Margaret Byron is an Associate Professor in the Department of Mechanical Engineering at Pennsylvania State University. Her research focuses on the transport of organisms and objects in environmental flows, particularly turbulence, with two main thrusts: particle settling/sedimentation and millimeter-scale animal locomotion. Research Interests: Hydrodynamic interactions in turbulent flows Microplastic transport influenced by biofilms Metachronal coordination in biological systems Computational fluid dynamics of low-Reynolds-number swimmers Shape-dependent particle dynamics in isotropic turbulence Scientific Awards: NSF CAREER Award (2022) Publications Trends: Her work spans interdisciplinary applications in environmental flows, marine science, and bio-inspired robotics, emphasizing metachronal propulsion, microplastic dynamics, and vortex-fluid interactions across scales.
Dr. Ian van der Linde is a Reader (equivalent to Associate Professor) at Anglia Ruskin University, affiliated with the Faculty of Science and Engineering and the School of Computing and Information Science. He conducts cross-faculty research at the intersection of computer science, vision science, image processing, algorithms, and cognitive psychology, with a particular focus on the human visual system. His research expertise includes: Vision Science and Eye Research Image Processing and Computer Vision Cognitive and Behavioural Psychology Computational Methods in Neuropsychological Testing Dr. van der Linde's research primarily investigates gaze control, visual short-term memory, face perception, visual task planning, image quality assessment, multimodal binding, the impact of ageing on visual performance, and the computations performed in the early visual system. His work combines computer modeling with experimental psychology to understand human vision, with applications in healthcare, computer vision systems, and cognitive assessment tools. His recent publications demonstrate a consistent focus on visual short-term memory, particularly examining how humans bind visual features together, how this process changes with age, and how it relates to cognitive decline. His work also extends to facial recognition, image quality assessment, and the application of computational methods to neuropsychological testing. Scientific recognition includes: Fellow of the British Computer Society (FBCS) Fellow of the Higher Education Academy (FHEA) Chartered Engineer of the Engineering Council UK (CEng) The Wellcome Trust, 'I'm a Scientist, Get Me Out of Here', Winner, Nickel Zone, £500 Dr. van der Linde has supervised numerous PhD students across Computing, Psychology, and Vision Sciences, with research spanning audio signal processing, online security behaviors, cardiovascular bioinformatics, cloud computing adoption, and visual working memory. He has secured research funding from various sources including the Wellcome Trust, Home Office Scientific Development Branch, and Knowledge Transfer Partnerships. He is a member of the Computing, Informatics and Applications Research Group and teaches courses in Image Processing, Data Structures & Algorithms, and Core Mathematics for Computing.
Dr. Emma Stewart is a Lecturer in Psychology at Queen Mary University of London, within the School of Biological and Behavioural Sciences. She leads research in the Centre for Brain and Behaviour, focusing on visual perception and eye movement control. Her educational background includes undergraduate degrees in Psychology, Law, and French, followed by a PhD in Psychology from The University of Adelaide in Australia under Prof Anna Ma-Wyatt. She completed postdoctoral research at the University of Marburg with Prof Alexander Schutz and later led an independent research program at JLU Giessen funded by the German Research Council (DFG). Dr. Stewart's research investigates how humans perceive the visual world and make eye movements to guide decisions and actions. Her work combines behavioral and psychophysical methods with eye-tracking, computational modeling, reaching tasks, computer graphics, and statistical modeling. Her primary research areas include: Oculomotor planning and gaze control Interactions between peripheral and central vision Perceptual inference from object properties Development of visual stability across saccades Mental rotation and 3D object perception How past visual experience shapes perception and action Analysis of Dr. Stewart's recent publications (2019-2024) reveals a consistent focus on transsaccadic perception, object viewpoint processing, and the integration of visual information across eye movements. Her work spans multiple disciplines including vision science, cognitive psychology, and computational neuroscience, with particular emphasis on understanding how humans maintain visual stability despite making 2-3 eye movements per second. Recent work has explored how object properties influence eye movement planning and how humans mentally rotate objects using 2D optical flow processing rather than true 3D rotation. Dr. Stewart was awarded a grant from the German Research Council (DFG) in 2021 to lead her own research program at JLU Giessen, where she investigated how inferences from physical object properties influence eye movements. Her research has been supported by various grants, including DFG funding that enabled her to establish and lead an independent research program in Germany prior to joining Queen Mary University of London in December 2023. She collaborates extensively with researchers across institutions, particularly with Prof Roland Fleming at JLU Giessen and Prof Alexander Schutz at the University of Marburg. Dr. Stewart leads the Stewart Lab at Queen Mary University, which focuses on transsaccadic perception, 3D object perception, saccade sampling and information uptake, past history and prediction in visual processing, and inferences from objects. Her lab employs behavioral and psychophysical techniques coupled with eye-tracking and computational modeling to investigate fundamental questions about visual perception and eye movement control.
