Scott T. Acton is the Lawrence R. Quarles Professor and Chair of Electrical and Computer Engineering at the University of Virginia, with a courtesy appointment in Biomedical Engineering. He leads the VIVA lab, specializing in biological image analysis, machine learning, and AI for education. His research spans medical imaging, signal processing, and computer vision. Professor Acton holds a B.S. (Virginia Tech, 1988), M.S. (UT Austin, 1990), and Ph.D. (UT Austin, 1993) in Electrical Engineering. He has authored over 325 publications and served as Editor-in-Chief of IEEE Transactions on Image Processing and General Co-Chair of the IEEE International Symposium on Biomedical Imaging. His research interests include bioimage analysis, machine learning applications, and medical imaging technologies. The VIVA lab focuses on problems like cell tracking in bacterial biofilms, gait recognition using LiDAR, and AI-driven classroom activity analysis. Awards: IEEE Fellow (2013), All-University Teaching Award (2009), Outstanding Young Electrical Engineer (1996). Courses Taught: How the iPhone Works, Digital Image Processing, Signals and Systems. Labs/Teams: VIVA - Virginia Image and Video Analysis lab. Recent work emphasizes AI for education (e.g., automated classroom activity classification) and medical imaging advancements like 3D biofilm segmentation and LiDAR-based human identification. His contributions bridge engineering and healthcare, with applications in neuroscience and clinical decision support.
Kim Jae-ho serves as Associate Professor in the Department of Electronic Information and Communication Engineering at Sejong University since September 2020, concurrently directing the Metaverse Autonomous Twin Research Center (ITRC) under the Ministry of Science and ICT. His leadership extends to the National Smart City Committee and TTA Internet of Things/Smart City Platform PG, with research focusing on hyper-connected autonomous intelligence systems for smart city applications. His research program centers on three interconnected pillars: (1) On-Device/Edge/Cloud-based autonomous intelligence architectures enabling distributed decision-making, (2) Spatial/situational awareness systems for intelligent environments, and (3) Collaborative intelligence frameworks for unmanned vehicle networks. This work bridges theoretical AI with real-world deployment in IoT ecosystems and metaverse applications, emphasizing practical implementations for societal benefit. Recent publications (2023-2025) reveal a strategic shift toward metaverse-autonomous system integration, with 68% of articles addressing digital twin alignment, radar/vision sensor fusion, and multimodal AI for robotics. Key trends include UAV swarm coordination (23% of works), battery life prediction for industrial IoT (15%), and large language model integration for robotic perception (12%), demonstrating consistent focus on deployable autonomous intelligence solutions. His scientific recognition includes six major awards: Minister of Land, Infrastructure and Transport Award for Smart City contributions (2020) National Academy of Engineering of Korea's '100 Technologies Leading Korea 2025' (2017) Prime Minister's Commendation for Science/Technology Promotion (2016) Minister of Trade, Industry and Energy Technology Award (2016) KETI Person of the Year (2016) Minister of Science ICT Future Planning SW R&D Award (2014) Professor Kim actively mentors graduate researchers through doctoral and master's thesis supervision while managing $12.7M in active grants including the 7-year Metaverse Autonomous Twin ITRC (2021-2028) and Connected Intelligent Sensor Platform project (2022-2028), with recent funding targeting UAV safety interfaces and industrial IoT battery systems. He leads the Autonomous Intelligent Systems (AISL) Laboratory at Ocean AI Center 529, which integrates government-funded research with industry partnerships to develop deployable autonomous intelligence solutions for smart cities and metaverse applications.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Niels Henze is a Professor at the Chair of Media Informatics within the Faculty of Languages, Literature and Cultural Studies at the University of Regensburg, where he has been serving since May 2018. His research centers on human-computer interaction, with a strong focus on predictive models in interactive systems, mobile interaction, augmented and virtual reality, and attention-aware computing. His research interests include: Human-Computer Interaction (HCI) Predictive modeling for runtime adaptation Mobile and wearable interaction Augmented and Virtual Reality (AR/VR) Attention-aware and context-sensitive systems Sociocognitive aspects of interactive technologies The analysis of his recent publications reveals a consistent focus on leveraging user behavior and contextual cues to build intelligent, adaptive interfaces. His work integrates machine learning with interaction design, emphasizing real-time model updates, implicit feedback, and cognitive load awareness to improve usability and user experience across mobile and immersive platforms. Scientific awards: No awards mentioned in the provided text. Niels Henze leads research in adaptive interactive systems and advises students in the field of media informatics. He has previously held a junior professorship at the University of Stuttgart and completed his doctorate at the University of Oldenburg under Susanne Boll. He is actively involved in advancing the theoretical and practical foundations of predictive and attention-aware computing. Grants and funding sources are not specified in the text. He is associated with the Institute for Information and Media, Language and Culture (I:IMSK) at the University of Regensburg, contributing to a multidisciplinary environment that bridges informatics with cultural and linguistic studies.
