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Silvère BonnabelView profile

Professor

• Systems and control
• Robotics
• Machine learning
• +5 more

Silvère Bonnabel is a Professor at École des Mines ParisTech (Mines ParisTech), a prestigious engineering school in France. He holds a Research Habilitation HDR (2014) and a PhD (2007) from Mines ParisTech, along with advanced degrees in financial mathematics and engineering. His professional experience includes roles as Professor at the University of New Caledonia (2019-2022), Visiting Fellow at the University of Cambridge (2017), and Assistant Professor at Mines ParisTech (2009-2016). Bonnabel’s research focuses on systems and control, robotics, machine learning, and navigation, with notable contributions to invariant Kalman filtering and optimization on manifolds. He has authored/co-authored over 100 articles, 9 patents, and achieved an h-index of 39. Notable awards include the 2024 George N. Saridis Award and 2021 European Control Award. His work bridges theoretical advancements with industrial applications, including collaborations with companies like Safran, Thales, and Manitowoc. Bonnabel has supervised numerous PhD students, contributing to breakthroughs in inertial navigation, radar tracking, and autonomous systems. Education: 2014: HDR in Mathematics (Paris VI Sorbonne) 2004-2007: PhD, Mines ParisTech 2003-2004: Master in Financial Mathematics (Paris XII) 2001-2004: Engineering Degree, Mines ParisTech Industrial Collaborations: Partnerships with Safran, Thales, Manitowoc, and startups like OFFROAD, focusing on crane control, inertial navigation, and autonomous systems. Editorial Roles: Associate Editor for IEEE Control Systems Magazine, European Control Conference, and Systems & Control Letters. His research emphasizes geometric control theory, stochastic algorithms, and sensor fusion, with applications to robotics, aerospace, and industrial automation. Bonnabel’s work on invariant extended Kalman filters has been commercialized in products like Safran’s inertial navigation systems.

George C. AlexandropoulosView profile

Associate Professor

• Wireless Communication Systems
• Signal Processing
• Algorithmic Design
• +9 more

George C. Alexandropoulos George C. Alexandropoulos is an Associate Professor at the Department of Informatics and Telecommunications, School of Sciences, National and Kapodistrian University of Athens (NKUA). He also holds an adjunct professorship at the University of Illinois Chicago. His research focuses on wireless communication systems, signal processing, and reconfigurable intelligent surfaces (RIS), with contributions to integrated sensing and communications (ISAC), millimeter-wave/THz systems, and distributed machine learning. Education Ph.D. in Computer Engineering and Informatics, University of Patras, Greece (2010) M.A.Sc. in Signal and Communications Processing Systems, University of Patras, Greece (2005) Engineering Diploma (5 years), Computer Engineering and Informatics, University of Patras, Greece (2003) Research Interests Alexandropoulos' work spans algorithmic design for wireless networks, optimization of multi-antenna systems, and full-duplex radios. He is particularly known for pioneering contributions to reconfigurable metasurfaces, including their application in ISAC and 6G networks. His research emphasizes practical implementations, such as prototypes for AoA estimation and field trials for RIS deployment. Articles His publications highlight advancements in secure ISAC, fluid RIS design, and edge-intelligence networks. Recent work explores challenges in THz RIS hardware and OTA edge inference using metasurface-integrated neural networks. Scientific Awards IEEE ComSoc Leonard G. Abraham Prize (2024) IEEE Marconi Prize Paper Award (2021) NKUA Research Excellence Award (2023-2024) Advising & Grants Alexandropoulos has led several EU and industry projects, including contributions to Huawei's 5G standards and TII's AI research. His work often involves interdisciplinary teams and practical prototyping. Labs & Teams He is affiliated with the Communications and Signal Processing division at NKUA and collaborates with institutions like ATHENA Research Center and the Technology Innovation Institute (Abu Dhabi).

