Electronics Engineering) - Sogang University, Seoul (1989) M.S.E. (Electrical Engineering) - KAIST (1991) Ph.D. (Electrical Engineering) - KAIST (1994) Research Interests: Professor Choi's work addresses connectivity challenges in non-terrestrial networks, leveraging statistical signal processing and machine learning. Current projects include UAV-assisted LEO satellite technologies, cognitive satellite radios, and semantic communication protocols. His research aims to enhance global connectivity and efficiency in terrestrial and satellite networks. Publications: His recent work spans semantic communication, satellite quantum key distribution, federated learning optimization, and coverage diversity in mega constellations. These studies reflect trends in 6G-ready technologies, AI-driven communication systems, and hybrid satellite-terrestrial networks. Awards: 1999 Best Paper Award for Signal Processing (EURASIP) IEEE Fellow (Leadership in technical excellence) World’s Top 2% Scientists (Stanford University, 2020–present) Grants & Supervision: As a senior academic, he oversees grants in wireless innovation and has advised numerous students on advanced communication systems. His lab focuses on next-generation networks, integrating theoretical insights with practical implementations. Labs/Teams: Active in interdisciplinary teams at the University of Adelaide, collaborating on projects funded by industry and government to bridge gaps between academic research and real-world applications.
Professor Benjamin Eggleton is a distinguished academic and researcher at the University of Sydney, where he holds the position of Professor of Optical Physics and serves as Pro-Vice-Chancellor (Research). He is also the co-Director of the NSW Smart Sensing Network (NSSN) and has been instrumental in establishing major research centers including the University of Sydney Nano Institute (Sydney Nano, 2018-2022), CUDOS (ARC Centre of Excellence for Ultrahigh bandwidth Devices for Optical Systems, 2003-2017), and Sydney's Institute of Photonics and Optical Science (IPOS, 2009-2018). Professor Eggleton's research spans fundamental to applied science, with pioneering contributions to nonlinear optics and all-optical signal processing. His work focuses on the nonlinear optics of periodic media, slow-light in photonic crystals, ultrafast planar waveguide nonlinear optics, and chalcogenide glasses for telecommunications applications. His research aligns with the Faculty of Science Research Strengths in Fundamental Laws of Nature, Communication Technologies, and National Security. Key projects include Stimulated Brillouin Scattering in photonic chips, Quantum integrated photonics, All optical and non-linear signal processing, Mid-Infrared Photonics, and Air quality sensing using photonic sensors. His publication record is extensive, with over 500 journal publications (42,000 citations, h-index of 113 on Google Scholar). His recent work demonstrates a strong trend toward integrated photonics, particularly in chip-based microwave photonics, quantum applications, and biomedical sensing technologies. There's a clear emphasis on practical implementation of fundamental optical phenomena for real-world applications in communications, sensing, and defense. 2022 Academic of the Year for Defence Industry Awards 2020 W. H. Beattie Steel Medal of the Australian and New Zealand Optical Society 2020 Eureka Prize for Outstanding Science in Safeguarding Australia 2017 Vice Chancellors Award for Outstanding Research 2011 Eureka Prize for Leadership in Science 2007 Pawsey Medal from the Australian Academy of Science Professor Eggleton has held significant leadership roles including President of the Australian Optical Society (2008-2010) and Editor-in-Chief for Optics Communications (2007-2015) and currently serves as Editor-in-Chief for APL Photonics. He is a Fellow of multiple prestigious societies including the Australian Academy of Science (AAS), Optical Society of America (OSA), IEEE Photonics, SPIE, and the Australian Academy of Technological Sciences and Engineering (ATSE). His work has attracted substantial research funding through ARC Laureate and Federation Fellowships, and he leads major collaborative projects with industry and defense applications. His research group maintains strong connections with multiple institutes including the Sydney Environment Institute, Sydney Institute of Agriculture, and The University of Sydney Nano Institute, demonstrating the interdisciplinary nature of his work which bridges physics, engineering, and practical applications across multiple sectors.
Dr. Wei Dai is a Senior Lecturer (Associate Professor) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds affiliations with the EPSRC Centre for Maths of Precision Healthcare and the Communications and Signal Processing group. His research focuses on sparse signal processing, machine learning applications in signal processing, linear and bilinear inverse problems, wireless communications, and random matrix theory. Notably, he contributed to the first compressive sensing DNA microarray prototype and has a highly cited 2009 paper on compressive sensing reconstruction. Dr. Dai's educational background includes a Ph.D. in Electrical and Computer Engineering from the University of Colorado at Boulder (2007) and postdoctoral research at the University of Illinois at Urbana-Champaign (2007-2010). His work bridges theoretical signal processing with practical applications in sensing, communication systems, and biomedical signal analysis. He leads research initiatives in gridless DOA estimation, robust beamforming, and cortico-muscular coupling analysis using advanced optimization techniques. His research outputs span topics like spectral compressed sensing, Bayesian methods for integrated sensing-communication systems, and dictionary learning for causal discovery. Ongoing work emphasizes low-rank matrix recovery, distributed compressed sensing, and mathematical frameworks for super-resolution localization. Dr. Dai collaborates across disciplines, leveraging signal processing innovations for healthcare technology and next-generation wireless systems.
