Marius MARCU is an Associate Professor at the Computer and Software Engineering Department , Faculty of Automation and Computers , Politehnica University of Timisoara , Romania. His career spans over two decades, combining academic rigor with industry collaboration, particularly in mobile systems and energy-efficient computing. PhD in Computer Science (2005) Master's in Advanced Computer Engineering (1996) BSc in Computer Systems Engineering (1995) His research focuses on Mobile Systems and Applications , Dynamic Power and Thermal Management , and Power-Aware Applications , with over 60 publications and leadership in 11 R&D projects. His work bridges theoretical advancements with practical implementations in wireless sensor networks and embedded systems. Recent publications emphasize energy characterization , thermal profiling , and wireless positioning accuracy across 2010–2011. He actively contributes to academic governance as Studies Program Manager for the Master of Information Technology and peer reviewer for IEEE and ACM. Merit Diplomas 2021 Project Director, FP7-eMuCo (2008–2010) Advisory roles for Alcatel-Lucent R&D contracts
Philippe Cudré-Mauroux is a Full Professor at the University of Fribourg, Switzerland , where he leads the eXascale Infolab . He has held visiting researcher positions at MIT and Microsoft CISL , and serves on the Research Council of the Swiss National Science Foundation and the Scientific Advisory Board of the CHIST-ERA EU Research Programme . Research Interests: His work spans exascale information management , big data , AI , knowledge graphs , linked data , time series data repair , and emergent semantics . He focuses on building scalable, intelligent data systems that integrate storage, computation, and semantics. Publication Trends: His recent work emphasizes schema-aware knowledge graph completion , time series imputation and benchmarking , hardware-accelerated data systems , and large language models for data cleaning . His research bridges database systems, AI, and systems architecture, often targeting high-performance, real-world applications. Scientific Awards: ERC Consolidator Grant (2016) Google Faculty Research Award (2013) Verisign Internet Infrastructures Award (2012) Best Paper Awards at VLDB (2020), AAMAS (2019), and Swiss Data Science Conference (2020) EPFL Doctorate Award and Press Mention (2007) Best Mentor Award at ISWC (2010) Advising and Grants: He mentors a large group of researchers and students, many of whom are co-authors on his publications. He has secured significant funding, including a €2M ERC Grant and multiple Google and Amazon grants, supporting a vibrant research lab focused on next-generation data infrastructure. Labs and Teams: He leads the eXascale Infolab at the University of Fribourg, a dynamic research group actively publishing in top-tier venues and developing innovative tools for data management and AI integration.
Marco Gruteser is a Professor of Electrical and Computer Engineering (primary) and Computer Science (by courtesy) at Rutgers University, leading research at the Wireless Information Network Laboratory (WINLAB). His work focuses on mobile computing, location privacy, vehicular networks, and wireless security. With over 100 peer-reviewed publications, he has pioneered privacy-preserving techniques in location-based services and advanced connected vehicle systems. NSF CAREER Award recipient Rutgers Outstanding Engineering Faculty Award Best Paper Awards at ACM MobiCom 2012/2011, MobiSys 2010 His research spans vehicular security , visual MIMO networks , mobile sensing , and privacy-preserving systems . Recent publications highlight WiFi-based vehicle positioning, light-based communication, and federated learning applications. He has advised numerous PhD and MS students now at institutions like Google, Carnegie Mellon, and AT&T Labs. Notable contributions include ParkNet (parking sensing), capacitive touch authentication, and tire-pressure monitoring security analysis. His work has been featured in NPR , The New York Times , and CNN .