Yaxiong XieView profile

Assistant Professor

• Next-generation networks
• Mobile computing
• Edge computing
• +6 more

Yaxiong Xie is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, affiliated with the School of Engineering and Applied Sciences. Their research focuses on next-generation networks, mobile computing, edge computing, and artificial intelligence of things (AIoT). Dr. Xie holds a PhD in Computer Science from Nanyang Technological University, Singapore (2017). Research interests span advanced wireless communication systems, mmWave sensing, smart surfaces, and IoT applications. They pioneer innovations in battery-free sensors, privacy-preserving networks, and robust human-centric sensing technologies. Notable projects include mmRotation (mmWave radar versatility), Hydra (OFDM security), and Cyclops (intraocular pressure monitoring via nanomaterials). Publications emphasize cross-disciplinary contributions to 5G/6G systems, metasurface engineering, and biomedical IoT. Their work bridges theory and practice through prototypes like Athena (wireless interference mitigation) and Waffle (waterproof mmWave sensing in bathrooms). Advising focuses on graduate students in computer networks and sensing systems. Grants and collaborations support cutting-edge projects in smart environments and healthcare technologies. Labs/teams include interdisciplinary groups working on wireless systems, edge AI, and sensor networks.

Timothy GlotchView profile

Professor

• Planetary Science
• Geochemistry
• Remote Sensing
• +4 more

Timothy Glotch is a Professor in the Department of Geosciences at Stony Brook University, leading the RISE2 node of NASA's Solar System Exploration Research Virtual Institute. His research focuses on planetary spectroscopy, mineralogy, and remote sensing, particularly on the Moon, asteroids, and Martian moons. He is also a Co-Investigator on the Lunar Reconnaissance Orbiter Diviner Lunar Radiometer and a Participating Scientist on the OSIRIS-REx mission. Education: B.A. in 1999 from Colgate University, Ph.D. in Geological Sciences from Arizona State University (2004). Affiliated with the Center for Planetary Exploration. Research interests include laboratory spectroscopy of extraterrestrial materials, micro-Raman spectroscopy, light scattering models for planetary regoliths, and thermal infrared remote sensing of planetary surfaces. His work addresses NASA's goals in human exploration and science. Key contributions include studies of lunar silicic volcanism, asteroid Bennu mineralogy, and Martian phyllosilicate detection. Over 50 researchers/students collaborate in his team. Grants/awards: Not explicitly listed, but leadership roles in major NASA initiatives indicate significant funding. Advises numerous graduate students highlighted in publications. Labs/teams: RISE2 team, Center for Planetary Exploration, collaborations on NASA missions.

• Deep Learning
• Machine Learning
• Medical Image Analysis
• +4 more

Euijoon Ahn is a Senior Lecturer in the Department of Information Technology at James Cook University’s College of Science and Engineering. He holds a PhD in Computer Science (Medical Image Analysis) from The University of Sydney (2020), alongside earlier degrees from The University of Sydney and The University of Newcastle. His research focuses on developing machine learning and deep learning models for biomedical applications, particularly unsupervised/self-supervised learning in medical imaging and telehealth. He has authored over 30 peer-reviewed publications in top-tier venues like IEEE T-MI, CVPR, and MICCAI. Education PhD in Computer Science (Medical Image Analysis), The University of Sydney (2016–2020) MPhil in Computer Science, The University of Sydney (2014–2016) Bachelor of IT, The University of Newcastle (2007–2010) Research Interests His work emphasizes translational health technologies, including unsupervised learning for medical image segmentation/classification, telehealth systems for remote patient monitoring, and AI-driven vital sign estimation using facial video analysis. Current projects explore self-supervised frameworks for histopathology, low-dose CT denoising, and ecoacoustic data analysis. Key Projects Unsupervised/self-supervised learning for medical image analysis AI-enabled telehealth systems for non-contact vital signs measurement LEAVES: Open-source tool for ecoacoustic dataset management Awards Recipient of Telehealth Technology Innovation Awards (2018, 2019), Best Oral Presentation (2019), and The University of Sydney’s Research Excellence Prize (2019). Teaching Coordinates and lectures courses in Machine Learning (CP1407), Programming II (CP1404), Advanced Data Communications (CP5601), and Operating Systems (CP1409). He also supervises multiple PhD/MPhil students in medical AI and data science. Labs & Affiliations Active in James Cook University’s Health Informatics and AI research groups, and collaborates with Nepean Blue Mountains Local Health District on clinical AI projects.

