Andrew Thomas Campbell is a Professor and Albert Bradley 1915 Third Century Professor in the Department of Computer Science at Dartmouth College. His research focuses on ubiquitous computing, machine learning, and mental health, particularly using mobile and wearable sensors to assess and manage mental illnesses. He leads the StudentLife project, which tracks college students' mental health over four years, and co-directs the HealthX Lab. Campbell's work has received prestigious awards, including the ACM UbiComp 10-Year Impact Award for pioneering mobile sensing in mental health. He previously held tenure as an Associate Professor at Columbia University and has industry experience at Google and Verily. His research spans $42M in grants from NIH, NSF, and corporate partners, emphasizing technology-driven solutions for mental health challenges. Education: B.Sc. from Aston University, M.Sc. from City University, Ph.D. from Lancaster University. He teaches CS 1 Introduction to Programming and mentors numerous students. Awards include the Dean of the Faculty Award for Mentoring (2025) and multiple ACM Test of Time Awards. His lab collaborations involve跨学科 teams addressing mental health through AI and sensing technologies.
Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.
Hans Jonas Fossum Moen is an Associate Professor with a 20% appointment at the Department of Technology Systems, University of Oslo (UiO), and holds a 100% position as a researcher at the Norwegian Defence Research Establishment (FFI). His primary affiliation is with the Section for Autonomous Systems and Sensor Technologies. He is based at the Kjeller campus, with a visiting address at Gunnar Randers Road 19 and a postal address at Postboks 70. His research focuses on advancing autonomous systems and sensor technologies, particularly in the domains of swarm robotics, multi-agent coordination, and optimization algorithms. Key areas include UAV navigation, distributed localization in IoT networks, radar detection enhancement, and adaptive control systems for multi-functional swarms. He emphasizes the integration of biological principles into robotic systems, as evidenced by his participation in the ICRA 2018 Workshop on Swarms. His publications consistently highlight contributions to swarm intelligence, with a focus on improving data quality and efficiency in robotics applications. He has collaborated extensively with colleagues such as Kyrre Glette, Oleg Yakimenko, and Jan Dyre Bjerknes, exploring topics ranging from task allocation in multi-agent systems to evolutionary algorithms for filter optimization. His work bridges theoretical computer science with practical engineering challenges in autonomous systems. No scientific awards have been explicitly mentioned in the provided texts. Moen’s advising and grants narrative indicates no listed advisees or active grant projects, though his 20% UiO position suggests potential involvement in academic supervision. His primary research activities are embedded within FFI and the Autonomous Systems section at UiO, contributing to interdisciplinary efforts in sensor technologies and robotic systems.
Martin Norgren is a Professor at KTH Royal Institute of Technology, leading the Department of Electromagnetic Fusion Physics. His research focuses on electromagnetic inverse problems, including material characterization, biomedical imaging (e.g., brain current sources), environmental monitoring (e.g., snow and avalanche prediction), and smart grid technologies. He specializes in reconstructing object properties using electromagnetic measurements and has contributed to applications in healthcare, energy systems, and environmental science. His work involves advanced analytical and numerical methods such as mode-matching techniques, perturbation theory, and convex optimization. Notable projects include noncontact current measurement in power grids and transformer diagnostics using microwave radiation. Norgren teaches courses in electromagnetic field theory and electrical engineering design, emphasizing practical applications and interdisciplinary collaboration. Recent research trends highlight advancements in glide/twist symmetry-based metamaterial design, waveguide analysis, and inverse scattering techniques. His studies bridge fundamental physics with applied engineering, addressing challenges in energy infrastructure and medical diagnostics. As a department head, he oversees educational and research programs at KTH, fostering innovation in electromagnetism and fusion physics. His contributions to curriculum development include project-based courses integrating theory and hands-on design.
