Dr. Mario Bukal is an Assistant Professor at the University of Zagreb’s Faculty of Electrical Engineering and Computing, Department of Applied Mathematics. He specializes in Partial Differential Equations (PDEs), particularly evolution equations and systems, with current involvement in the Croatian Science Foundation-funded project Mathematical Analysis of Multi-Physics Problems Involving Thin and Composite Structures and Fluids (MAMPITCoStruFl) . His research focuses on fluid-structure interaction, nonlinear systems, and mathematical modeling. He has taught courses in Mathematics, Probability & Statistics, and Stochastic Processes at both undergraduate and PhD levels, including roles at Vienna University of Technology. Education & Positions: Holds a PhD and has taught at multiple institutions, including TU Wien. Research Interests: Nonlinear PDEs, fluid dynamics, quantum mechanics, and applied mathematical analysis. His recent publications emphasize rigorous derivation of reduced models for complex systems, entropy analysis in quantum diffusion, and numerical schemes for fourth/sixth-order equations. Bukal actively contributes to seminars and conferences on nonlinear analysis and differential equations.
Prof. Juergen Gall is a Professor at the University of Bonn, affiliated with the TRA Mathematics, Modelling and Simulation of Complex Systems and the Department of Information Systems and Artificial Intelligence. He leads the Computer Vision Group at the Lamarr Institute for Machine Learning and Artificial Intelligence. His research focuses on action recognition, video understanding, anticipation, human pose estimation, and applications in plant science, earth science, and neuroscience. He has contributed to numerous projects and conferences, including organizing workshops on machine learning for Earth systems and holistic video understanding. His work spans over 200 publications in top venues like CVPR, ICCV, and NeurIPS. He oversees software tools like MANTA and STING-BEE, and has developed datasets such as Humans in Kitchens and PoseTrack21. Research interests emphasize advancing computer vision for real-world challenges, with a focus on multi-modal learning and deep learning applications. Recent articles explore parameter-efficient models, diffusion-based anticipation, and multi-view matching for plant analysis. His contributions bridge academia and industry, addressing agricultural and environmental monitoring needs.
Dr. Xingchen Zhang is a Marie Skłodowska-Curie Individual Fellow at the Personal Robotics Laboratory (PRL), Imperial College London, and holds a Lecturer position in the Department of Electrical and Electronic Engineering. Previously, he served as a Teaching Fellow and Research Associate at Imperial College. He teaches Deep Learning courses and is actively involved in academic reviews for prestigious programs like UKRI Future Leaders Fellowships and top conferences/journals such as CVPR, ECCV, and IEEE TNNLS. Xingchen earned his BSc from Huazhong University of Science and Technology (2012) and PhD from Queen Mary University of London (2018). His research focuses on human motion prediction, pose estimation, image fusion techniques (visible-infrared, multi-focus), visual object tracking, and deep learning applications in computer vision. His work has led to a notable book on image fusion, awarded the National Science and Technology Academic Publications Fund in China (2019). His recent publications emphasize real-world applications such as traffic flow prediction, pedestrian privacy protection, and fusion-based object tracking. He contributes to advancing graph neural networks for mobility analysis and spatio-temporal modeling, while maintaining a strong focus on interdisciplinary robotics and AI solutions. Awards: Marie Curie Fellowship, National Science & Technology Academic Publications Fund (2019) Key Roles: Reviewer for UKRI FLF, CVPR, ECCV, IJCV Labs: Personal Robotics Lab (PRL), Imperial College London
Beat Signer is Professor of Computer Science at Vrije Universiteit Brussel (VUB) and Director of the Web & Information System Engineering (WISE) laboratory. His research focuses on cross-media information spaces and architectures (CISA) spanning interactive paper, dynamic data physicalisation, and tangible holograms. His educational background includes a Computer Science degree from ETH Zurich, where he completed his PhD thesis on fundamental concepts for interactive paper and cross-media information spaces in 2005. Key research areas include: Cross-media document formats and resource-selector-link (RSL) hypermedia metamodel Context-sensitive adaptation and cross-media transclusion Multimodal interfaces (Mudra, iGesture) and tangible computing Internet of Things interoperability and semantic middleware Recent publications (2023-2025) reveal strong trends in dynamic data physicalisation hardware, FAIR positioning systems (OpenHPS), end-user IoT development (eSPACE), and next-generation human-information interaction paradigms. His work increasingly integrates Solid protocols for decentralized data and explores thermal dimensions in tangible holograms. As Principal Investigator, he leads multiple EU-funded projects including OpenHPS, TangHo, and eSPACE. His team has developed influential frameworks like MindXpres for cross-media presentations and CMT for context modeling. He supervises 13 doctoral researchers including Dr. Maxim Van de Wynckel and Dr. Audrey Sanctorum, focusing on cross-media applications in education, IoT, and data visualization. His WISE laboratory develops innovative solutions bridging digital and physical information spaces through projects like ArtVis for collaborative data exploration and TangHo for physically augmented virtual objects.
