Dr. Babar Jamil is a Lecturer in Electrical Engineering at the University of York's School of Physics, Engineering and Technology. His expertise spans robotics, sensors, control engineering, and mechanism design. He holds a Ph.D. from Hanyang University (South Korea) and conducted postdoctoral research at Sungkyunkwan University, where he also served as a Research Professor. His current research focuses on safe human-robot collaboration systems, novel control algorithms for robotic systems, and smart structures through sensor integration. Education: Ph.D. in Electrical and Electronic Engineering, Hanyang University, South Korea Postdoctoral Researcher, Sungkyunkwan University, South Korea Research Interests: Developing hybrid robotic manipulators combining soft and rigid actuation Designing proprioceptive sensors for extreme environments Advances in pneumatic artificial muscles and soft actuators Integration of machine learning in robotics control systems Publications: Recent work emphasizes soft robotics actuators, sensor design, and human-robot interface innovations. Key themes include energy-efficient actuation, sensorized robotic fingers, and pumpless pneumatic systems. Labs/Teams: Leads robotics research at York, focusing on collaborative robotics and sensor-actuator integration. Maintains an active research group through his UoY Robotics website .
Dr. Kayo Ide is an Associate Professor at the University of Maryland's Department of Atmospheric and Oceanic Science, within the College of Computer, Mathematical, and Natural Sciences. Her research focuses on dynamics of atmosphere and oceans, with expertise in data assimilation, scientific prediction, transport/mixing processes, and climate variability. She contributes to NOAA's operational systems and collaborates with teams like the UFS Coastal Applications Team. Her work emphasizes integrating advanced observational technologies (e.g., satellite data from CrIS, Aeolus) into numerical weather prediction and ocean modeling frameworks. Key projects include optimizing data assimilation algorithms, evaluating new sensor constellations (e.g., CubeSats), and improving forecast initialization techniques. Dr. Ide also develops software tools like the System for Analysis of Wind Collocations (SAWC) to intercompare multi-platform wind observations. Publications highlight innovations in satellite data utilization, ensemble-based methods, and the impact of novel observing systems on operational forecasting. Her research bridges computational methods, environmental science, and applied meteorology, addressing challenges in global climate monitoring and predictive modeling.
Cara Tomaso, Ph.D., is an Assistant Professor and licensed clinical psychologist specializing in pediatric psychology at Yale School of Medicine. Her practice is embedded within the Section of Pediatric Orthopaedics and Rehabilitation at Yale New Haven Hospital, where she contributes to the Yale Limb Restoration and Lengthening Program and the Female+ Athlete Program. She provides behavioral health consultation to multidisciplinary teams and direct interventions for youth and families navigating chronic/acute health conditions, surgical readiness, and behavioral pain management. Dr. Tomaso’s education includes a PhD from the University of Nebraska-Lincoln (2023) and postdoctoral training at the Yale Child Study Center (2024). Her research focuses on the interplay between executive control, mental health, sleep, and obesity-related outcomes across developmental stages. Her work integrates longitudinal studies on childhood executive function’s role in BMI trajectories, pandemic-related psychopathology buffering, and neurobiological mechanisms of obesity. She has published extensively on adolescent health behaviors, sleep’s impact on mood/emotion regulation, and culturally informed approaches to stress resilience in college students. Dr. Tomaso’s clinical and research expertise bridges behavioral medicine, developmental psychology, and pediatric healthcare systems.
Gita Reese Sukthankar is a Professor in the Department of Computer Science at the University of Central Florida (UCF) , where she directs the Intelligent Agents Lab . Her research focuses on activity and plan recognition , with applications in multi-agent systems, robotics, and human-robot interaction. She earned her Ph.D. from the Robotics Institute at Carnegie Mellon University and joined UCF in fall 2007. Research Interests: Her work spans activity recognition , intent inference , multi-agent coordination , and human-robot teams . She has applied these techniques to domains such as adversarial games (e.g., military simulations, Unreal Tournament), assistive technologies, and cooperative robotics. Her research integrates AI, machine learning, and probabilistic models to understand and predict complex team behaviors. Publication Trends: Her publications emphasize spatio-temporal modeling , probabilistic graphical models (e.g., HMMs, CRFs) , and multi-agent plan recognition . She frequently publishes in top venues like AAMAS, AAAI, and ICRA, with a focus on robust recognition of team behaviors, transfer learning, and real-world AI applications. Scientific Awards: NSF CAREER Award (2009) AFOSR Young Investigator (2009) ONR Summer Faculty Fellow (2008) UCF Faculty Excellence for Doctoral Mentoring (2012) CECS Dean's Research Professorship (2013) AAAI Senior Member (2021) ACM and IEEE Senior Member Advising and Grants: She mentors graduate students in AI and robotics and has led research funded by DARPA, AFOSR, and ONR. Her lab develops systems for intelligent agents that can understand and collaborate with humans. She has served on numerous program committees and editorial boards, including ACM Transactions on Autonomous and Adaptive Systems . She teaches courses such as Intelligent Systems , Robotics , and Machine Learning , and has been recognized for both research and teaching excellence. Labs and Teams: She leads the Intelligent Agents Lab at UCF, which focuses on data-driven social informatics and AI for human-agent teams. Her group collaborates with researchers in robotics, computer vision, and cognitive science to build adaptive, intelligent systems.