Rick Butler is a Postdoctoral Researcher in the ICT Lab at Eindhoven University of Technology (TU/e), focusing on optical fibre sensing for early earthquake warnings. He holds an M.Sc. in Electrical Engineering from TU/e (2020) and completed his Ph.D. at Delft University of Technology, researching computer vision for surgical workflow detection. His expertise bridges polarisation mode dispersion compensation and deep learning applications in biomedical contexts. Education: M.Sc. Electrical Engineering, TU/e (2020) His research emphasizes optical fibre sensing , deep learning , and computer vision , particularly in medical settings like cardiac catheterization labs. Recent work involves 2D pose tracking, workflow analysis, and robust object detection frameworks. Collaborations span institutions such as TU Delft and involve interdisciplinary teams in biomedical engineering and machine learning. He contributes to the UN Sustainable Development Goal of Quality Education through his academic mentorship and research innovation. His publication metrics include 92 citations (Scopus) and collaborations across Europe.
Mazyar Seraj serves as an Assistant Professor in the Software Engineering and Technology department within the Mathematics and Computer Science school at Eindhoven University of Technology (TU/e). His academic foundation includes a Doctorate in Software, Algorithms, and Control Systems from the University of Bremen (2020), a Master's in User Interfaces and Multimedia from Multimedia University (2012), and a Bachelor's in Software, Algorithms, and Control Systems from Islamic Azad University (2009). His research centers on Educational Technology and Human-Computer Interaction , with specific focus areas including block-based programming for young learners, mobile learning applications for logic circuits, computational notebook visualization, and domain knowledge integration in software development. His work consistently bridges theoretical computer science with practical educational implementations, particularly examining gender dynamics and accessibility in programming education. Analysis of his 14 recent publications reveals a strong emphasis on contextual learning environments (smart homes, living labs), gender-inclusive programming tools , and mobile-first educational interfaces . His research methodology frequently combines prototype development with empirical validation through user studies involving students and educators. Seraj actively contributes to academic instruction through courses including Programming, Programming and Modelling, DBL App Development, and CBL Autonomous Systems. His research has been featured in media coverage regarding AI applications in classroom settings, specifically addressing queue efficiency through improved question formulation.