Dr. Reginald R. (Reg) Souleyrette is the Chair of the Department of Civil Engineering and Commonwealth Professor of Transportation Engineering at the University of Kentucky. He also serves as Program Manager for Planning and Education at the Kentucky Transportation Center. With over 25 years of experience, he teaches courses in highway safety, GIS, and airport design. His research focuses on transportation data analytics, traffic safety, railroad engineering, and sustainable infrastructure, with over $10M in sponsored projects. Education: PhD in Civil Engineering (Transportation) from UC Berkeley (1989), M.S. and B.S. in Civil Engineering from University of Texas (1986, 1984). Licensed as a Professional Engineer in KY and IA. Research interests include transportation policy, data-driven decision support, and multimodal safety systems. He is a Fellow of the Association of Traffic Safety Information Systems Professionals and active in TRB committees, serving as Communications Coordinator for its Data and Information Systems Section. Key contributions include railroad crossing safety tools (e.g., FARSA system), automated safety assessment frameworks, and innovations in traffic incident management. His work spans international case studies, policy analysis, and advanced modeling techniques.
Ravishankar Ramanathan is an Assistant Professor at the University of Hong Kong's School of Computing and Data Science, Department of Computer Science. His research spans Quantum Cryptography, Quantum Information Theory, and Human-Robot Interaction. He holds a PhD from the National University of Singapore (2013), with prior postdoctoral roles at The National Quantum Information Centre (Poland), Université Libre de Bruxelles, and the University of Oxford. His work includes foundational studies in quantum mechanics and advanced robotics applications, with over 30 refereed publications in top journals like Nature Communications and Physical Review Letters. Research interests focus on quantum device-independent protocols, randomness amplification, and human-centric robotics systems. His lab (QIFT Group: https://qift.weebly.com/) explores both theoretical quantum mechanics and practical robotics solutions for mobility assistance and social interaction. Recent publications highlight contributions to quantum cryptography (e.g., noise-tolerant protocols) and autonomous systems (e.g., robotic wheelchair navigation and social robot design). He collaborates internationally, bridging quantum theory with real-world robotics applications.
Sam Leroux is a Tenure Track Assistant Professor and IMEC Postdoctoral Researcher at Ghent University's Faculty of Engineering and Architecture, Department of Information Technology. His research spans multiple interdisciplinary domains where artificial intelligence meets real-world applications, with particular emphasis on machine learning, deep neural networks, and distributed systems. He maintains active collaborations with IMEC and contributes to several EU and national research initiatives focused on practical AI deployment. Dr. Leroux's research interests center on developing efficient and privacy-aware machine learning systems that can operate effectively on resource-constrained devices. His work bridges theoretical advances in neural network architectures with practical applications in agriculture, healthcare monitoring, industrial IoT, and sustainable computing. He has pioneered approaches in adaptive neural networks that dynamically adjust computation based on available resources, enabling AI deployment at the network edge without compromising privacy. His publication record reveals a clear trajectory from foundational work in neural network architectures toward increasingly applied research. Recent publications demonstrate strong focus on privacy-preserving AI techniques, hardware-efficient machine learning, and computer vision applications in agriculture and industrial settings. His work consistently addresses the tension between model performance and resource constraints, with growing emphasis on ethical considerations in AI deployment. As an academic supervisor, Leroux currently guides numerous doctoral researchers across diverse projects including broiler welfare monitoring, hardware-efficient continuous learning, UAV-based agricultural sensing, and privacy-aware ergonomic analysis. He serves as Promotor for the 'Hardware-efficient continuous learning' project funded by the Special Research Fund, demonstrating his leadership in securing competitive research funding. His work environment features strong connections between Ghent University's academic research and IMEC's technological expertise, creating a fertile ground for translating theoretical advances into practical solutions. This positioning enables his research group to tackle challenges spanning from algorithm development to hardware implementation, with particular focus on real-world validation of proposed techniques.