Haonan ChenView profile

Assistant Professor

• Big Data and Machine Learning
• Radar and Satellite Hydrometeorology
• Remote Sensing of Natural Disasters
• +5 more

Dr. Haonan Chen is an Assistant Professor in the Department of Electrical and Computer Engineering at Colorado State University (CSU) since 2020, and an Affiliate Faculty at the Data Science Research Institute (DSRI). He previously worked at NOAA's Physical Sciences Laboratory as a Research Associate and Scientist. His research focuses on remote sensing, radar/satellite hydrometeorology, and big data analytics applied to disaster observation and smart city systems. Education: B.S., Electrical Engineering, Chongqing University of Posts and Telecommunications (2010) M.S., Electrical Engineering, Colorado State University (2013) Ph.D., Electrical Engineering, Colorado State University (2017) Research interests include cloud/precipitation processes, natural disaster remote sensing, and machine learning integration with environmental data. He has authored/co-authored 65 peer-reviewed papers and serves as an Associate Editor for Journal of Atmospheric and Oceanic Technology , URSI Radio Science Bulletin , and IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , as well as a Guest Editor for Remote Sensing . Affiliated with professional organizations including URSI Commissions F, American Meteorological Society, American Geophysical Union, and IEEE.

TOORAJ NIKOUBINView profile

Professor

• NanoElectronics Device/Circuit/System
• Approximate Computing
• Reconfigurable Computing
• +3 more

Dr. Tooraj Nikoubin is a Professor of Instruction in the Department of Electrical and Computer Engineering at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a Ph.D. in Computer Engineering from Shahid Beheshti University (2009), and M.Sc./B.Sc. degrees in Electrical Engineering-Electronics from K.N.T. University of Technology (Iran). His research focuses on nano-electronics, approximate computing, reconfigurable systems, and wearable electronics, emphasizing device-circuit co-design in emerging technologies. With over 35 journal/conference publications and one book chapter over 13+ years in academia, Dr. Nikoubin teaches both graduate and undergraduate courses across computer and electrical engineering domains. Research interests include nano-scale device optimization, energy-efficient circuits, and hardware acceleration for edge computing applications. His work spans from biometric authentication systems using PPG signals to drone-based precision agriculture and molecular property prediction. Recent efforts prioritize low-power, area-efficient designs in cryptography (e.g., AES S-box optimization) and health monitoring wearables. Dr. Nikoubin has contributed to innovative hardware solutions such as fiber antennas for wireless body area networks and low-cost radar systems for structural health monitoring. His lab explores intersections between emerging technologies like CNTFETs and FinFETs with system-level power management. Current openings exist for students/researchers to contribute to this dynamic research environment.

• Software Defined Radio
• AI/ML for Wireless Communications
• Cyber Security
• +3 more

Ruolin Zhou is an Associate Professor in the Department of Electrical & Computer Engineering at the University of Massachusetts Dartmouth, part of the College of Engineering. She joined UMass Dartmouth in 2018 after serving as a tenured Associate Professor at Western New England University from 2012 to 2018. Her research focuses on software-defined radio (SDR), AI/ML-driven wireless communications, spectrum sensing, cyber & electromagnetic spectrum security, and hardware security. Her work has been supported by grants from NSF, ONR, AFRL, ARL, and industry partners like Lockheed Martin. Dr. Zhou holds a PhD in Engineering from Wright State University (2012), an MS in Electrical Engineering (2007), and a BS in Electrical Engineering from Dalian Jiaotong University (2003). She teaches courses including Foundations of Cybersecurity, Digital Logic Design, and Software-Defined Radio. Her research has led to innovations in FPGA-accelerated deep learning architectures, real-time spectrum monitoring, and secure communication systems. Her contributions have been recognized with awards including the 2024 IEEE Region 1 Outstanding Teaching Award, ONR Summer Faculty Research Fellowships (2022-2023), and Best Demo Awards at GLOBECOM 2010. She actively serves in IEEE roles, including Vice President for Technical Activities within the IEEE Reliability Society (2025) and steering committee memberships in IEEE Future Networks and IoT Technical Communities.