Dr. Fawaz Y Annaz serves as Reader in Mechatronics at Birmingham City University, leveraging an international research career spanning the UK, Singapore, Japan, Malaysia, and Brunei. His expertise encompasses mechatronics engineering, robotics, and smart actuation systems, with significant contributions to aerospace, energy, and biomedical applications through leadership of the High Integrity System Laboratory and Mechatronics Group. His academic foundation includes: BSc in Electrical and Electronic Engineering (1987, University of East London) focusing on Electromagnetic Vehicle Levitation Control MSc in Engineering of Dynamic Systems (1989) researching Helical and Planar Induction Actuators PhD in Avionics (1996) addressing Architectures Consolidation in High Integrity Multi-Lane Smart Electric Actuators Dr. Annaz's research integrates robotics, control theory, and energy systems with notable emphasis on unmanned aerial vehicles (including swarm systems), microgrid resilience engineering, and electromechanical actuation. His multidisciplinary approach bridges theoretical control systems with practical applications in aerospace actuation, sustainable energy management, and speech production analysis, demonstrating consistent innovation across diverse engineering domains. Analysis of his publication trajectory reveals evolving research priorities: early work (2000-2015) centered on UAV navigation, actuator monitoring, and speech phoneme signatures, while recent publications (2020-2025) show strategic pivot toward AI-driven microgrid resilience, battery life optimization, and advanced UAV control systems—reflecting growing industry demand for sustainable energy solutions and autonomous systems. Scientific recognition includes: Granted European Patent for electromechanical actuator technology Two international patent applications Over 50 peer-reviewed publications Book and book chapters in mechatronics As an educator and research leader, Dr. Annaz has supervised numerous MSc and PhD students while directing international projects including Japanese Ministry of Education collaborations. His research output includes commercialization efforts for patented actuator technologies and development of educational platforms for robotics training. Current focus areas encompass deep reinforcement learning for energy management and swarm UAV systems. His laboratory initiatives include the High Integrity System Laboratory (operating in Japan and Malaysia) specializing in vibration control, active magnetic bearing systems, and artificial heart development, alongside the Malaysia-based Mechatronics Group advancing industrial robotics and unmanned vehicle technologies.
Fabio Morbidi is an Associate Professor (Maître de Conférences HDR) at the Université de Picardie Jules Verne (UPJV), France, where he heads the Robotic Perception group at the MIS laboratory since 2022. His office is located at 33 Rue Saint-Leu, Amiens. He holds a Ph.D. in Robotics and Automation from the University of Siena (2009) and conducted postdoctoral research at Northwestern University, University of Texas at Arlington, and Inria Grenoble. His research focuses on multi-robot systems, event-based vision, autonomous vehicles, and assistive robotics. Research interests span robotic vision, distributed control, and sensor fusion for applications in exploration, wheelchair assistance, and drone technology. His work emphasizes real-world implementations, such as the SpheriCol driving assistant for power wheelchairs and event-based perception for autonomous vehicles. Awards include ICRA'25 Best Paper Award Finalist and contributing to an IEEE RAM issue that received the APEX 2024 Awards of Excellence. He actively advises PhD students and coordinates ANR projects (e.g., CERBERE, DEVIN) focused on robotic perception. He serves as Associate Editor for IEEE Transactions on Robotics and IEEE Robotics and Automation Letters.
Dr. Apan Dastider is a tenure-track Assistant Professor in the Division of Engineering Technology at Wayne State University’s James and Patricia Anderson College of Engineering. He leads research in Robotics and AI, focusing on adaptive robotic systems, human-robot interaction, and generative AI for intelligent control. Previously, he earned his Ph.D. and M.S. in Electrical Engineering from the University of Central Florida, where he worked as a Graduate Research Assistant in the Autonomous Robotic Computing Lab. Education: Ph.D. in Electrical Engineering, University of Central Florida, 2025 M.S. in Electrical Engineering, University of Central Florida, 2023 B.S. in Electrical and Electronic Engineering, Bangladesh University of Engineering and Technology, 2017 Research Interests: Dr. Dastider’s research lies at the intersection of robotics, AI, and control theory. His work emphasizes: Knowledge distillation across heterogeneous domains Human-robot interaction and motion synthesis Generative AI for adaptive robotic control Non-parametric reinforcement learning Low-dimensional manifold learning for motion planning Publications Overview: His recent publications demonstrate a strong focus on real-time robotic planning, diffusion models for motion generation, and safe human-robot collaboration. Notable works include APEX (dual-arm manipulation), RETRO (reactive trajectory optimization), and unified frameworks for interception and obstacle avoidance using diffusion variational autoencoders. Awards & Recognition: UCF ECE Outstanding Graduate Researcher Award Reviewer for IEEE ICRA and IROS conferences Teaching & Mentorship: Dr. Dastider is committed to developing the next generation of AI and robotics professionals. He teaches courses like MIT5700 - Industrial Robots Modeling and Simulation at Wayne State University and actively mentors students in cutting-edge research projects.