Prof. Abhinav Valada is a Full Professor (W3) at the University of Freiburg, leading the Robot Learning Lab and serving as Chair of the IEEE RAS Technical Committee on Robot Learning. He holds affiliations with the Department of Computer Science , BrainLinks-BrainTools , and the ELLIS unit Freiburg . His research focuses on enabling robots to learn continuously through perception and interaction in complex environments, emphasizing scalable lifelong learning systems. Education: PhD (summa cum laude), University of Freiburg (2019) MS in Robotics, Carnegie Mellon University (2013) BTech in Electronics & Instrumentation, VIT University (2010) Research Interests: Robotics, machine learning, computer vision, with specializations in robot perception, state estimation, and planning. Current work emphasizes open-vocabulary scene understanding , uncertainty-aware perception , and embodied AI . Projects include multi-modal SLAM systems, neural radiance fields for robotics, and generalizable manipulation policies. Awards: IEEE RAS Early Career Award (2023) NVIDIA Research Award (2022) AutoSens Most Novel Research Award (2022) DFG Emmy Noether AI Fellowship (2021) Grants & Labs: Director of the Robot Learning Lab , co-founder of Platypus LLC (2013–2015), and collaborator on projects like SORTIE (disaster response robotics). Active in funding initiatives focusing on autonomous systems and AI ethics.
Karthik Desingh is an Assistant Professor in the Department of Computer Science and Engineering at the University of Minnesota, College of Science and Engineering. His research focuses on robotics, computer vision, and artificial intelligence, particularly in robot perception, object representation, and manipulation learning. His research interests span robotics , computer vision , machine learning , and human-robot interaction . He investigates how robots can perceive and interact with articulated and multi-object environments using physics-based models, belief propagation, and deep learning techniques. A significant portion of his work emphasizes unsupervised and end-user-directed learning for robot manipulation. The recent publications reveal a strong trend in multi-object representation , visual dynamics modeling , and vision-language integration for robot learning. His work frequently appears in top-tier robotics conferences such as IEEE ICRA and journals like IEEE Transactions on Robotics and IEEE Robotics and Automation Letters. While no formal awards are listed, his contributions to robotics and AI are evident through consistent high-impact publications and active research in perception for general-purpose manipulation. He advises students in robotics and AI, though specific names are not listed. His research is likely supported by academic and federal grants, given the sustained output and publication in peer-reviewed venues. He leads or contributes to a robotics research group focusing on perception and learning for manipulation tasks. The work contributes to UN Sustainable Development Goals through advancements in intelligent systems and automation. His lab or research team engages in interdisciplinary collaboration, particularly in computer vision and robotics, with recent work involving vision-language models and policy learning for assembly tasks.
Jun Luo is an Associate Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He earned his PhD in Computer Science from EPFL under the supervision of Prof. Jean-Pierre Hubaux and completed postdoctoral research at the University of Waterloo. He joined NTU in 2008 as an Assistant Professor and was promoted to Associate Professor in 2014. He served as Deputy Director of the Centre for Multimedia and Network Technology from 2010 to 2013. Education: PhD in Computer Science, Swiss Federal Institute of Technology in Lausanne (EPFL), 2006 MS in Electrical Engineering, Tsinghua University, 2000 BS in Electrical Engineering, Tsinghua University, 1997 Research Interests: Jun Luo's research focuses on mobile and pervasive computing, wireless networking, machine learning, and applied operations research. His primary research thrusts include: Contact-free Sensing Driven by Deep Learning : Leveraging RF, acoustic, and visible light signals for human activity recognition, respiration monitoring, and localization without wearable devices. Visible Light Communication and Sensing : Exploring LED-camera systems for data transmission, occupancy inference, and indoor broadcasting. Indoor and Outdoor Localization and Tracking : Developing systems using WiFi, geomagnetism, and crowdsourced data for precise positioning. Machine Learning for Mobile Networking : Applying deep learning and optimization to improve wireless network performance, mobile crowdsensing, and resource allocation. Publication Trends: His recent publications (2021–2023) demonstrate a strong focus on deep learning-enhanced sensing using RF and acoustic signals, particularly for health monitoring (e.g., respiration, heartbeat), multi-person tracking, and privacy-preserving techniques. He frequently collaborates with researchers in signal processing, computer vision, and networking, publishing in top venues like IEEE Transactions on Mobile Computing, MobiCom, and INFOCOM. His work emphasizes practical deployment on commodity devices and integration of sensing with communication systems. Scientific Recognition: IEEE Fellow Advising and Grants: Dr. Luo has advised numerous PhD and Master's students, as evidenced by the extensive list of student co-authors across his publications. He has led significant research projects in wireless sensor networks, mobile computing, and IoT systems, likely supported by competitive grants from Singaporean and international funding agencies. His role as Deputy Director of a research center indicates leadership in managing research teams and collaborative efforts. Labs and Teams: He leads a research group focused on mobile and distributed computing, deep learning, and computer vision. His team actively publishes in top-tier conferences and journals, working on projects involving RF sensing, acoustic platforms, visible light communication, and privacy-aware systems. The group collaborates with researchers both within NTU and internationally, particularly in Canada and China.
