Prof. Jing Zhou is a Professor in the Department of Engineering Sciences at the University of Agder, Norway, and serves as Research Director of the Top Research Centre in Mechatronics and Head of the Robotics and Automation Group. She holds a Ph.D. from Nanyang Technological University (2006) and has extensive industry experience, including roles at the International Research Institute of Stavanger (NORCE) and the Norwegian University of Science and Technology (NTNU). Her research focuses on control theory, robotics, offshore mechatronics, and learning-based systems. She leads major projects such as DEEPCOBOT (2020-2025) and INMOST (Indo-Norwegian collaboration), and has authored three influential books on adaptive control. Her academic roles include IEEE Industrial Electronics Society (IES) AdCom Member, Women-in-IES Chair, and editorial positions in top journals like IEEE Transactions on Automatic Control. She has organized major conferences like IEEE ICIEA 2024 and contributed to over 150 publications. Awards include membership in the Norwegian Academy of Technological Sciences and Agder Academy of Sciences. Prof. Zhou’s research spans adaptive control for nonlinear systems, robotics autonomy, offshore crane control, and drilling technology. Her work integrates AI and reinforcement learning to address challenges in industrial automation and environmental monitoring. She advises numerous PhD students and collaborates globally on projects like ImpactWind and INDURB, advancing sustainable energy and smart industrial systems.
Dr. Sasan Ahdi Rezaeieh is a Research Fellow and Adjunct Research Fellow in the School of Electrical Engineering and Computer Science at The University of Queensland. His work focuses on biomedical engineering, antenna design for medical diagnostics, and microwave imaging systems for detecting conditions like hepatic steatosis and pulmonary edema. He holds a Ph.D. in Engineering from UQ (2016). Position: Research Fellow & Adjunct Research Fellow Affiliation: School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology Research interests include developing antennas and electromagnetic systems for non-invasive medical diagnostics, such as liver health monitoring and torso imaging. Recent work involves HepNet (a deep learning model for liver disease detection) and portable electromagnetic devices using microwave signals. His publications span antennas for medical imaging, microwave-based diagnostic systems, and biomedical applications. Publications highlight advancements in antenna design (e.g., tapered lens antennas, metasurface antennas) and their integration with medical diagnostics. Key contributions include a patent for an apparatus to characterize internal body features using electromagnetic methods and a thesis on wideband microwave imaging for fluid accumulation detection.
Laszlo A. Jeni is an Assistant Research Professor at Carnegie Mellon University's Robotics Institute, leading the Computational Behavior (CUBE) Lab. His research focuses on computer vision, digital humans, and computational behavior science, with applications in healthcare, affective computing, and assistive technologies. He develops methods to model human behavior using multi-modal sensors, including facial, body, and physiological data. Current research emphasizes human motion synthesis, clinical movement analysis, and 3D scene reconstruction. Key research topics include action recognition for clinical applications, generative models for 4D scene synthesis, and video-based physiological estimation. Jeni supervises a team of PhD and master's students in the CUBE Lab, advancing interdisciplinary projects at the intersection of AI and behavioral science. His work has led to innovations in non-contact health monitoring and virtual avatar control systems. Notable contributions include frameworks for sim-to-real transfer in human mesh recovery, diffusion-based camera alignment, and video transformers optimized for efficiency. Jeni's lab actively participates in challenges like the V4V (Vision for Vitals) initiative and benchmarks for 3D facial alignment, maintaining a strong presence in both academic and applied computer vision communities.