Bogdan StrimbuView profile

Associate Professor

• Forest Biometrics
• Remote Sensing
• Machine Learning
• +5 more

Bogdan Strimbu is an Associate Professor in the Department of Forest Engineering, Resources & Management at Oregon State University's College of Forestry . He specializes in forest biometrics, remote sensing, and forest operations planning. His research integrates machine learning, LiDAR, and unmanned aerial systems (UAS) to advance forest inventory accuracy and sustainable management strategies. Education includes a Ph.D. (2009) and M.Sc. (2003) in Forest Management from the University of British Columbia, an M.Sc. (2011) in Mathematics/Statistics from Louisiana Tech University, and a B.S. (1992) in Forest Operations from Transilvania University, Romania. Research focuses on biometrics , LIDAR applications , forest planning algorithms , and UAS-based land monitoring . Notable contributions include developing parsimonious forest models and optimizing harvest strategies using heuristic methods. His work spans North American and European forests, addressing climate impacts, tree growth dynamics, and precision forestry. Strimbu advises graduate students in forest operations and biometrics. He co-authored "Unmanned Aerial Systems for Land Monitoring" (2020) and collaborates on interdisciplinary projects involving machine learning and environmental reporting. His grants fund studies on canopy water estimation, steep-slope harvesting, and crop change detection in Oregon's Willamette Valley. He leads a research team focused on integrating remote sensing technologies with classical forestry methods. Current projects include lidar-based defect detection and UAV applications in herbaceous vegetation mapping.

• Control Systems
• Energy Systems
• Artificial Intelligence
• +5 more

Eduard Petlenkov is a Tenured Full Professor at the Department of Computer Systems at Tallinn University of Technology (TalTech), leading the Centre for Intelligent Systems. His research focuses on advanced control systems, energy efficiency, robotics, and artificial intelligence applications. He holds the award of Recognized Lecturer 2023 . Key research areas include: Fractional-order control systems Data-driven optimization for energy systems Robotics and autonomous systems Model predictive control (MPC) Power grid stability and renewable integration Recent work emphasizes: Stabilization techniques for hybrid power systems Radar technologies for unmanned ground vehicles Energy harvesting in metamaterials Real-time control of unknown linear systems Smart grid digital twin frameworks His contributions span 20+ peer-reviewed articles (2022–2025), addressing topics like reinforcement learning in microgrid control and fractional-order PID tuning methods. Active in bridging industry 5.0 education through blended learning approaches.

Bariscan YonelView profile

Assistant Professor

• Computational Imaging
• Inverse Problems
• Machine Learning
• +3 more

Bariscan Yonel is an Assistant Professor in Electrical & Computer Engineering at the University at Albany's College of Nanotechnology, Science, and Engineering. He holds a PhD from Rensselaer Polytechnic Institute and conducts research at the intersection of computational imaging, machine learning, and signal processing. His research develops theory and efficient algorithms for high-dimensional inference problems with applications in wave-based imaging systems. Key focus areas include: Foundations of AI for inverse problems in imaging Computational methods for passive radar systems Low-rank matrix recovery theory Phaseless synthetic aperture techniques His publications demonstrate consistent focus on advancing imaging algorithms through optimization theory and machine learning. Recent work explores deep learning approaches for interferometric imaging and robust phase retrieval methods.

Rafi AhmadView profile

Professor

• Antimicrobial Resistance
• Bioinformatics
• Drug Discovery
• +7 more

Rafi Ahmad is a Professor and Group Leader of the strategic research group B3 (Bioinformatics, Biodiscovery & Biorefinery) at the University of Inland Norway. He holds a BPharm from Jamia Hamdard (India), MSc in Bioinformatics from the University of Exeter (UK), and a PhD in Bioinformatics and Computational Chemistry from the Arctic University of Tromsø (Norway). His research focuses on antimicrobial resistance (AMR), rapid diagnostic technologies, and bioinformatics-driven solutions to global health challenges. He leads major projects like OH-AMR-Diag and UTI-Diag, funded by the Research Council of Norway (RCN) and international partners. Key affiliations include visiting professorships at the University of Southampton and the Arctic University of Tromsø. Education: BPharm (2000), MSc Bioinformatics (2001), PhD (2008). Postdoctoral work at the Norwegian Structural Biology Centre (2008-2011). Industrial roles at AstraZeneca and Medivir AB (2011-2015). Current roles include leadership in RCN projects (AMR-RADAR, ABR-Diag) and the NORSE network for One Health resistome surveillance. His team includes 30+ researchers across postdocs, PhDs, and technicians. Research interests span AMR mechanisms, rapid diagnostics, metagenomics, and machine learning applications. He supervises over 20 students and collaborates internationally on drug discovery and diagnostic innovation. Recent media coverage highlights his work on reducing antibiotic overuse through rapid testing (NRK news, Hamar Arbeiderblad). Grants include NOK 55M for ongoing projects. Labs focus on nanopore sequencing, SERS nanowire chips, and Voyager bioinformatics tools for pathogen detection. He is part of the RCN’s COST Action Management Committee (2020) and partners with Harvard and Indian institutions on AMR-Educate initiatives.