Prof. Dr.-Ing. Holger Blume serves as Vice President for Research and Transfer at Leibniz University Hannover while maintaining his academic position as Professor in the Architectures and Systems Section within the Faculty of Electrical Engineering and Computer Science. He holds multiple leadership positions including Chairperson of the Research Commission and Central Ethics Committee, Executive Board member of eNIFE (Leibniz Research Initiative for Neurosciences), and membership in both the Laboratory of Nano and Quantum Engineering and L3S Research Centre. His research interests span computer architecture, hardware design, signal processing, AI accelerators, hearing aid technology, and biomedical engineering. His work bridges theoretical computer science with practical applications in automotive systems, medical devices, and quantum engineering. Professor Blume's research demonstrates strong interdisciplinary connections between electrical engineering, computer science, and biomedical applications, with particular emphasis on hardware-oriented solutions for real-world problems. Analysis of his recent publications (2023-2025) reveals a strong focus on hardware acceleration for AI and signal processing applications, particularly in automotive radar/LiDAR systems and hearing aid technology. His work shows consistent innovation in RISC-V processor design, specialized hardware for mathematical functions, and biomedical applications of engineering principles. The research demonstrates a clear trajectory toward energy-efficient, specialized computing architectures for specific application domains. As Vice President for Research and Transfer, Professor Blume oversees significant research initiatives at Leibniz University Hannover, which hosts multiple Clusters of Excellence including PhoenixD (Photonics, Optics, and Engineering), QuantumFrontiers, and Hearing4all. The university participates in numerous collaborative research centers and junior research groups funded by DFG, BMBF, and EU programs. Professor Blume is actively involved in multiple research facilities including the Laboratory of Nano and Quantum Engineering and the L3S Research Centre. His work connects with Leibniz University's research focuses on optical technologies, quantum optics and gravitational physics, and biomedical research and technology. His leadership positions indicate strong involvement in shaping the research strategy and ethical framework of the university's scientific endeavors.
Emilie Avignon-Meseldzija is a Researcher at the Electrical and Electronic Engineering of Paris , affiliated with the School of Engineering at the University of Paris. Her work focuses on advanced analog and RF circuit design, with significant contributions to filter design, metamaterials, biomedical electronics, and radar systems. She has published over 24 peer-reviewed articles between 2010 and 2025, emphasizing innovations in group delay filters, non-Foster components, and FMCW radar systems. In education, she co-developed a new microelectronics teaching method implemented at Supélec in 2012, focusing on practical circuit design and integrated systems education. Her research combines theoretical rigor with practical applications, such as low-power biomedical stimulators and high-performance radar front-ends. Key areas of expertise include: Passive and active filter design Non-Foster metamaterial components Biomedical signal conditioning circuits RF frequency synthesizers Integrated circuit layout optimization Recent work (2024–2025) emphasizes dynamically reconfigurable systems, tunable inductors, and programmable biomedical stimulation circuits. Her contributions bridge foundational RF engineering with emerging applications in healthcare and radar technology.
Dr. Shunqiao Sun is an Assistant Professor in the Department of Electrical and Computer Engineering at The University of Alabama, College of Engineering. He joined the faculty in August 2019 as a tenure-track professor after working at Aptiv’s radar core team in Malibu, California. His research focuses on advanced signal processing, machine learning, and optimization for automotive and MIMO radar systems in autonomous vehicles. Ph.D. : Electrical and Computer Engineering, Rutgers University, 2016 M.S. : Electrical Engineering, Fudan University, 2011 B.S. : Electrical Engineering, Southern Yangtze University, 2004 Dr. Sun's research lies at the intersection of statistical and sparse signal processing , mathematical optimization , and machine learning , with applications in automotive radar , MIMO radar , and autonomous driving . His work emphasizes sparsity-oriented frameworks, AI-powered radar perception, and high-resolution 4D sensing. He leads a dynamic research group focused on next-generation radar technologies for intelligent transportation systems. His recent publications demonstrate a strong trend in deep learning for radar signal recovery , collaborative radar imaging , direction-of-arrival estimation with sparse arrays , and integrated sensing and communication . Several of his papers are among the most downloaded and cited in IEEE journals, including top articles in IEEE Signal Processing Magazine and IEEE Journal of Selected Topics in Signal Processing. Scientific Awards and Honors: NSF CAREER Award (2024) NSF CRII Award (2022) IEEE AESS Robert T. Hill Best Dissertation Award (2016) Best Student Paper Award at IEEE SAM Workshop (2020) Rutgers ECE Academic Achievement Award (2015–2016) University of Alabama Hewson Engineering Faculty Fellow (2025) Dr. Sun is actively involved in academic service and leadership. He is an Associate Editor for IEEE Signal Processing Letters and IEEE Open Journal of Signal Processing . He serves as Vice Chair of the IEEE Signal Processing Society’s Autonomous Systems Initiative and is an elected member of the IEEE Sensor Array and Multichannel (SAM) Technical Committee and the Integrated Sensing and Communication (ISAC) Technical Working Group. He has co-organized numerous workshops and special sessions at ICASSP, EUSIPCO, and IEEE Radar Conference. His lab has secured significant research funding from the National Science Foundation , NXP Semiconductors , MathWorks , and NOAA . He mentors multiple Ph.D. students, several of whom have interned at leading industry labs such as NXP and GM Cruise. He has co-organized the Workshop on Signal Processing for Autonomous Systems (SPAS) at ICASSP and EUSIPCO and delivered invited seminars at institutions including TU Delft, UC Davis, and Lehigh University.