Dr. Svenja Papenmeier is a Researcher in the Marine Geophysics Working Group at the Leibniz Institute for Baltic Sea Research Warnemünde . She previously worked at the Alfred Wegener Institute (2012–2019) and the Christian-Albrechts-University of Kiel (2008–2012). She holds a PhD in Marine Geology from Kiel University, a Master’s in Marine Geosciences from the University of Bremen, and a Bachelor’s in Geosciences from the same institution. Research Focus: Hydroacoustic habitat mapping in the North Sea, Baltic Sea, and Siberian Arctic Development of seabed mapping standards (BSH No: 7201) Automated stone/boulder detection in acoustic datasets using neural networks Paleogeographic evolution of the Elbe River valley and Sylt Outer Reef Long-term analysis of dynamic bedforms and sediment distribution Scientific Contributions: Published 15+ peer-reviewed articles on marine sedimentology, AI applications in acoustic data, and coastal dynamics Key projects: OTC Stone (BMBF-funded), DAM CREATE , LABS , PaleoElbe , AMIN Regular presenter at international conferences (GeoHab, EGU, AGU) on topics like AI-based habitat mapping and sediment dynamics
Wim Dewulf is a full professor at the Faculty of Industrial Engineering Sciences, KU Leuven, and serves as the dean of the faculty. He is a contact person for the Manufacturing Processes and Systems (MaPS) unit at Campus Group T Leuven and holds leadership roles such as division head and member of councils like the University Council and Academic Council. His research focuses on life cycle engineering, ecodesign, sustainable manufacturing, and computed tomography applications in industrial processes. His research interests span sustainable engineering, additive manufacturing (AM), dimensional quality control, and X-ray CT. Recent projects include using deep learning for CT reconstruction, improving AM surface quality via laser remelting, and enabling autonomous demanufacturing of battery-containing products. He actively supervises students in these areas, particularly in laser powder bed fusion and CT metrology. Wim Dewulf is a member of Leuven.AM (KU Leuven Institute for Additive Manufacturing) and SIM² (Institute for Sustainable Metals and Minerals). His work involves advising on circular economy strategies, process optimization, and advanced imaging techniques, with no explicit scientific awards listed in the provided data. He has contributed to education through courses like Applied Sustainability Assessment and Life Cycle Engineering , emphasizing sustainable design and manufacturing. His research teams focus on technology transfer, industrial collaboration, and developing data-driven models for AM and recycling.
Ákos Lédeczi is a prolific researcher with significant contributions to computer science, sensor networks, robotics, and K-12 education. His work spans hardware-software co-design, wireless localization, and accessible programming environments. Co-developed NetsBlox and DeepForge for novice-friendly distributed computing. Created PinPtr high-precision GPS cloud service and inTrack mobile node tracking systems. Pioneered Browser-based robotics simulation and smartphone IoT education for K-12 students. Explored RF interferometry , Doppler shift tracking , and acoustic shooter localization in urban environments. Contributed to medical capsule robot design and security co-design for embedded systems. His recent focus includes project-based AI curricula for female high school students and collaborative visual programming environments . While specific university affiliations aren't detailed here, his collaborations with Péter Völgyesi, Miklós Maróti, and others highlight his role in advancing distributed and sensor network research.
Salvo Rossi is a Full Professor of Statistical Machine Learning for Signal Processing at the Department of Electronic Systems, Faculty of Information Technology and Electrical Engineering, Norwegian University of Science and Technology (NTNU). He holds leadership roles as Deputy Director for Research and Deputy Manager of the Centre for Green Shift in the Built Environment. Additionally, he serves as a part-time Senior Research Scientist at SINTEF Energy Research. His work bridges academia and industry, with significant contributions to signal processing, machine learning, and IoT systems. Research Interests: Statistical Machine Learning Data Fusion in Wireless Sensor Networks Digital Twins and Industrial IoT Anomaly Detection and Fault Diagnosis Federated and Distributed Learning Graph Signal Processing His recent publications reflect a strong trend toward intelligent, distributed systems for industrial monitoring, particularly in energy and safety-critical environments. The integration of machine learning with signal processing for decision fusion in sensor networks is a central theme. Scientific Awards and Recognition: Exemplary Senior Editor, IEEE Communications Letters (2018) Department Ambassador, NTNU (2016) IEEE Senior Member (since 2011) Editorial and Professional Service: Senior Area Editor, IEEE Transactions on Signal and Information Processing over Networks (since 2025) Topical Editor, IEEE Sensors Journal (since 2022) Area Editor, IEEE Open Journal of the Communications Society (2019–2023) Executive Editor, IEEE Communications Letters (2019–2021) General Chair, IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Trondheim, 2022 He has advised numerous PhD and master’s students (not explicitly listed), led major research projects in IoT and green energy, and contributed to national and international research initiatives. His lab, SPIN (Signal Processing Group), focuses on intelligent signal processing for real-world applications in smart environments and Industry 4.0.