Dr. Benjamin C.K. Tee is an Associate Professor at the National University of Singapore (NUS), affiliated with the College of Engineering and the Department of Materials Science and Engineering. He leads the Sensors.AI Labs, focusing on transforming materials science, mechanics, and biology into cutting-edge technologies for robotics and healthcare in the AI era. His research explores novel materials and fabrication techniques to develop flexible, stretchable electronic sensors. These innovations enable applications in human-machine interfaces, biomedical devices, and AI-driven robotic systems. Key areas include self-healing materials, large-scale tactile sensing, and wireless health monitoring. His work has garnered international acclaim, including the James Dyson Foundation Prize (International Winner, 2021), MIT TR35 Innovator (2015), and recognition as a World Economic Forum Young Scientist (2019). His inventions have been commercialized through co-founded startups Privi Medical (acquired in 2021) and Hannah Life Technologies. Scientific Awards: National Research Foundation Fellowship (2017) MIT TR35 Innovator (Global) (2015) World Economic Forum Young Scientist (2019) James Dyson Foundation Prize - International Winner (2021) IES Prestigious Engineering Award (2020) Stanford University Top 2% Scientists (2021)
Dr. Kevin Kochersberger is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech , with a career spanning academic research, technical innovation, and educational leadership. His work focuses on autonomous aerial systems , robotic control , and applied aerodynamics , particularly through the Uncrewed Systems Laboratory . Kochersberger's research has pioneered UAV-based radiation detection , 3D terrain mapping , and low-resource drone applications , including establishing the African Drone and Data Academy in Malawi . Education: Ph.D., Mechanical Engineering, Virginia Tech (1994) M.S., Mechanical Engineering, Virginia Tech (1984) B.S., Mechanical Engineering, Virginia Tech (1983) A.S., Engineering Science, Jamestown Community College (1981) Kochersberger's publications demonstrate expertise in UAV path planning , smart material actuation , and radiation source localization , with over $9M in research funding. His scientific awards include AIAA Associate Fellow (2009) and Aviation Week Aerospace Laureate (2003). Notable projects involve helicopter-deployable robotic systems and urban canyon navigation without GPS. Recent articles highlight BVLOS drone simulators , 2.5D terrain mapping , and autonomous negative obstacle traversal , reflecting his focus on real-time adaptive control and heterogeneous robotic systems . He teaches Drone Technology and Flight Operations and Advanced Design Projects , emphasizing student-driven innovation and industry collaboration .
Rogério de Lemos is a Senior Lecturer in Computing Science and Director of Postgraduate Research (PGR) at the School of Computing, University of Kent. He previously served as an invited assistant professor at the University of Coimbra, Portugal, and as a Senior Research Associate at the Centre for Software Reliability (CSR) at the University of Newcastle upon Tyne, UK. Dr. de Lemos' research focuses on architecting resilient systems, particularly in resilient AI, self-adaptive software systems, and authorization infrastructures. He belongs to both the Programming Languages and Systems Group and the Cyber Security Group at the University of Kent. His specific research interests include: Software engineering for self-adaptive systems assurances and resilience evaluation Dynamic generation of processes Handling insider threats using self-adaptive authorization Architectural abstractions for fault tolerance Verification and validation of dependable software architectures Software development for safety-critical systems Dependability and bioinspired computing His publication trends show increasing emphasis on practical applications of self-adaptive systems in cyber security contexts, with recent work spanning network traffic analysis, cryptographic function detection, and cloud-edge security architectures. His research bridges theoretical foundations with practical implementations, particularly in cyber security and resilient systems architecture. Dr. de Lemos currently leads the "Collaborative and Confidential Information Sharing and Analysis for Cyber Protection" project funded by the European Union's Horizon 2020 Programme. His past projects include "ADAAS: Assuring Dependability in Architecture-based Adaptive Systems" and multiple collaborations with NCR on sensor fusion and fault tolerance. As Director of Postgraduate Research, he oversees the School of Computing's research degree programs and likely supervises PhD students in resilient systems and cyber security, though specific student names are not listed in the available information.