Yuting Wang is a Tenure-Track Associate Professor at Shanghai Jiao Tong University , specifically affiliated with the John Hopcroft Center for Computer Science. Their research focuses on Formal Verification , Programming Languages , and Logical Frameworks , with significant contributions to verified compilation of system software. Core developer of the Abella theorem prover for higher-order reasoning Lead developer of Stack-Aware CompCert extensions Committee member for conferences like C++ (CPP), Programming Language Foundations in Software Engineering (PLDI), and Practical Aspects of Declarative Languages (PADL) Current research includes verifying compilers for concurrent systems, designing secure programming languages, and developing compositional verification frameworks. Collaborations with institutions like Yale University and the University of Minnesota under notable advisors Zhong Shao and Gopalan Nadathur have produced 15+ peer-reviewed publications. Their work on CompCertOC demonstrates verified compositional compilation for multi-threaded programs, while their contributions to the Abella system enhance schematic polymorphism and higher-order abstract syntax support. Yuting Wang actively advises a team of 5 current Ph.D. and M.S. students, with alumni placed at organizations like Tencent and AMD. They maintain a public ORCID profile and welcome prospective students interested in formal methods and systems verification.
Rabab K. Ward is a Professor in the Department of Electrical and Computer Engineering at the University of British Columbia, Faculty of Applied Science, Vancouver, Canada. She is a leading figure in signal processing with extensive contributions to biomedical engineering, image reconstruction, and machine learning. She served as President of the IEEE Signal Processing Society (2016–2017) and continues to be actively involved in research and leadership. Her research focuses on signal processing , biomedical signal analysis , compressed sensing , medical image fusion and reconstruction , and deep learning for healthcare applications . She has pioneered methods in EEG and CT image processing, epileptic seizure detection, and 3D human pose estimation. The recent publications highlight her work in 3D face and human modeling , GAN-based forensics , brain-computer interfaces , and medical video analysis . Her research integrates advanced machine learning with practical engineering solutions for clinical and robotic systems. Her scientific awards include being an IEEE Fellow , Distinguished Lecturer of the IEEE Signal Processing Society , and serving as President of the IEEE Signal Processing Society . She has advised numerous students and researchers in areas such as compressed sensing , EEG signal processing , image reconstruction , and deep learning . Her collaborative work includes significant grants and projects in biomedical engineering , neurotechnology , and autonomous systems . Dr. Ward is associated with research labs and teams focused on signal processing for healthcare , medical imaging , and intelligent systems at UBC, fostering interdisciplinary collaboration in engineering and medicine.
Prof. Dr. Paulo Drews-Jr is a Visiting Professor at the Department of Computer Science, Faculty of Engineering, University of Freiburg, Germany. His research focuses on Robotics, Computer Vision, and Deep Learning, particularly for autonomous systems operating in underwater and aerial environments. He holds a D.Sc. and M.Sc. in Computer Science with minors in Robotics and Computer Vision from the Federal University of Minas Gerais, Brazil, and a B.Sc. in Computer Engineering from the Federal University of Rio Grande, Brazil. Education: D.Sc. in Computer Science (Minor: Robotics and Computer Vision), Federal University of Minas Gerais, Brazil M.Sc. in Computer Science (Minor: Robotics and Computer Vision), Federal University of Minas Gerais, Brazil B.Sc. in Computer Engineering, Federal University of Rio Grande, Brazil Research Interests: Paulo Drews-Jr specializes in Robot Perception, Robotics, and Computer Vision. His work addresses challenges in Underwater Robotics, Aerial Robotics, and Industrial Automation, including Active Perception to Account for Uncertainty in Deep Learning Applied to Robotics. His recent publications emphasize Deep Reinforcement Learning, Image Processing, and Trans-Media Navigation for Hybrid Unmanned Vehicles.