Professor Raja Jurdak is a leading academic in distributed systems and applied data sciences at Queensland University of Technology (QUT), where he directs the Trusted Networks Lab. He holds dual roles as Professor of Distributed Systems and Chair in Applied Data Sciences, alongside leadership in the Centre for Data Science. His research focuses on dynamic network modeling, blockchain-based trust frameworks, and IoT applications, with particular emphasis on cybersecurity, energy efficiency, and mobility-driven diffusion processes. Jurdak formerly led CSIRO's Distributed Sensing Systems Group and maintains a visiting scientist role there. Education: PhD in Information and Computer Science, University of California, Irvine MS in Computer Networks and Distributed Computing, University of California, Irvine BE in Computer and Communications Engineering, American University of Beirut Research Interests: Network science, blockchain technology, IoT security, sustainable energy systems, and data-driven decision-making. His work bridges theoretical advancements with practical applications in smart grids, health surveillance, and urban mobility. Awards: Finalist for the 2019 Eureka Prize, multiple CSIRO accolades, and IEEE Senior Member status. His research has received industry recognition for interdisciplinary innovation, including the DiNeMo project's real-time disease surveillance system. Advisory & Grants: Leads high-impact projects funded by government and industry partnerships. Supervises PhD candidates in areas like decentralized data processing and privacy-preserving AI. Holds editorial roles at journals such as Ad Hoc Networks and PLoS ONE . Labs & Teams: Directs the Trusted Networks Lab at QUT, fostering collaborations with institutions like Oxford University and MIT. His work emphasizes cross-disciplinary teams to address global challenges in cybersecurity and sustainable systems.
Dr. Jean-Philippe Couderc is a Research Assistant Professor of Medicine in the Cardiology Department at the University of Rochester Medical Center and Chief Technology Officer of iCardiac Technology Inc. He holds a PhD in Biomedical Engineering from the French National Institute of Applied Sciences (1997), an MBA in Healthcare Management from the Simon School of Business (2003), and an MS in Medical Specialties from a French institution (1994). His research focuses on quantitative electrocardiography, ventricular repolarization, and cardiac safety, with contributions to clinical study design and medical software development. He leads the Heart Research Follow-up Program Laboratory and serves on the editorial board of Annals of Non-Invasive Electrocardiology . Notable awards include the Frost & Sullivan Technology Innovation Award (2006) and the Mirowski-Moss Career Development Award (2003). His work spans federal grants, industry collaborations, and over 100 peer-reviewed articles. Research Interests: Dr. Couderc’s work integrates computational science, electrophysiology, and clinical cardiology to address challenges in cardiac safety, drug evaluation, and wearable health technologies. His lab develops novel ECG analysis tools and evaluates their application in arrhythmia monitoring, drug-induced QT prolongation, and non-invasive diagnostics. Publications: His recent work includes advancements in video-based cardiac monitoring, demographic factors in ECG patch usage, and biomarker identification for epilepsy and heart conditions. He co-authored key consensus statements on mHealth in arrhythmia management, emphasizing digital tools for heart rhythm professionals. Awards & Grants: Dr. Couderc has secured NIH and industry grants, leading projects on repolarization dynamics and cardiac resynchronization therapy. He advises on FDA drug evaluation and contributes to international clinical guidelines.
Dr. Dave Rowe is a Senior Research Fellow at the University of Southampton, affiliated with the Optoelectronics Research Centre (ORC). His research focuses on mid-infrared spectroscopy, silicon photonics, and their applications in biomedical engineering and sensing technologies. He has contributed to advancements in therapeutic drug monitoring, point-of-care diagnostics, and machine learning-driven biomarker analysis. Currently supervising PhD students Daniel Owens (PhD Infec Inflamm&Immunity PT) and Nikita Anastasia Sapphire Lack (PhD ORC), his work bridges fundamental photonics research with real-world clinical and industrial applications. Collaborations include projects on mid-infrared photonic devices, terahertz spectroscopy, and integrated circuit design. Contact: D.Rowe@soton.ac.uk.