• Energy Systems
• Renewable Energy
• Urban Energy Systems
• +3 more

Vahid Arabzadeh is a Researcher affiliated with the Department of Built Environment at Aalto University. He holds a Master of Science in Chemical Technology (Process System Engineering) from Aalto University (2016) and a Doctorate (Tekn. toht., teknillinen fysiikka) in 2021. His research focuses on renewable energy systems, urban energy planning, wind power integration, and heat demand management, with a strong emphasis on Sustainable Development Goals. Key research interests include energy system modeling, large-scale renewable integration, decarbonization strategies, and sector-coupling flexibility. His work bridges technical innovation with policy implications, particularly in cold-climate urban settings like Helsinki. Recent projects explore vertical farming powered by wind energy, hybrid renewable forecasting, and sea ice monitoring via satellite data. Publications highlight interdisciplinary approaches combining physical modeling with data-driven techniques. Collaborations span climate policy analysis, energy infrastructure optimization, and AI-driven cost estimation in civil engineering. His research has been cited in policy documents and referenced in Wikipedia/Wikidata entries, demonstrating real-world impact.

Yuan DingView profile

Associate Professor

• Wireless Communications
• Directional Modulation
• Antenna Design
• +2 more

Dr. Yuan Ding is Associate Professor at Heriot-Watt University's School of Engineering & Physical Sciences, specializing in directional modulation, antenna design, and RF systems within the Institute of Sensors, Signals & Systems. Research advances wireless communication security through directional modulation techniques, with focus on RF fingerprinting, backscatter communication, and integrated sensing-communication systems. Recent work explores quantum-enhanced security and mmWave implementations. Publications demonstrate leadership in directional modulation research, with contributions to antenna array design, physical layer security, and nonlinear transmitter modeling. Work bridges theoretical concepts with practical RF hardware implementations.

Raffaella GuidaView profile

Professor

• Remote Sensing
• Synthetic Aperture Radar (SAR)
• Earth Observation
• +6 more

Raffaella Guida is a Reader in Satellite Remote Sensing at the University of Surrey's Surrey Space Centre (SSC), part of the School of Computer Science and Electronic Engineering. She leads the Remote Sensing Applications research group and serves as PostGraduate Research Director at SSC since 2019. Her academic career began with a Laurea (cum laude) in Telecommunications Engineering (2003) and a PhD in Electronic and Telecommunications Engineering (2007), both from the University of Naples Federico II. She has held research roles at Technische Universität München (2006) and joined the University of Surrey in 2008 as a Lecturer, advancing to Reader and Head of her research group. Her research focuses on Remote Sensing and Earth Observation , particularly Synthetic Aperture Radar (SAR) applications in urban monitoring, natural disasters, maritime surveillance, and environmental science. Notable achievements include developing methodologies for detecting illegal fishing activities in Mauritius, flood mapping, and oil spill analysis. She has published over 80 papers and received the IEEE J-STARS Best Paper Award (2013) and the TeNT Teaching Award (2008). Research collaborations span universities, space agencies, and NGOs Leadership roles: PGR Director at SSC, Member of Faculty Research Degrees Committee Teaching expertise: Satellite Remote Sensing, Electromagnetism Key projects: NEREUS maritime surveillance system, GNSS-R soil moisture monitoring Her publications emphasize SAR-based innovations in maritime domain awareness, disaster response, and environmental monitoring. Ongoing work includes fusion of SAR with thermal infrared data and development of algorithms for automated ship detection and wake analysis. She mentors over 15 PhD students, advancing topics like SAR-AIS data fusion and forest height estimation. Awards also include contributions to education technology through podcast integration in remote sensing courses. Her lab's work on damage assessment mapping in conflict zones (e.g., Mariupol) demonstrates real-world impact of SAR technology in humanitarian efforts.