Dr. David K. Geller is a Researcher at Utah State University's College of Engineering, affiliated with the Space Dynamics Laboratory. His research program spans spacecraft dynamics, orbital mechanics, and UAV applications, with particular expertise in trajectory optimization and satellite operations. Dr. Geller's research focuses on solving complex problems in space operations through advanced mathematical techniques. His work bridges theoretical developments in orbital mechanics with practical applications for satellite inspection, debris removal, and autonomous spacecraft operations. He has pioneered the application of convex optimization for real-time trajectory planning and developed innovative approaches to spacecraft attitude determination using ground-based photometry. His research demonstrates consistent evolution from fundamental orbital dynamics to increasingly applied mission concepts. Analysis of Dr. Geller's publication record reveals a strategic progression from theoretical foundations to mission-critical applications. His recent work emphasizes autonomy in spacecraft operations, with multiple 2017-2021 papers addressing real-time trajectory planning and multi-spacecraft coordination. The recurring themes across his publications include optimization under constraints, safety in proximity operations, and efficient computational methods for space applications. Primary research areas: Satellite inspection, Spacecraft dynamics, Orbital rendezvous Methodological expertise: Convex optimization, Relative orbital mechanics, UAV navigation Application domains: Space debris removal, On-orbit servicing, Environmental monitoring Dr. Geller maintains active collaborations with researchers including Nicholas Ortolano, Aaron Avery, and previously mentored Austin M. Jensen on UAV-based fish tracking research. His work through the Space Dynamics Laboratory contributes to both fundamental aerospace engineering knowledge and practical space mission capabilities.
Stuart Perry is a Professor at the University of Technology Sydney (UTS), serving as Head of Discipline for Signal Processing and Analytics in the School of Electrical and Data Engineering. He holds affiliations with UTS' Faculty of Engineering and Information Technology, the Global Big Data Technologies Centre, and the Visualization Institute. With over 20 years of experience, his career spans roles at DSTO and Canon Information Systems Research Australia (CiSRA), focusing on image processing, signal processing, and perceptual quality measurement. Perry co-directs the Perceptual Imaging Laboratory (PILab), researching 3D environments, light field technologies, and human perception in immersive realities. He actively contributes to international standards committees like ISO/TC42 and ISO/SC29/WG7, leading JPEG's point cloud coding efforts. His research emphasizes adaptive image processing, machine learning-driven object detection, and medical imaging applications. Perry has authored 60+ publications, two books, and 20 patents. Current projects include VR empathy case studies, point cloud compression, and disaster management digital transformation. Education: PhD in Engineering, University of Sydney (1999) Research Interests: His work bridges computational imaging and human perception, addressing challenges in augmented/virtual reality (AR/VR), 3D scanning, and immersive media. Key areas include light field and point cloud coding, psychophysics of visual perception, and medical diagnostics via machine learning. He explores how perceptual principles can optimize interactive technologies for education, healthcare, and entertainment. Recent Research Trends: Recent articles focus on 3D Gaussian splatting, glaucoma detection via deep learning, and JPEG Pleno standards for plenoptic imaging. His work balances technical innovation (e.g., efficient point cloud compression) with human-centric design (e.g., reducing VR motion sickness through display lag analysis). Awards & Recognition: Member of IEEE and founding SPINet participant. Over 60 refereed publications and 20 patents highlight his industry-academic impact. Grants & Leadership: Leads SmartSat CRC projects on yield estimation and Aus4innovation-funded disaster response tech. Manages grants totaling millions AUD. Editorial roles include Associate Editor of SPIE/IS&T Journal of Electronic Imaging. Labs & Collaborations: PILab collaborates internationally on perceptual imaging standards. Active in ISO committees shaping future media technologies.