Cagdas Onal is an Associate Professor of Robotics Engineering at Worcester Polytechnic Institute (WPI). He holds a BS and MS from Sabanci University (2003, 2005) and a PhD in Robotics from Carnegie Mellon University (2009). His research focuses on soft robotics, bio-inspired systems, and control theory , emphasizing the development of flexible robotic components for healthcare, industry, and sustainable applications. He leads the Soft Robotics Lab and the Future of Robots in the Workplace (FORW-RD) initiative, advancing human-centric robotics solutions. Research interests include designing bio-inspired soft robots (e.g., origami-inspired snake robots), developing modular actuation systems with embedded sensors, and exploring applications in medical devices and assistive technology. His work aligns with UN Sustainable Development Goals, particularly in healthcare access (SDG 3), quality education (SDG 4), and innovation (SDG 9). Recent projects include origami-based robotic arms for wheelchair users , self-contained underwater robots, and haptic interfaces for teleoperation. His lab collaborates on国家级 grants like the NSF-funded NRT Program and has secured patents for actuator designs (e.g., Hydro Muscle). Labs/Teams: Soft Robotics Lab, FORW-RD, NRT Program. Notable media coverage includes Worcester Telegram & Gazette and Spectrum News for innovations in human-friendly robotics.
Federico Becattini is a Tenure-Track Assistant Professor at the Department of Information Engineering and Mathematics (DIISM), University of Siena, Italy. He is an active member of the Siena Artificial Intelligence Lab (SAILab), where he contributes to cutting-edge research in computer vision, deep learning, and artificial intelligence. His work spans multiple interdisciplinary domains, including autonomous driving, human behavior understanding, cultural heritage, neuromorphic vision, and fashion recommendation. His research interests center on memory-based neural networks , which he has applied in numerous publications at top-tier venues such as CVPR, ECCV, IEEE TPAMI, and ACM TOMM. He has also delivered tutorials on this topic at international conferences including ICIAP 2022 and ACM MM 2022, and taught a Ph.D. course at the University of Florence. His recent work is aligned with the Collectionless AI paradigm, which emphasizes continual learning and interaction with dynamic environments. The recent publications highlight a strong trend in human-centric AI , focusing on understanding people through multimodal analysis of face, body, and clothing, as well as generating 3D virtual avatars. There is also a clear emphasis on memory-augmented architectures for temporal reasoning, explainability, and adaptive learning. His editorial role as Associate Editor of the International Journal of Multimedia Information Retrieval further underscores his standing in the research community. Associate Editor, International Journal of Multimedia Information Retrieval (IJMIR) Organizer, Workshop on Facial and Body Expressions (ICPR2020) Co-organizer, T-CAP Workshop (ICIAP2021, ICPR2022) Co-organizer, MCFR Workshop (ACM MM 2022) Co-organizer, WCPA Workshop and Challenge (ECCV 2022) Federico Becattini actively advises students and researchers within SAILab, particularly in the context of Ph.D. theses and research projects related to Collectionless AI and memory-based models. While specific grants are not mentioned, his extensive publication record and leadership in workshops and editorial roles suggest involvement in funded research initiatives. He collaborates with both academic and international research communities, serving as a reviewer for top-tier conferences and journals. He is a core member of the SAILab research group, which is pioneering the Collectionless AI initiative—a framework for continual learning over time, interacting with humans and agents without relying on pre-built static datasets. This lab serves as a hub for innovation in adaptive and sustainable AI systems.
Tegoeh Tjahjowidodo is a Senior Lecturer at the Faculty of Industrial Engineering Sciences , KU Leuven , affiliated with the Department of Mechanical Engineering and the Manufacturing Processes and Systems (MaPS) unit at Campus De Nayer. He serves as Head of Education for Electromechanics programs and leads Subdivision 17 at the campus. Research Areas: Additive Manufacturing (Wire-Arc Additive Manufacturing), Process Monitoring, Control Systems, Laser Micromanufacturing, Wear Analysis, Robotics, and Condition Monitoring. Publication Trends: Focus on in-situ monitoring of laser micromanufacturing, machine learning for abrasive belt grinding, WAAM parameter optimization , and multi-sensor fusion for process control. Scientific Contributions: Co-promotor for MultiTRIBO (tribology), Promotor for WAAM structural integrity and pedicle screw surgical simulators . Active in international collaborations (e.g., 25th International Symposium on Laser Precision Microfabrication, Spain 2024).