Luyang Zhao is an incoming tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University (starting August 2025). He earned his PhD in Computer Science and double undergraduate degrees in Computer Science and Mathematics from Dartmouth College and the University of Minnesota respectively. Academic Affiliation : Clemson University (Assistant Professor) Education : PhD in Computer Science (Dartmouth College), BS in Computer Science & Mathematics (University of Minnesota) His research focuses on Robotics , particularly soft robotics, modular systems, and bio-inspired designs. Key areas include: Large Language Models for robotic design automation Modular tensegrity systems for self-assembling structures Swarm coordination strategies Multi-environment adaptability (land/aquatic/aerial) Simulation tool integration for design optimization Recent publications highlight his work on SoftSnap modular platforms, LLM-driven swarm intelligence, and bioinspired dolphin robots. He received the Neukom Outstanding Graduate Research Prize for his contributions. Industry Experience : Research internships at Amazon Robotics and TuSimple Mentorship : Advised 6+ graduate/undergraduate researchers Open-Source Contributions : Developed SoftSnap platform for rapid prototyping Academic Service : Workshop co-organization (IROS 2023), peer reviewing (RA-L, ICRA, IROS, RoboSoft, BioRob)
Sohail K. Mirza, MD, MPH is a Professor of Engineering at Dartmouth College's Thayer School of Engineering, specializing in biomedical engineering and orthopaedic surgery. His dual roles as a clinician and researcher focus on spinal biomechanics, surgical innovation, and healthcare policy. He received a BA in Physics from Colorado College (1985), an MD from the University of Colorado (1989), and an MPH from the University of Washington (2005). Research Interests: Dr. Mirza's work bridges clinical practice and engineering, with a focus on improving spinal surgery outcomes through advanced imaging techniques (e.g., intraoperative stereovision), reducing surgical overuse via policy analysis, and developing evidence-based guidelines for lumbar fusion procedures. His innovations include systems for pain measurement post-surgery and handheld stereovision tools for surgical navigation. Awards & Recognition: 2014 American Academy of Orthopaedic Surgeons Kappa Delta Award 2002/2008 University of Washington Service Excellence Award 1998 Cervical Spine Research Society Award Grants & Collaborations: His research has been supported by the National Institutes of Health and the Dartmouth College NSF I-Corps. He collaborates with biomedical engineers like Keith Paulsen and clinicians such as Roberts DW on projects like image-based registration for spine surgery. Labs & Teams: Leads the Spinal Surgery Innovation Lab at Thayer School, focusing on translating engineering solutions into clinical practices. Co-directs the Dartmouth Center for Surgical Innovation.
Diego Patiño is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington (UTA), a position he began in September 2024. He earned his Ph.D. in Computer Engineering from the National University of Colombia in 2020, following M.S. and B.S. degrees from the same institution. Prior to joining UTA, he served as a Postdoctoral Fellow at Drexel University and a Postdoctoral Researcher at the GRASP Laboratory, University of Pennsylvania. B.S. in Computer Engineering, National University of Colombia, 2010 M.S. in Computer Engineering, National University of Colombia, 2012 Ph.D. in Computer Engineering, National University of Colombia, 2020 Dr. Patiño's research centers on geometric computer vision and machine learning, with applications in robotics and 3D vision. His primary interests include 3D reconstruction, graph neural networks, symmetry detection, physics-informed machine learning, and reinforcement learning. He develops algorithms that integrate geometric priors and physical constraints into deep learning models to improve robustness and generalization in real-world robotic systems. His recent publications demonstrate a strong trend in leveraging implicit neural representations for 3D shape reconstruction, applying graph neural networks to swarm robotics, and enhancing computer vision tasks with self-supervised and physics-informed learning. Work spans high-impact venues such as IEEE RA-L, ICRA, ICPR, and MICCAI, showing a consistent focus on geometric reasoning, robotic perception, and medical imaging applications. His scientific contributions have been recognized with awards from the UTA Division of Student Affairs for exceptional dedication and positive impact (2024 and 2025). He is actively involved in securing research funding, with multiple grants under review from NSF, Air Force SBIR, and industry partners like Sony. Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (December 9, 2024) Exceptional dedication and positive impact recognition, UTA Division of Student Affairs (April 30, 2025) Dr. Patiño advises and serves on committees for multiple graduate students in computer science and engineering, including doctoral and master’s candidates. He is also leading or co-leading several research grants under review, covering topics such as aerial swarm navigation, neuromorphic sensing, and industrial computer vision. He teaches graduate courses in computer vision and is involved in service roles including PhD admissions and faculty appointments committees. He is affiliated with research initiatives at UTA, including the UTARI Research Institute, where he has presented on geometric modeling and physics-informed learning. His lab focuses on developing next-generation computer vision algorithms for robotics, industrial inspection, and safety-critical systems.