Professor Fuwen Yang is a leading academic at Griffith University's School of Engineering and Built Environment, specializing in Electrical and Electronic Engineering. With expertise in microgrid control, networked control systems, and renewable energy integration, he leads the Smart Energy Systems Group at the Institute for Intelligent and Integrated Systems. His research focuses on optimizing distributed energy resources and enhancing grid resilience through advanced control strategies. Current research aligns with Australian net-zero emissions goals Secured significant grants including ARC Discovery and Linkage projects Supervises 23 PhD and 35 Master's students Recent publications highlight innovations in virtual inertia control, digital twin frameworks, and data-driven predictive control. His work has earned global recognition, including the Stanford Top 2% Researchers list and Fellowships from Engineers Australia. Professor Yang's editorial roles include Associate Editor for IEEE Transactions on Industrial Informatics and other prominent journals. Acted as Chief Investigator on 8 major funded projects Contributed to Sustainable Development Goals 7 (Affordable Energy) and 9 (Infrastructure Innovation)
Charaf Hassan is a Professor and Head of Department at the Budapest University of Technology and Economics, specifically in the Department of Automation and Applied Informatics. His work spans interdisciplinary domains, focusing on distributed systems, network coding, and IoT technologies. His research interests include Distributed Systems and Domain-Specific Modeling Network Coding and Mobile Peer-to-Peer Systems Model-Driven Development for Multiplatform Applications Machine Learning in Fluid Dynamics and Pharmaceutical Analysis Recent publications highlight trends in applying convolutional neural networks to viscosity estimation, model-driven methodologies for IoT, and network coding in cloud storage. He teaches advanced courses in distributed systems and software architectures at the university level.
Fengying Dang is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University. Her research focuses on enhancing robotic intelligence through perception, sensor data utilization, and intelligent control systems for autonomy in complex environments. Education: PhD in Electrical and Computer Engineering (2021) from George Mason University, B.S. in Detection, Navigation & Control (2015) from Northwestern Polytechnical University. Research Interests : Designing robust perception/localization systems using vision and non-vision sensors, learning-based control algorithms for robotics, and applications in agricultural automation (e.g., weed detection in cotton systems) and bio-inspired underwater vehicles. Publication Trends : Key topics include autonomous driving , weed detection , flow sensing for AUVs , and deep learning in robotics . Her work combines sensor fusion , model reduction techniques , and control algorithms . Laboratory : Leads research in autonomous systems and robotics, mentoring doctoral and master's students. Alumni include MS EE graduate Benjamin Wittrup (2025), now employed at Treetown Tech LLC.
Shahriar Nirjon is an Associate Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill. His research focuses on Embedded Intelligence, developing end-to-end systems that make resource-constrained real-time and embedded sensing systems capable of learning, adapting, and evolving. Dr. Nirjon received his Ph.D. from the University of Virginia in 2014. Before joining UNC Chapel Hill in 2015, he worked as a Research Scientist at HP Labs (2014-2015) and as a Research Intern at Microsoft Research (Summer 2013) and Deutsche Telekom Lab (Summer 2010). His primary research interest is Embedded Intelligence, with recent works broadly categorized into embedded deep learning and multi-modal sensing techniques. Applications of his research span wearables and implantables, long-term monitoring and control systems, smart home environments, and mobile health solutions. Dr. Nirjon's research bridges theoretical foundations with practical implementations, resulting in systems that have real-world impact in healthcare, safety, and everyday computing. His work has been highlighted in prominent media outlets including IEEE Spectrum, The Economist, New Scientist, and BBC. Dr. Nirjon's publication record demonstrates a strong focus on mobile computing systems, embedded sensor networks, and wireless technologies. His recent work shows increasing integration of machine learning and artificial intelligence with embedded systems, particularly in healthcare applications. There's a clear trajectory toward more sophisticated, energy-efficient systems capable of on-device intelligence, with growing emphasis on privacy-preserving techniques and real-world deployments in healthcare settings. Best Paper Award, Challenges in AI and Machine Learning for IoT (AIChallengeIoT '20) Best Presentation Award, Pervasive and Ubiquitous Computing (Ubicomp '20) Best Paper Award, Distributed Computing in Sensor Systems (DCOSS '19) Best Presentation Award, Vehicular Networking Conference App Contest (VNC '18) Best Demo Runner Up, Vehicular Networking Conference App Contest (VNC '18) Best Paper Nomination, Embedded Wireless Systems and Networks (EWSN '17) Best Demo Runner Up Award, Embedded Networked Sensor Systems (SenSys '16) Best Paper Award, Mobile Systems, Applications, and Services (MOBISYS '14) Best Paper Award, Real-Time and Embedded Technology and Applications Symposium (RTAS '12) Dr. Nirjon has advised numerous PhD students including Chong Shao (Google), Shiwei Fang (Assistant Professor at Augusta University), Tamzeed Islam (Research Staff at Amazon), Bashima Islam (Assistant Professor at Worcester Polytechnic Institute), Seulki Lee (Assistant Professor at UNIST, Korea), and Yubo Luo (Black Sesame Technologies Inc.). He currently advises Mahathir Monjur, Zhenyu Wang, and Louie Lu who are in various stages of their PhD programs. His research is supported by significant grants including an NSF CAREER award ($561K), an NSF SCH grant ($941K), and multiple other NSF-funded projects totaling over $2 million. His active projects include Audio Privacy, Pedestrian Safety, IoT Data Privacy, and HVAC Acoustic Fingerprinting. Dr. Nirjon leads research in the Embedded Intelligence Lab at UNC Chapel Hill, where his team develops cutting-edge technologies in mobile computing, embedded systems, and wireless networks. His work spans multiple domains including healthcare (mobile health systems), safety (pedestrian safety applications), and smart environments (smart homes). He collaborates with researchers across disciplines, particularly in healthcare through the Carolina Health Informatics Program (CHIP), and is actively involved in the Be-A-Maker (BeAM) network of makerspaces at UNC.