Falko Dressler is a Full Professor and Chair for Telecommunication Networks at the School of Electrical Engineering and Computer Science, Technische Universität Berlin. He holds a Ph.D. and M.Sc. in Computer Science from Friedrich-Alexander University of Erlangen-Nuremberg (1998-2003). His research focuses on next-generation wireless systems , distributed machine learning , edge computing , and applications in Internet of Things (IoT) , cyber-physical systems , and internet of bio-nano-things . Editorial roles: IEEE Trans. on Mobile Computing, Elsevier Computer Communications, IEEE/ACM Trans. on Networking Conference leadership: IEEE INFOCOM, ACM MobiSys, IEEE VNC Textbooks: Self-Organization in Sensor and Actor Networks (Wiley), Vehicular Networking (Cambridge) Recent publications highlight trends in Edge Computing Resilience and 6G Network Architecture , with a strong emphasis on Molecular Communication , Terahertz Band Synchronization , and Federated Learning in vehicular environments. Scientific contributions include multiple IEEE Fellow , ACM Fellow , and VDE ITG Prize 2023 recognitions. Advisory and professional activities include membership in the German National Academy of Science and Engineering (acatech), IEEE COMSOC Conference Council, and ACM SIGMOBILE Executive Committee. His work spans cooperative driving, ultra-low power sensor networks, and security in nano-communication systems.
Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.
Kohei Nakajima is an Associate Professor at the Department of Intelligent Mechano-Informatics, Graduate School of Information Science and Technology, The University of Tokyo. He holds concurrent positions at the Department of Creative Informatics and the Next Generation Artificial Intelligence Research Center (AI Center). As an Endowed Chair in Advanced Artificial Intelligence Education, he leads the Physical Intelligence Lab, which focuses on the intersection of soft robotics, nonlinear dynamics, and physical computing. His research interests center on Physical Reservoir Computing (PRC), a paradigm that exploits the natural dynamics of physical systems for computation, with applications in soft robotics, spintronics, and quantum machine learning. Nakajima's work demonstrates how physical systems can inherently process information without traditional digital computation, leveraging phenomena like chaos, bifurcations, and embodied intelligence. Nakajima's publications reveal a strong focus on understanding how physical systems can perform computational tasks. His recent work spans from biological applications (jellyfish cyborgs, ostrich-inspired robotics) to fundamental theoretical advances in reservoir computing. The research demonstrates how physical phenomena can be harnessed for information processing, with implications for energy-efficient computing and novel robotic control paradigms. As the organizer of the Reservoir Computing Seminar, Nakajima has built a vibrant research community exploring the nature of information processing across disciplines. His lab actively recruits graduate students and postdocs, indicating strong research momentum and institutional support for his work in physical intelligence.
Ricardo Zednik is a Professor at the Department of Mechanical Engineering, École de Technologie Supérieure (ÉTS) in Montreal. Holding degrees from Rice University (BA, BS) and Stanford University (MS, PhD), he specializes in piezoelectric materials, fracture mechanics, and microelectronic systems. His research focuses on sensors, innovative materials, and health technologies. Fields of Interest: Piezoelectricity, Fracture Mechanics, MEMS, Smart Materials, Crystallography With over 36 peer-reviewed publications and extensive supervision of graduate research (including 15+ co-directed theses and projects since 2016), Zednik contributes to applied research in materials science and biomedical engineering. He collaborates with LaCIME and PULÉTS laboratories on cutting-edge projects involving ultrasonic transducers, flexible sensors, and high-temperature material characterization. Current courses include Materials Technology (MEC200) and advanced research topics in Functional and Smart Materials (SYS877). His students explore applications like terahertz quality control, piezoelectric earcanal sensors, and Kirigami techniques for wearable electronics.
Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.
/hjmoen/j.png)