Ran Dai is a Professor in the Department of Aeronautics and Astronautics at Purdue University's College of Engineering. His research focuses on optimal control theory, trajectory optimization, and robotics applications, with an emphasis on aerospace systems and energy-efficient solutions. He leads the Autonomous Optimization Lab (AOL) and has contributed extensively to advancements in learning-based control, mixed-integer programming, and deployable space systems. His work spans applications such as spacecraft guidance, unmanned vehicle path planning, and energy management for solar-powered systems. Notable contributions include algorithms for fuel-optimal powered descent, real-time trajectory optimization, and origami-inspired deployable mechanisms. He holds a Ph.D. in Aerospace Engineering and has published over 100 peer-reviewed articles. Research interests include: Optimal control and trajectory optimization Reinforcement learning for decision-making Autonomous systems and robotics Energy-efficient aerospace engineering Recent work emphasizes meta-reinforcement learning frameworks and adaptive optimization engines for complex systems.
Sandra Carroll serves as Vice-Dean of the Faculty of Health Sciences and Executive Director of Nursing at McMaster University. With 81 publications from 2003-2024 (160 total), she is a prominent figure in cardiovascular nursing research and education. Her leadership roles indicate her significant standing within the academic community at McMaster. Dr. Carroll's research interests center on cardiovascular nursing, stroke care in resource-limited settings, patient decision-making, and implantable cardioverter defibrillators (ICDs). Her work strongly emphasizes patient-centered care, shared decision-making models, and healthcare delivery in low-resource environments. The OSCAIL (Organized Stroke Care Across Income Levels) study represents a major component of her research portfolio, focusing on implementing stroke care protocols in countries like Uganda, Rwanda, and South Africa. Analysis of her recent publications reveals a consistent focus on improving patient outcomes through better decision support tools, particularly for cardiac conditions. Her work spans from developing patient decision aids for ICD candidates to studying quality of life impacts of different cardiac device therapies. She has increasingly incorporated digital health approaches, examining remote monitoring technologies and their impact on chronic disease management. Dr. Carroll maintains an extensive collaborative network with 53 co-authors, primarily from McMaster University but also including international partners. Her research shows strong translational impact, with numerous publications referenced in clinical guidelines and policy documents. While specific advising roles aren't detailed in the provided information, her leadership position suggests involvement in mentoring junior faculty and graduate students. Her research program demonstrates particular attention to vulnerable populations, including patients in low-resource settings, women with heart conditions, and immigrant communities navigating healthcare systems. This focus on equity and accessibility represents a consistent thread throughout her scholarly work.