Dr. Helen Branthwaite is a Senior Lecturer at Staffordshire University, leading the postgraduate Clinical Biomechanics program. Her clinical work includes MSK podiatry in private practice. She holds a PhD (2015) from Staffordshire University, an MSc in Sports Injury (2001), and a BSc in Podiatric Medicine (1996). Her research focuses on footwear biomechanics, balance in aging populations, and musculoskeletal health. She advises national bodies like the Royal College of Podiatry and collaborates internationally with organizations like Mizuno and Cambrion. Her editorial roles span journals such as Journal of Foot and Ankle Research and Footwear Science . Education: PhD: 'The Impact of Footwear Choice on Foot Biomechanics' (2015) MSc Sports Injury (2001) BSc(Hons) Podiatric Medicine (1996) Her research interests include clinical biomechanics applications, footwear design effects on foot function, and interdisciplinary approaches to musculoskeletal dysfunction. Recent studies explore balance improvements via footwear innovations and interventions for aging populations. She has pioneered a clinical biomechanics alumni network and co-organized international conferences. Awards include Fellow of the Higher Education Academy. Teaching focuses on postgraduate modules in critical appraisal, gait analysis, and sports biomechanics. Collaborations with industry partners drive therapeutic footwear design advancements. Grants include research on footwear sizing for women and product development with Mizuno. Labs/Teams: Clinical Biomechanics alumni network, international footwear research collaborations Grants: Mizuno (balance management in aging adults), Cambrion (women's footwear sizing)
Kris Kitani is an Associate Research Professor at the Robotics Institute and Courtesy Professor in Electrical and Computer Engineering at Carnegie Mellon University (CMU). He also holds roles as a Research Scientist at Meta FAIR and Co-Director of CMU's Extended Reality Technology Center (XRTC). His research focuses on computer vision, human activity forecasting, first-person vision, inverse reinforcement learning, and assistive technologies for visually impaired individuals. Kitani's education includes a BS from the University of Southern California, and MS and PhD from the University of Tokyo. His work bridges perception, decision-making, and interaction in autonomous systems, with applications in wearable cameras, assistive devices, and human-robot collaboration. He has advised numerous PhD and master's students, including current researchers like Jinkun Cao and Erica Weng. Key awards include the Marr Prize honorable mention (ICCV 2017), Best Paper Honorable Mentions at CHI 2017/2020, and Best Paper Awards at W4A 2017/2019. His lab, the Cognitive Assistance Laboratory, develops systems for robust real-world perception and interactive decision-making. Kitani teaches courses such as Graduate Computer Vision (16-720) and Undergraduate Computer Vision (16-385) at CMU. His research spans robotics, AI, and human-centered computing, with a focus on first-person vision and assistive technologies.
Holden H Wu is a Professor in the Department of Radiological Sciences at the University of California Los Angeles (UCLA) School of Medicine. His research focuses on advanced medical imaging techniques, particularly in quantitative MRI, artificial intelligence applications, and image-guided interventions. He leads the UCLA MRRL Wu Lab and has established himself as a leading researcher in free-breathing MRI techniques for body composition analysis and disease quantification. Dr. Wu's research interests span nanotheranostics, quantitative imaging, MRI technology development, artificial intelligence applications in medical imaging, and image-guided interventions. His work particularly emphasizes developing motion-robust techniques for abdominal and pediatric imaging, with applications in liver fat quantification, body composition analysis, and prostate cancer imaging. He has pioneered several free-breathing MRI techniques that have eliminated the need for breath-holding in patients, significantly improving clinical applicability, especially for pediatric populations and patients with limited breath-holding capacity. His recent publications demonstrate a strong focus on integrating deep learning with physics-based modeling to improve quantitative MRI techniques. Major trends in his research include the development of self-gated radial MRI techniques, motion-compensated imaging methods, and AI-powered analysis tools for medical imaging data. His work bridges engineering innovation with clinical applications, particularly in liver disease, metabolic disorders, and oncology. New Technologies for Real-Time MRI-Guided Robotic-Assisted Abdominal Interventions (NIH R01EB031934, 2022-2026) - Principal Investigator Quantitative MRI and Deep Learning Technologies for Classification of NAFLD (NIH U01EB031894, 2022-2027) - Principal Investigator Quantifying Body Composition and Liver Disease in Children using Free-Breathing MRI and MRE (NIH R01DK124417, 2020-2024) - Principal Investigator Integrating Quantitative MRI and Artificial Intelligence to Improve Prostate Cancer Classification (NIH R01CA248506, 2020-2025) - Co-Principal Investigator Dr. Wu has mentored numerous students and researchers through his active laboratory, focusing on training the next generation of biomedical imaging scientists. His research group, the UCLA MRRL Wu Lab, develops innovative imaging technologies with direct clinical translation potential. Current projects include developing real-time MRI-guided robotic interventions, advanced quantitative techniques for liver fat and fibrosis assessment, and AI-powered prostate cancer detection methods.