Anna Tcherkassof is a University Professor at the University of Grenoble Alpes, affiliated with the Laboratoire Inter-universitaire de Psychologie (LIP/PC2S). Her research focuses on nonverbal communication, emotions, and emotional facial expressions within a socio-cognitive framework. She leads the DynEmo project, developing a unique database of spontaneous and dynamic facial expressions that has positioned her laboratory as one of the few in France studying facial expressions of emotions from a spontaneous and dynamic perspective. Her research interests center on the process of recognizing facial expressions and the nature of facial information used to decode emotional messages. She approaches facial expressions as a privileged access route for studying non-verbal communication of emotions, with particular emphasis on spontaneous and dynamic expressions occurring during real social interactions, especially intercultural ones. Her work integrates eye tracking analysis and joint analysis of eye tracking and EEG signals to complement standard subjective judgment methodologies. Dr. Tcherkassof has developed a theoretical model based on embodied cognition that challenges traditional psychological conceptions of emotions and their expressions. Her research takes a constructivist epistemological framework, offering new perspectives on behavioral and sociocognitive processes. Her recent work has examined the effects of mask-wearing on emotional communication, particularly in early childhood settings, which has garnered significant media attention during the pandemic. Her publications demonstrate consistent contributions to understanding emotion-facial expression links, with recent work exploring cross-cultural aspects, neural correlates of emotion processing, and methodological innovations in emotion recognition research. Her interdisciplinary approach bridges psychology, neuroscience, and computer science through projects like BrainGazeDynemo and Oudjat. Dr. Tcherkassof actively engages with the public through media interviews and public conferences, including her 2018 presentation at the 'Avenue centrale' public lecture series titled 'What do we really know about facial expression of emotions?' She leads several significant research projects including BrainGazeDynemo (studying visual processing of emotional facial expressions), Cosmethics (examining cosmetics at the intersection of health and beauty), and the DynEmo corpus creation project funded by the French National Research Agency. Her collaborative work extends across multiple institutions including Gipsa-lab, LPNC, DCM, CERAG, CHUGA, ESRF, GRESEC, and SYMMES. Her laboratory, LIP/PC2S, represents a major asset in French psychology research as few laboratories in France focus on emotions as their primary object of study, and even fewer examine facial expressions from the perspective of their spontaneous and dynamic aspects in natural contexts.
Diana Borza is a lecturer in computer science, active since 2014, with a focus on computer vision , facial feature analysis , and perception systems . Her research applies to soft biometrics , human behavior understanding , and visual surveillance . She teaches courses including Computer Networks , Computer Vision and Deep Learning , and Object-Oriented Programming . Projects: MULTIFACE (PN-II-RU-TE-2014-4-1746, 2015-2017) DEEPSENSE (PN-III-P1-1.1-TE-2016-0440, 2018-2020) Research interests span computer vision, deep learning, and their interdisciplinary applications. Her work explores: Facial feature analysis for biometric recognition Human behavior modeling through video surveillance Efficient model ensembling and knowledge distillation Multimodal sentiment analysis and online learning dynamics Recent publications emphasize temporal action detection, attention mechanisms, and adaptive networks. Key themes include: Dynamic facial/body tracking Pedestrian attribute recognition Micro-expression detection Periocular biometrics Energy-efficient AI Scientific contributions : Received Romanian Ministry of Education grants for MULTIFACE and DEEPSENSE projects Developed models for real-time micro-expression detection and hair segmentation Published extensively on soft biometrics and human behavior analysis