Kundan Kumar is a Postdoctoral Researcher at Aalto University , affiliated with the Department of Electrical Engineering and Automation. His research focuses on advanced signal processing, nonlinear state estimation, and control systems. Recent work emphasizes Kalman filtering , Bayesian inference , and underwater tracking , with publications in venues like IEEE International Conference on Multisensor Fusion and Integration. He develops methods addressing unknown measurement noise , false data injection attacks , and delayed measurements in complex systems. His publications highlight interdisciplinary applications in target tracking , distributed filtering , and polynomial chaos expansions for improved estimation accuracy.
Yang Wang is an Associate Professor of Public Affairs at the La Follette School of Public Affairs, University of Wisconsin-Madison, with faculty affiliations spanning the Center for Demography and Ecology, Center for Demography of Health and Aging, Center for Financial Security, Institute for Research on Poverty, and the Risk Management and Insurance Department in the Wisconsin School of Business. Her educational background includes: Ph.D. in Economics, Duke University M.A. in Economics, Duke University MIPA in International Public Affairs, University of Wisconsin-Madison B.A. in International Relations, Peking University Dr. Wang's research focuses on applied microeconomics, health economics, and applied econometrics, with publications in high-impact journals including the American Economic Journal: Applied Economics and Health Economics. Her work examines policy impacts on health outcomes, economic disparities, and demographic trends, notably contributing to The Oxford Handbook of Economics and Human Biology. Her recent 2025 publications reveal a significant expansion into intelligent transportation systems, with 15 articles centered on autonomous vehicle technologies. These works explore cloud-based driving systems, edge computing architectures, and vehicle-to-everything communication frameworks, addressing critical challenges in adverse weather navigation, safety protocols, and computational resource allocation for connected vehicles. As Co-Principal Investigator for the project 'The Consequences of Abortion Restrictions for Intimate Partner Violence and Child Welfare Involvement,' Dr. Wang leads interdisciplinary policy analysis. She teaches core courses including PA 281 Discovering What Works in Health Policy and PA 871 Public Program Evaluation, mentoring students in evidence-based policy design. Her research integrates with UW-Madison's ecosystem through collaborations with transportation innovation initiatives and demographic research centers, positioning her at the intersection of public policy and emerging technology governance.
Fouad KHENFRI is a Professor and researcher at ESTACA (Ecole Supérieure des Techniques Aéronautiques et de Construction Automobile) , specializing in embedded systems and control engineering. He holds a PhD in Computer Science and Applications from the University of Nantes (2016), a Master's in Automatic Control from EMP Algiers (2011), and an Engineering Degree in Automation from the University of Biskra (2008). Key research areas: Systems Control, Embedded Software Design, and Artificial Intelligence Recent publications focus on UAV control systems, Neural Architecture Search, and energy optimization Supervising doctoral students on topics including drone development and deep learning applications
Marina Indri is a Tenured Associate Professor in the Department of Electronics and Telecommunications at Politecnico di Torino, Italy. She serves as a member of the Interdepartmental Center PIC4SeR for Service Robotics and chairs the Robotics Laboratory (1999–present). With over 100 publications, her work bridges industrial robotics , mobile robotics , and Industry 4.0 . Education: Laurea (Electronic Engineering, cum laude , 1991) Ph.D. (Systems Engineering, 1995) Research Interests: Specializing in Collision detection and avoidance for industrial manipulators Friction modeling and compensation Path planning and autonomous navigation Smart manufacturing systems Human-robot collaboration Awards: James C. Hung Best Paper Award (ETFA 2013, 2024) euRobotics Technology Transfer Prize (2014, 2017) Leadership: As IEEE Senior Member , she chairs the IEEE Industrial Electronics Society’s Factory Automation Technical Committee and serves on editorial boards for IEEE Transactions on Industrial Informatics and IEEE/ASME Transactions on Mechatronics . She leads projects like SpaceItUp! (space robotics) and PNRR FAIR (AI-driven manufacturing).