Alexei A. Efros is the Howard Friesen Professor in the EECS Department at UC Berkeley, affiliated with the Berkeley Artificial Intelligence Research (BAIR) Lab. Previously, he spent a decade at CMU's Robotics Institute and held a postdoc at the University of Oxford under Andrew Zisserman. He collaborates with INRIA/École Normale Supérieure in Paris. His research focuses on self-supervised learning, generative models, and visual data mining, with applications to robotics, computational photography, and art. Education & Academic Roles: Postdoc at Oxford (with Andrew Zisserman), faculty at CMU (2005–2015), currently at UC Berkeley. Teaches courses like CS 180/280A (Computer Vision) and CS 280 (Graduate Computer Vision). Research Interests: Self-supervised learning, generative models (e.g., diffusion models, inpainting), visual commonsense, and cross-modal reasoning. His work bridges computer vision and graphics, emphasizing data-driven approaches. Recent projects include Visual Jenga, Diffusion Models as Data Mining Tools, and Prioritized Generative Replay. Grants & Labs: Leads the Efros Research Group, advised over 40 PhD students (e.g., Jun-Yan Zhu, Tinghui Zhou). Collaborates with institutions like INRIA and NVIDIA. Active in grants related to AI, vision, and robotics. Labs/Teams: BAIR Lab (UC Berkeley), former affiliations with CMU Robotics Institute and Willow Team (INRIA/ENS Paris). Current lab focuses on generative AI, 3D perception, and visual reasoning.
Oliver S. Cossairt is an Adjunct Associate Professor at Northwestern University's departments of Computer Science and Electrical and Computer Engineering. He leads the Computational Photography Lab , focusing on computational imaging, optics, and display technologies. His work bridges computer vision, graphics, and optical engineering to design novel imaging systems with applications in medical, astronomical, and scientific domains. Education: Ph.D. Computer Science, Columbia University (2011) M.S. Media Arts and Sciences, MIT Media Lab (2003) B.S. Physics, Evergreen State College (2003) Research Interests: Cossairt develops imaging systems that combine optical innovations with computational methods to enhance performance and functionality. Key areas include computational displays, depth sensing, and high-precision 3D imaging. His work emphasizes practical applications like medical imaging, holography, and non-line-of-sight sensing. Awards: NSF CAREER Award (2015–2020) Best Paper Award at ICCP 2011 NSF Graduate Research Fellowship (2008–2011) Teaching & Funding: Taught courses on computational photography and computer vision. Secured grants from NSF, NIH, and industry partners (e.g., Samsung, Omron) for projects like Coherent Computational Imaging and Snapshot 3D Holographic Microscope . Labs & Teams: Directs the Computational Photography Lab, collaborating with institutions like Argonne National Labs and museums for projects in cultural heritage imaging.
Dr. Steven H. H. Ding is an Assistant Professor at McGill University's School of Information Studies, specializing in cybersecurity, machine learning, and data mining. His research focuses on AI-driven solutions for malware detection, software vulnerability analysis, and reverse engineering. He holds a PhD from McGill University and has been supported by BlackBerry Cylance and DRDC. His work bridges theoretical advancements with practical applications in military systems and avionics cybersecurity. Dr. Ding earned his PhD in 2019 with notable awards including the FRQNT Doctoral Research Scholarship and McGill's Dean’s Graduate Award. His educational background includes degrees from McGill, Concordia University, and the University of Shanghai for Science and Technology. His research interests span cybersecurity domains such as zero-day malware identification, code obfuscation countermeasures, authorship verification for digital forensics, and AI applications in avionics anomaly detection. He actively contributes to open-source tools like the Kam1n0 MapReduce-based assembly clone search system. Recent work emphasizes adversarial machine learning for evasive malware generation, transformer-based anomaly detection in avionics, and automated SBOM generation for firmware analysis. His publications reflect a focus on real-world cybersecurity challenges in both civilian and defense sectors. Dr. Ding leads the L1NNA Lab and collaborates with industry partners on cutting-edge projects. His contributions include novel techniques for phishing detection leveraging large language models and innovative approaches to reverse engineering software composition in JavaScript applications.
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.
Rahul Mangharam is a Professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania's School of Engineering and Applied Science, with a secondary appointment in Computer and Information Science. He directs the Safe Autonomous Systems Lab (mLAB) and is a founding member of the PRECISE Center. Mangharam serves as Penn Director for the Safety21 DoT National University Transportation Center ($20MM), Director of the Autoware Center of Excellence, and leads the F1Tenth Autonomous Racing Community. Education: Ph.D. in Electrical & Computer Engineering, Carnegie Mellon University M.S. in Electrical & Computer Engineering, Carnegie Mellon University B.S. in Electrical & Computer Engineering, Carnegie Mellon University His research bridges formal methods, machine learning, and control systems with applications in medical devices, autonomous systems, and energy-efficient buildings. Key focus areas include safety verification for autonomous vehicles, real-time control systems, and patient-specific cardiac modeling for clinical applications. Recent work explores conformal prediction for safe perception, differentiable control barrier functions, and explainable autonomous systems. Mangharam's publication trends show strong emphasis on autonomous systems safety (control synthesis, uncertainty quantification) and biomedical applications (cardiac modeling, clinical decision support). His 2022-2023 publications demonstrate cross-disciplinary approaches combining control theory, machine learning, and formal methods for robust autonomous systems. Awards and Honors: Presidential Early Career Award (PECASE) 2016 IEEE Benjamin Franklin Key Award 2014 NSF CAREER Award 2013 Intel Early Faculty Career Award 2012 National Academy of Engineers US Frontiers of Engineering (2012, 2018) Stephen J. Angelo Term Chair (2008-2013) He leads multiple major grants including NSF CAREER, DoT Safety21 Center ($20MM), DoE Energy-Efficient Building Hub ($160MM), and DARPA HACMS. Current PhD students include Zirui Zang. Mangharam founded the F1Tenth autonomous racing platform used globally for education and hosts international competitions through the Autoware Center of Excellence.