Christian Gagné is a Full Professor in the Department of Electrical Engineering and Computer Engineering at Laval University's Faculty of Science and Engineering. His research spans machine learning, artificial intelligence, and cybersecurity, with applications in healthcare and autonomous systems. Research Units: VITAM - Sustainable Health Research Center, CRDM (Center for Research in Massive Data), CeRVIM (Research Center in Robotics, Vision and Machine Intelligence), IID (Intelligence and Data Institute) Grants: Multiple MITACS-funded projects (2024-2026) on AI architecture, medical imaging, and autonomous driving His recent work focuses on adversarial robustness, data augmentation, and suicide risk prediction via health data analysis. He has supervised numerous Master's and PhD students in electrical engineering, computer science, and biophotonics. Scientific Awards: Recipient of the Teaching Star award from Laval University's Faculty of Science and Engineering (2008-2018)
Jeffrey Stolet is Professor of Music Technology and Director of Future Music Oregon at the University of Oregon's School of Music and Dance. With a PhD in Music from the University of Texas at Austin (1984), he is among the very first individuals appointed to a Philip H. Knight professorship at the University of Oregon. His research focuses on Music Technology, Data-driven Instruments, and Electroacoustic Music. Stolet has pioneered interactive performance environments using wands, sensing devices, game controllers, and other specialized interfaces to control sonic and videographic domains. His work spans computer music programming, real-time performance systems, and multimedia composition. Stolet's compositions have been presented globally at major electroacoustic and new media festivals including the International Computer Music Conference, Society for Electro-Acoustic Music in the United States Conference, and MusicAcoustica Festival in Beijing. His work has also been featured at prestigious venues such as the Museum of Modern Art in New York and the Pompidou Center in Paris. Inducted into China's prestigious DeTao Masters Academy Honorary professorships at two Chinese music conservatories Lifetime achievement award from Musicacoustica for contributions to interactive music Author of the first book about Kyma programming language Developer of Electronic Music Interactive , an award-winning multimedia educational resource Stolet's recent work explores themes of balance, social justice, cultural identity, and the intersection of physical limitations with technological expression, as evidenced in compositions like In Desperate Times (2020) which responded to the George Floyd murder and disproportionate Latinx deaths during the pandemic.
Kevin Darby is an Assistant Professor in the Department of Psychology at Florida Atlantic University's Charles E. Schmidt College of Science. His research focuses on cognitive development, memory binding, interference effects, and attention allocation across the lifespan. He leads the Lifespan Cognition Lab, which emphasizes computational modeling and transparency in cognitive aging research. Ph.D. from The Ohio State University (2017) M.S. from The Ohio State University (2014) B.S. from University of Houston (2010) His research explores how memory binding mechanisms evolve from childhood to old age, using eye-tracking and computational models. Recent work examines temporal delay effects, confidence judgment dynamics, and cross-species cognitive flexibility comparisons. He advocates for replicable methodologies in cognitive aging studies. Key article trends from 2013–2025 reveal expertise in memory interference, developmental attention allocation, computational modeling, and lifespan cognitive changes. Subfields span object-scene integration, forgetting prevention, and neural binding mechanisms.