Ayush Bhandari is a Senior Lecturer (Associate Professor level in the UK system) in the Department of Electrical and Electronic Engineering at Imperial College London, part of the Faculty of Engineering. He holds a PhD from MIT (2018) and has held research positions at leading institutions including INRIA, Nanyang Technological University, and EPFL. He is a UKRI Future Leaders Fellow (2020–present) and received the prestigious ERC Starting Grant (2024–2029) for his work on computational sensing. His research focuses on computational sensing, inverse problems, and signal processing with applications in scientific imaging and novel sensors. Key contributions include the Unlimited Sensing Framework, which enables high-dynamic-range imaging and signal acquisition beyond conventional ADC limitations. This work has led to over 10 US patents, including foundational technologies for modulo sampling and HDR reconstruction. Bhandari has been recognized with multiple awards, including the IEEE Best PhD Dissertation Award (2020), the President's Medal for Outstanding Early Career Researcher at Imperial (2021), and the Frontiers of Science Award (2023). He has delivered keynote speeches at major conferences such as IEEE CVPR, SPIE Photonics West, and the International Congress of Basic Science. His grants include the ERC Starting Grant (€1.5M) and UKRI Future Leaders Fellowship (£1.2M). He collaborates internationally, leading projects in computational radar, MIMO systems, and tomography. His work bridges hardware design and mathematical algorithms, emphasizing co-design principles to achieve breakthrough sensing capabilities.
Souheil Ben Smida is an Associate Professor at the School of Engineering & Physical Sciences at Heriot-Watt University, affiliated with the Institute of Sensors, Signals & Systems. His research focuses on power amplifiers, signal processing, radar systems, and biomedical applications, with a particular emphasis on integrating neural networks and wireless communication technologies. His work spans areas such as nonlinear systems, 5G technology, and healthcare innovation, leveraging radar and machine learning for non-invasive monitoring. Recent contributions include dual-band radar systems for vital signs detection, low-complexity neural network-based channel estimation, and predistortion techniques for power amplifiers. Key research trends include biomedical applications using radar and neural networks, optimization of communication systems for 5G, and hardware implementations on FPGA platforms. His publications reflect a balance between theoretical advancements and practical applications in electronics, healthcare, and telecommunications. Dr. Ben Smida's expertise contributes to the UN Sustainable Development Goals, particularly in advancing health technologies and sustainable industrial innovation.
Dr. Jing Fu is a Lecturer at the School of Engineering, RMIT University in Australia. Her research focuses on applying optimization algorithms and machine learning to telecommunications, wireless networks, and edge computing. She specializes in restless bandit models for dynamic resource allocation, multi-agent coordination, and energy-efficient systems design. Key areas include satellite communications, radar systems, and distributed computing architectures. Her work bridges theoretical operations research with practical applications in 5G/6G networks, UAV communication platforms, and smart sensor networks. She has published extensively on topics like beam scheduling, edge computing offloading strategies, and adaptive wireless protocols. Current supervision interests span neural networks for sensor systems, IoT resource allocation, and next-generation mega satellite networks. Research Themes: AI-driven network optimization, distributed radar systems, energy-efficient edge computing Key Projects: Reinforcement learning-based IoT resource allocation 6G wireless communication with AI integration Optimal beam scheduling for phased array radars Collaborations: Active in multi-disciplinary projects involving telecommunications, aerospace engineering, and computer science Dr. Fu supervises research projects on neural networks for telecommunications, adaptive wireless techniques, and satellite formation flying. She is based at RMIT's City Campus and open to guiding postgraduate research in her areas of expertise.