Lt Col Robert A. Bettinger, Ph.D., is an Associate Professor of Aerospace Engineering at the Air Force Institute of Technology (AFIT), part of the Graduate School of Engineering & Management. His research focuses on re-entry dynamics, spacecraft design, optimization, and survivability in cislunar environments. He has authored over 50 publications and received numerous awards, including the 2022 MOAA Outstanding Military Professor Award and multiple Air Force Science and Engineering awards. His work spans orbital mechanics, debris risk analysis, and policy implications of space operations. Bettinger holds a Ph.D. in Astronautical Engineering from AFIT and has extensive professional military education. He advises on space law challenges and collaborates on projects like Cislunar debris propagation and space domain awareness architectures. Education: Ph.D. (2014), M.S. (2011), and B.S. (2007) in Astronautical Engineering from AFIT and the U.S. Air Force Academy. He also earned an M.A. in European History (2010) and a Graduate Certificate in Space Systems (2009). Research interests emphasize spacecraft survivability in high-risk orbital environments, cislunar dynamics, and the intersection of technical innovation with geopolitical strategy. His recent work includes studies on mega-constellation debris risks, Lagrange point surveillance via periodic orbits, and space cybersecurity in the era of large satellite networks. Awards highlight his contributions to advanced technology development and mid-career scientific excellence. He is a member of Tau Beta Pi and Sigma Gamma Tau honor societies. His advisory work includes MS thesis supervision and participation in collaborative research groups like the CSRA Orbital Warfare Research Team.
Weizhen Mao is a Professor in the Department of Computer Science at the College of William and Mary. His research focuses on algorithms, theoretical computer science, parallel computing, distributed systems, and networking. He holds an office in McGlothlin-Street Hall 114 and can be reached at wm@cs.wm.edu. Teaching Responsibilities: Professor Mao instructs a range of courses including Discrete Structures (CSci 243), Algorithms (CSci 303), Theory of Computation (CSci 663), and advanced topics in algorithms and computation at both undergraduate and graduate levels. His courses emphasize foundational concepts in computer science theory and practical applications. Research Interests: His work spans algorithm design, scheduling theory, distributed computing, and network protocols. Recent publications address challenges in sensor networks, RFID systems, vehicular communication, and parallel processing. His research often bridges theoretical guarantees with practical implementations in real-world systems. Professional Contributions: With over 50 publications in top conferences and journals (e.g., IEEE Transactions on Wireless Communications, ACM MobiHoc), his work has impacted areas like energy-efficient networking, optimal storage placement in sensor networks, and GPU cluster optimization. He maintains active collaborations with industry and academic partners to advance computational methodologies.
Dr. John Darby is a Senior Lecturer in the Department of Computing, Mathematics and Digital Technology at Manchester Metropolitan University (MMU). He serves as the programme leader for the MSc Computer Science and teaches AI and machine learning modules. His research focuses on non-invasive Computer Vision techniques for analyzing human movements, spanning from skeletal muscle analysis to behavior classification and human-object interactions. He holds a PhD from MMU and has industry experience as a Software Engineer at Echostar Europe. Education: BSc Hons. in Computational Physics (University of Edinburgh, 2000–2003), MSc in Mobile and Distributed Computer Networks (Leeds Metropolitan University, 2003–2004), and a PhD from MMU (2006–2010). Postdoctoral roles included Research Assistant at IRM (2010–2011) and Research Associate at SCMDT (2011–2013). Research Interests: His work integrates Machine Learning, Image Processing, and Particle Filtering to address challenges in surveillance, biomechanics, and healthcare. Key applications include markerless motion tracking, implement recognition, and clinical decision support systems. He collaborates with the Image and Sensory Computation Group on interdisciplinary projects. Advising & Grants: While specific student names or grant details are not listed, his role as a Senior Lecturer and programme leader implies involvement in supervising postgraduate students and securing research funding. His projects often involve clinician-led approaches and industry partnerships. Labs/Teams: Active contributor to the Image and Sensory Computation Group, focusing on human movement analysis and healthcare technology applications.
Faezeh Pasandideh is a Lecturer for Special Tasks in Electrical Engineering at the Hamm-Lippstadt University of Applied Sciences. Her research focuses on UAV networks, software-defined networks, machine learning, autonomous vehicles, IoT, and 5G/6G networks. She is also involved in projects like the B5GCyberTestV2X initiative, addressing cybersecurity testing for V2X systems. Contact: faezeh.pasandideh@hshl.de . Her work emphasizes energy-efficient UAV base station placement, complex arithmetic reasoning for UAVs, and topology management using particle swarm optimization. Recent publications highlight advancements in multi-UAV communication systems and flying ad hoc networks, with contributions to sustainable smart farming via IoT and LPWAN technologies. Key research areas include network optimization, wireless protocols, and healthcare applications of wireless sensor networks. She has explored fuzzy logic for congestion control in medical and underwater sensor networks, demonstrating interdisciplinary expertise. Current projects include the publicly funded B5GCyberTestV2X project, where she leads cybersecurity testing for vehicle-to-everything (V2X) systems. No awards are explicitly listed, but her extensive publication record reflects active research engagement.