Maria Paz Linares Herreros is a Lecturer at the Universitat Politècnica de Catalunya (UPC), affiliated with the School of Mathematics and Statistics (FME) and the Department of Statistics and Operations Research. She is a member of the IMP (Information Modeling and Processing) research group and collaborates with inLab FIB on intelligent transportation systems. Research interests: Transportation systems, smart cities, traffic simulation, data-driven modeling, environmental impact assessment Specializes in applying machine learning and simulation to urban mobility challenges Her recent publications focus on: Parking availability prediction using deep learning Traffic emission modeling linked to urban policies Dynamic ride-sharing system optimization Integration of IoT data in transportation planning Scientific recognition: Recipient of the IV International Award on Transport Infrastructure Management Research (2018) Active contributor to projects like CitScale and Virtual Mobility Lab Collaborator in European initiatives like KIC Urban Mobility
Dr. Benjamin Evans is an Assistant Professor in Computer Science & AI (Informatics) at the University of Sussex , affiliated with the School of Engineering and Informatics . His research integrates computational neuroscience and artificial intelligence, focusing on biologically inspired neural networks. Current Position: Assistant Professor, Department of Informatics, University of Sussex Previous Roles: Research Associate at University of Bristol, University of Exeter, Imperial College London, and University of Oxford Education: DPhil in Computational Neuroscience (University of Oxford), MSc in Intelligent Systems (UCL), BA in Experimental Psychology (Oxford) His research centers on how neural systems self-organize to produce intelligent behavior, studied through both biological and computational modeling. He investigates spiking neural networks , convolutional neural networks , and the role of biological constraints in enhancing AI robustness and human-like perception. He is particularly interested in how spike-based information processing contributes to adaptive cognition in noisy environments. His recent publications reveal a strong trend in evaluating deep neural networks as models of human vision, questioning their biological plausibility while proposing bio-inspired improvements. He also works on optogenetics simulation (e.g., PyRhO platform), developmental biology modeling , and reproducible data science through containerization tools like Docker. His scientific contributions have been recognized through publications in high-impact journals such as Nature Communications , PLoS Computational Biology , and Behavioral and Brain Sciences . EPSRC Grant: "Exploring the multiple loci of learning and computation in simple artificial neural networks" (2023–2024) EPSRC Grant: "Using ant biology and natural environments to enhance models of vision and robot navigation" (2022–2026) Dr. Evans actively contributes to open science through GitHub repositories (e.g., PyRhO, DPE, BioNet) and promotes reproducible research. He has no listed advisees in the provided data, but leads funded research projects involving junior researchers. He is a core member of the Informatics research group at Sussex, contributing to both AI and neuroscience domains.
Dr. Clark N. Taylor is an Associate Professor of Computer Engineering and Director of the ANT Center at the Air Force Institute of Technology (AFIT), located at Wright-Patterson Air Force Base, Ohio. He is actively engaged in research and education within the Graduate School of Engineering and Management, focusing on advanced navigation and sensor fusion technologies for autonomous systems. Ph.D., Electrical and Computer Engineering (Computer Engineering), University of California, San Diego, 2004 M.S., Electrical and Computer Engineering, Brigham Young University, 1999 B.S., Electrical and Computer Engineering, Brigham Young University, 1995 Dr. Taylor's research spans computer engineering, navigation systems, and autonomous robotics, with a strong emphasis on sensor fusion, state estimation, and robust uncertainty modeling. His work integrates vision, inertial, magnetic, and pressure sensors for navigation in GPS-denied environments, particularly for unmanned aerial vehicles (UAVs). He is a leading expert in factor graph-based estimation, visual-inertial odometry, cooperative localization, and magnetic navigation. His publications demonstrate a consistent trend toward robust, uncertainty-aware estimation frameworks. Over the past decade, his research has evolved from early work on visual stabilization and pose estimation to advanced topics such as conservative covariance estimation, invariant filtering, and machine learning for spacecraft pose estimation. His recent articles focus on factor graphs, multi-agent fusion, and deep learning, indicating a trajectory toward intelligent, resilient navigation systems for defense and aerospace applications. Scientific awards include a Best Presentation in Session award at the ION GNSS+ conference in 2021. His research is supported by the U.S. Air Force and related defense agencies, with applications in surveillance, autonomous refueling, and on-orbit inspection. Dr. Taylor has advised numerous MS and PhD students, particularly in the areas of UAV navigation, sensor fusion, and cooperative localization. His lab, the ANT Center, focuses on advanced navigation and tracking, bringing together students and researchers to develop cutting-edge solutions for real-world operational challenges. The team conducts both simulation and experimental work, often integrating novel sensor modalities and estimation algorithms for improved system performance.