Dr. Christoph von Tycowicz serves as Head of the Research Group "Geometric Data Analysis and Processing" at the Zuse Institute Berlin (ZIB), within the "Visual and data-centric computing" department of the "Mathematics of Complex Systems" division. His research bridges advanced mathematical theory with practical applications in medical imaging, biomechanics, and cultural heritage analysis. He leads multiple interdisciplinary projects connecting mathematics, computer science, and biomedical engineering, with funding from major research initiatives. Dr. von Tycowicz earned his doctoral degree from Freie Universität Berlin in 2014 with a dissertation titled "Concepts and Algorithms for the Deformation, Analysis, and Compression of Digital Shapes" under the supervision of Konrad Polthier. His educational background established the foundation for his current work in geometric data analysis and computational shape modeling. His primary research interests center on Geometric Data Analysis , Shape Analysis , and Manifold-valued Data Processing . He develops mathematical frameworks for analyzing complex shapes in medical imaging, biomechanics, and cultural heritage applications. His work bridges differential geometry with machine learning to create robust methods for shape comparison, classification, and prediction. Dr. von Tycowicz has made significant contributions to Riemannian statistical shape modeling and geometric deep learning, with applications spanning knee osteoarthritis assessment, Alzheimer's disease progression analysis, and archaeological artifact analysis. Analysis of his publication trajectory reveals a sophisticated evolution from foundational geometric methods toward integrated approaches combining differential geometry with deep learning. His recent work increasingly focuses on manifold-valued graph neural networks, shape-based disease grading systems, and longitudinal analysis of anatomical changes. There's a clear trend toward clinical translation, with growing emphasis on applying these methods to specific medical problems like osteoarthritis assessment using data from the Osteoarthritis Initiative and Alzheimer's disease progression modeling. Dr. von Tycowicz has received significant recognition for his contributions: Best Paper Honorable Mention Award @ Eurographics (2016) Best Paper Award (2020) Student Travel Award (2020) Special Mention @ ICLR Computational Geometry & Topology Challenge (2022) As a mentor, Dr. von Tycowicz has supervised doctoral and master's students including Felix Ambellan (doctoral thesis on Efficient Riemannian Statistical Shape Analysis with Applications in Disease Assessment) and Martha Paskin (master's thesis on Estimating 3D Shape of the Head Skeleton of Basking Sharks). He currently leads multiple substantial research projects including WEAR (mathematical solutions for analyzing ancient tools), Model-Regularized Learning of Complex Dynamical Behavior, and Geometric Learning for Single-Cell RNA Velocity Modeling, demonstrating strong grant acquisition capabilities across interdisciplinary domains. The Geometric Data Analysis and Processing research group, which Dr. von Tycowicz heads, develops the open-source Morphomatics library (v4.0) for statistical shape analysis. This Python library implements intrinsic manifold-based methods that maintain geometric consistency while avoiding bias from arbitrary coordinate choices. The group participates in major research networks including MATH+ and BIFOLD, and collaborates extensively with medical researchers at Charité - Universitätsmedizin Berlin and other institutions. Their work spans medical imaging (particularly knee osteoarthritis analysis), biomechanics, archaeology, and machine learning, with a unifying focus on creating geometrically principled methods for analyzing complex shape data.
Dr. Sajid Javed is an Assistant Professor of Computer Science at Khalifa University of Science and Technology, UAE, and affiliated with the Khalifa University Centre of Autonomous Robotics Systems (KUCARS) and the University of Warwick, United Kingdom. He leads the computer vision group at KUCARS and focuses on research spanning computer vision, machine learning, and computational pathology. PhD: Computer Science and Engineering, Kyungpook National University, Republic of Korea BSc (Hons): Computer Science, University of Hertfordshire, UK His research interests include: Broad disciplines: Computer Vision, Machine Learning, Artificial Intelligence, Computational Pathology Specific subfields: Visual Tracking, Multi-Object Tracking, Object Detection, Background-Foreground Modeling, Video Object Segmentation, Histology Image Classification, Tissue Phenotyping, Nucleus Detection/Classification Applications: Underwater Video/Image Analysis, Cancer Diagnosis, Tissue Classification Dr. Javed supervises several PhD, MSc, and postdoctoral researchers, including: PhD candidates: Basit Alawode, Moshira Ali Abdala, Abderrahmene Boudiaf, Muaz Khalifa Alradi MSc candidate: Mehnaz Umar Combined Master-PhD student: Omar Ibrahim Belal Current projects include: Artificial Intelligence for Oceans Surveillance Developing a large-scale underwater video analysis dataset with enhanced algorithms "Detecting Cancerous Footprints from the Histopathological Landscape" Muhammad Bin Zayed International Robotics Challenge-2023