Helin Ulas is a Lecturer and Research Associate in the Digital Arts Department at Karlsruhe University of Arts and Design. Her work explores socio-political changes, ecological perspectives, and digital cultures through networked installations, performances, and AI-driven art. She also guest lectures at NYU's IMA Low-Res program and the NODE Institute, and co-leads the Collective Dreams initiative focusing on community-centered practices. Current affiliations include roles in Kanapé Kollektiv and collaborations with institutions like ZKM. Research emphasizes speculative machines, collective imagination, and the interplay between technology and human expression. Key projects like ToM (2024) reimagine mythological narratives through AI and interactive installations. Her work frequently addresses ethical dimensions of AI, ecological futures, and human-machine symbiosis. Recent exhibitions include installations at ZKM, Basel Naouri, and the School of Machines. Teaching and mentorship focus on fostering experimental practices in digital art, with an emphasis on interdisciplinary collaboration. Ongoing projects investigate generative design, performative spaces, and the socio-cultural impacts of emerging technologies. Professional networks span academia, independent collectives, and institutional partnerships in Europe and North America.
David Canales Garcia is an Assistant Professor in the Department of Aerospace Engineering at Embry-Riddle Aeronautical University (ERAU), located in Daytona Beach, Florida. He serves as ERAU's representative to the Universities Space Research Association (USRA) and is actively involved in the AIAA Astrodynamics and Space Systems Technical Committees. His academic journey includes B.Sc. and M.Sc. degrees in Aerospace Engineering from the Polytechnic University of Catalonia (Spain), a second M.Sc. in Astrophysics, Particle Physics, and Cosmology from the University of Barcelona, and a Ph.D. in Astrodynamics and Space Applications from Purdue University under Dr. Kathleen C. Howell. Dr. Canales' research expertise spans astrodynamics in multi-body systems, Cislunar surveillance, orbit determination, and space applications such as telecommunications and mission planning. His work integrates advanced mathematical methods with practical challenges in space exploration, including trajectory optimization, lunar surface utilization, and space debris modeling. He teaches courses on spacecraft attitude dynamics, orbital mechanics, and computational astrodynamics. Recent research outputs highlight innovations in low-energy trajectory design for Near-Earth Objects, LiDAR-based precision landing systems, and open-source tools for Cislunar debris propagation. His interdisciplinary approach bridges astrodynamics with emerging technologies like neural networks and augmented reality for trajectory visualization. Education: Ph.D., Aeronautics and Astronautics, Purdue University (USA) M.Sc., Aeronautical Engineering, Universitat Politècnica de Catalunya (Spain) M.Sc., Astrophysics, Particle Physics and Cosmology, Universitat de Barcelona (Spain) B.Sc., Aeronautical Engineering, Universitat Politècnica de Catalunya (Spain) Professional Affiliations: Member, AIAA Astrodynamics Technical Committee Member, AIAA Space Systems Technical Committee His courses emphasize cutting-edge topics such as computational astrodynamics and orbital mechanics, reflecting his commitment to advancing space engineering education and research.
Dr. Gustavo Vejarano is an Associate Professor of Electrical and Computer Engineering at Loyola Marymount University's Frank R. Seaver College of Science and Engineering. His teaching and research focus on the intersection of mathematics and physical systems, particularly in cyber-physical systems, robotics, and wireless networks. He emphasizes hands-on, collaborative learning to help students design practical technological solutions. Education: B.S. in Electrical Engineering, Universidad del Valle, Colombia (2005) M.S. and Ph.D. in Electrical and Computer Engineering, University of Florida (2009, 2011) Research Interests: Dr. Vejarano leads the Intelligent and Embedded Networks and Systems Laboratory (Intemnets Lab) , where research explores autonomous systems like drones for wildfire monitoring, computer vision, and wireless communication networks. His work integrates mathematical modeling to optimize system performance in fields like robotics, surveillance, and medical devices. Lab & Projects: The Intemnets Lab develops solutions for intelligent networked systems, including drone-based surveillance using thermal imaging and computer vision. Collaborative student projects focus on fault-tolerant systems, target localization, and wireless body area networks for healthcare applications. Teaching Philosophy: Dr. Vejarano prioritizes student engagement through real-world problem-solving. He advises future engineers to leverage LMU’s resources for impactful innovation and emphasizes the societal applications of their work.