Sidonie Christophe is a Senior Researcher (Directrice de Recherche, DR1) at UMR LASTIG, a joint research unit of Université Gustave Eiffel, IGN-ENSG, and EIVP. She serves as co-director of the LASTIG laboratory and leads research in geovisualization, map design, and interactive spatial data exploration. She also holds a part-time advisory role (60%) at the French Ministry of Higher Education and Research in the domain of digital technology, environment, climate, and sustainable urban development. PhD in Geographic Information Sciences Senior Researcher, DR1, MTECT Co-Director, LASTIG Laboratory (since 2021) Former Team Leader, GEOVIS (Geovisualization, Interaction, and Immersion) Advisor, Environment and Urban Climate, French Ministry of Higher Education & Research Her research centers on innovative methods for 2D/3D and nD geospatial data visualization, with a focus on enabling spatio-temporal understanding through visual and non-visual spatial thinking. Her work integrates principles from geographic information science, human-computer interaction, and computer graphics. Key areas include urban climate visualization, tactile and augmented reality for accessibility, expressive cartographic rendering, and cognitive aspects of map design. She investigates how aesthetic and semiotic choices impact map comprehension and utility. The 15 most recent publications reflect a strong trend in interactive and accessible geovisualization, particularly for urban and environmental applications. Topics include neural map style transfer, 3D urban climate analysis, tactile maps for the visually impaired, augmented reality in geography, and visual analytics for crisis and climate data. There is a consistent emphasis on user-centered design, interdisciplinary integration, and the development of tools for decision-making under uncertainty. Scientific Recognition and Service: Invited speaker at major conferences (IEEEVIS, AGILE, ICC, CPGIS) Co-organizer of international workshops (e.g., GeoVIS, ISPRS AR/VR sessions) Leader of national research projects (ANR ORACLES, ANR ACTIVmap, ANR ECOCIM) Recipient of international mobility grants (AMICI I-SITE FUTURE) Contributor to national glossaries and research strategy (e.g., French photogrammetry glossary) Advising and Grants: Sidonie Christophe actively supervises PhD students and postdoctoral researchers, including Markie Jiang, Maria-Jesus Lobo, and Alexandre Mielniczek. She leads or participates in multiple funded research projects such as ANR ORACLES (marine flooding visualization), ANR ACTIVmap (tactile 3D maps), and ANR ECOCIM (eco-design of city information models). Her advisory role at the MESR involves shaping national research strategy in digital and environmental sciences. Labs and Research Teams: She is a core member of the GEOVIS team (Geovisualization, Interaction, and Immersion) at LASTIG and previously served as its leader. As co-director of LASTIG, she plays a central role in the leadership and strategic direction of the entire laboratory, which comprises over 100 researchers across four teams: ACTE, GEOVIS, MEIG, and STRUDEL.
Dong Ngo Duy is an Associate Professor in the Department of Civil & Environmental Engineering at Monash University, where he serves as the Head of the Transport Section. He holds a PhD in Traffic Flow Theory and Simulation from Delft University of Technology and has held academic positions at the University of Leeds (UK), University of Canterbury (NZ), and now Monash University (Australia). PhD, Traffic Flow Theory, Technische Universiteit Delft (2006) MSc, Traffic Engineering, Linköpings Universitet (2002) His research focuses on Connected and Autonomous Vehicles (CAVs) , Traffic Flow Theory , Data Fusion , and Urban Network Optimization . He applies AI and machine learning to model, predict, and control multi-modal traffic systems, aiming to develop smart city platforms for sustainable transport in mega-cities. The recent trend in his publications (2022–2025) reflects a strong focus on intelligent transportation, including trajectory planning, risk-aware control, car-following modeling using neural symbolic regression, and intercity mobility analysis. His work bridges theoretical modeling with practical applications in emerging connected environments. Scientific Awards: UK Research Council (EPSRC) Advanced Fellow Award (2011–2016) in Connected and Autonomous Vehicles Dong Ngo Duy actively supervises PhD students and contributes to major research initiatives in intelligent transport systems. His work aligns with UN Sustainable Development Goals, particularly in sustainable cities and transport. He previously chaired the Connected Traffic Systems Lab at the University of Canterbury and continues to lead impactful research in transport innovation.
Mary Hegarty is a Distinguished Professor in the Department of Psychological & Brain Sciences at UC Santa Barbara. She has been on the faculty since 1988 following her PhD from Carnegie Mellon University. Her research focuses on spatial thinking, navigation strategies, and individual differences in spatial abilities, with emphasis on STEM education. She leads the Spatial Thinking Lab, employing methods like fMRI and virtual environments. Current NSF-funded projects explore navigation variability and spatial ability’s role in STEM success. Education: BA and MA from University College Dublin, PhD in Psychology from Carnegie Mellon (1988). Research Interests: Spatial cognition (e.g., mental rotation, navigation strategies), spatial representation in STEM, and effects of technology like GPS on spatial skills. Unique focus on integrating experimental psychology with individual difference analysis. Awards: APS Fellow, Spencer Postdoctoral Fellowship, former Cognitive Science Society board chair. Editorial roles at Journal of Experimental Psychology: Applied and Topics in Cognitive Science. Lab Activities: Develops VR tools for spatial assessment, collaborates on projects like Sea Hero Quest. Focuses on translating spatial cognition research into educational interventions. Current research examines neural correlates of navigation and midlife spatial aging effects.