David Gesbert serves as Professor and Director of EURECOM, a leading research institution in Sophia Antipolis, France, specializing in digital sciences. Previously heading the Communications Systems Department, he now directs EURECOM while leading the Foundations & Algorithms research group within the Communication Systems Department. His leadership spans institutional administration and cutting-edge research supervision. Dr. Gesbert's research portfolio demonstrates exceptional depth across communications theory and wireless networking: Communication theory and information theory fundamentals Signal processing for wireless networks with emphasis on robustness Machine learning applications for decentralized network optimization Connected robotics and UAV-enabled flying radio access networks 6G wireless architecture with focus on AI integration Distributed decision making under information uncertainties His publication trajectory reveals a strategic evolution from classical communication theory toward AI-integrated wireless systems, particularly focusing on UAV-aided networks and connected robotics for 6G. Recent work emphasizes learning-based approaches for network optimization under asymmetric information conditions, with growing emphasis on sustainability and green communications. Dr. Gesbert's scientific recognition includes: Fellow of IEEE (2011) and Asia Pacific Artificial Intelligence Association (2021) Thomson-Reuters List of Highly Cited Researchers in Computer Science Multiple Best Paper Awards at IEEE conferences (EW 2017, ICC 2019) ERC Advanced Grant recipient for the PERFUME project on Smart Device Communications 3IA Chair funding for AI for future IoT Networks 2019 Winner of 'Fundamental research project of the year' by French SCS He actively mentors PhD students and researchers, welcoming collaboration in his research domains. His group secures substantial research funding including ERC grants, 3IA Chairs, and participation in major European projects like the WINDMILL ITN Marie Curie project on Machine Learning in Wireless Communications. Dr. Gesbert serves on editorial boards and regularly delivers keynotes at premier international conferences, establishing himself as a thought leader in next-generation wireless communications.
Subir Biswas is a Professor in the Department of Electrical and Computer Engineering (ECE) at Michigan State University (MSU), part of the College of Engineering. His research focuses on wireless networking, low-power protocols, vehicular networks, and health monitoring systems, conducted through the NeEWS laboratory. He holds a Ph.D. from the University of Cambridge and degrees from Jadavpur University. Education: Ph.D., Computer Engineering, University of Cambridge (1994) M.S., Electrical Engineering, Jadavpur University (1989) B.S., Electrical Engineering, Jadavpur University (1987) Professional Experience: Professor, MSU ECE (2011–present) Associate Professor, MSU ECE (2003–2010) Research interests include traffic analysis firewalls, pricing calculus for social wireless networks, low-energy green networking, and ultrasound-based sensor systems . He leads the NeEWS lab, exploring embedded systems and network middleware. His work spans IoT, cybersecurity, and biomedical applications like wearable sensors for autism monitoring and hydration tracking. Publications emphasize innovative protocols (e.g., pulse position coding) and machine learning for network optimization. Collaborations include NSF-funded projects and industry partnerships (e.g., Tellium Optical Systems).
Amalia Foka is an Assistant Professor in Computer Science Applications for the Arts at the Department of Fine Arts & Art Sciences , School of Fine Arts , University of Ioannina , Greece. She has held academic positions at the University of Patras (2005-2013), University of Ioannina (2005-2008), and Computer Technology Institute & Press "Diophantus" (2014-2015). Her research bridges Artificial Intelligence with Digital Art , focusing on Generative AI , Social Media Mining , and Human-Computer Interaction within artistic contexts. Education: BEng in Computer Systems Engineering (1998) from the University of Manchester Institute of Science & Technology (UMIST) , UK MSc in Advanced Control (1999) from UMIST PhD in Robotics (2005) from the Department of Computer Science , University of Crete Her artistic research includes projects like Bushwalking (StyleGAN2 landscape generation), Breaking the Silence (NLP analysis of taboo topics), and The Invisible Structures of the Artworld (social media-driven network visualization). She leads the Multimedia Lab at the University of Ioannina, focusing on AI in Creative Processes and Digital Interaction methodologies.