Adam Kennedy serves as a Cyberinfrastructure Architect within the Department of Forest Ecosystems & Society at Oregon State University's College of Forestry. His role focuses on developing robust data systems to support environmental research, particularly in forest hydrology and ecosystem monitoring. His educational foundation includes: B.S. in Environmental Sciences and Resources (2004) from Portland State University with a Biology minor M.S. in Environmental Sciences and Resources (2006) from Portland State University with a Hydrology certificate Completed PhD coursework and comprehensive exams in Environmental Sciences (2012) alongside an Ecosystem Informatics Certificate through the IGERT program Dr. Kennedy's research integrates data engineering with ecological science, specializing in real-time environmental monitoring systems. His primary interests encompass Data Management, Ecosystem Ecology, Long-range Wireless Communication, Quality Control Algorithms for Streaming Data, and Sensor Network Best Practices. This interdisciplinary approach enables advanced studies of forest canopy processes and hydrological dynamics through innovative technological solutions. Analysis of his publication trajectory reveals consistent focus on canopy hydrology and sensor technology applications. His recent work (2019-2022) demonstrates sophisticated modeling of dewfall dynamics using machine learning, while earlier research pioneered distributed temperature sensing for stream ecosystems and explored climate-streamflow relationships through teleconnection indices. The body of work shows progressive technical refinement in environmental data acquisition and analysis. No scientific awards are documented in the available sources, though his contributions to environmental data infrastructure are substantiated through technical publications and system development. Information regarding graduate student advising or research grants is not publicly accessible in the provided materials. Dr. Kennedy contributes to the Ecosystem Informatics initiative through his development of real-time data systems, as evidenced by his work on provisional data graphs and sensor network infrastructure. His technical expertise supports long-term ecological research at facilities like the HJ Andrews Experimental Forest, enabling high-resolution environmental monitoring across complex forest landscapes.
Dr. Zhidong Xiao serves as Principal Academic (Associate Professor) at Bournemouth University's National Centre for Computer Animation within the Faculty of Media and Communication. With over ten years of leadership experience including roles as Programme Leader, Head of Education, and Deputy Head of Department, he drives academic strategy and research innovation in computer animation and digital media. His work bridges technical excellence with creative industry applications through extensive collaborations across the UK and China. Dr. Xiao's educational foundation includes a PhD in Computer Graphics (2010) and postgraduate certificates in Education Practice (2010) and Research Degree Supervision (2011) from Bournemouth University, complemented by a BEng (Hons) in Thermodynamics from Taiyuan University of Technology, China (1994). PhD in Computer Graphics, Bournemouth University (2010) PGCE in Education Practice, Bournemouth University (2010) PGCE in Research Degree Supervision, Bournemouth University (2011) BEng (Hons) in Thermodynamics, Taiyuan University of Technology (1994) His research spans Computer Graphics, Motion Capture, Artificial Intelligence, and Virtual Reality with focus on physics-based simulation, sign language recognition, and motion synthesis. Recent work integrates partial differential equations with machine learning to solve animation challenges in facial realism, deformation simulation, and 3D reconstruction. His interdisciplinary approach connects computer science with creative industries, healthcare applications, and educational technology while advancing core techniques in neural rendering and motion analysis. Analysis of his 15 most recent publications reveals consistent innovation in physics-based animation techniques (40%), motion capture processing (25%), and neural approaches to 3D reconstruction (35%). Key trends include the fusion of analytical physics models with deep learning architectures, development of efficient real-time simulation methods, and expansion into accessibility applications through sign language recognition systems. Scientific recognitions include: Fellow of British Computer Society (2023) Fellow of Higher Education Academy (2011) Best Poster Award at Pacific Graphics 2014 He maintains active peer review roles for EPSRC, ESRC, IEEE Transactions on Multimedia, and ACM SIGGRAPH conferences. Dr. Xiao has supervised seven PhD students to completion while currently guiding Alexandra Sergeeva Alexdottir's research on Phantom Touch phenomena. His grant portfolio demonstrates strong industry-academia collaboration: Principal Investigator Capturing and representing sign language (British Council, 2025) VE Communication Programme (Erasmus+, 2020) Co-Investigator Rehabilitation Enhancement via Motion Capture (BU Fusion Fund, 2013) Cross-Channel Film Lab (Interreg, 2012) Digital Beijing Opera Project (2010) As a core member of Bournemouth's Computer Graphics and Visualisation Research Group and Centre for Digital Entertainment, he leads initiatives in motion capture technology through AccessMocap Studio. His international outreach includes invited lectures across China on computer animation education and visual effects techniques, strengthening global partnerships in creative technology development.
