James Stewart is a Professor at Queen's University's School of Computing. His research focuses on biomedical computing and surgical navigation systems. Education: Ph.D. in Computer Science from Cornell University (1992) Location: Office Goodwin 732 Contact: Phone 613 533-3156 Research Interests Professor Stewart's work bridges computer graphics, image processing, and medical applications. Key areas include: Computer-assisted surgical navigation 3D medical visualization Geospatial data representation Robust geometric computation Human-computer interaction in clinical settings Medical imaging uncertainty analysis Scientific Awards Best Poster Award (2013) for 'Image-guided Osteochondral Autologous Autografting of the Ankle' Publications His publications span multiple domains including: Computer graphics algorithms Medical imaging techniques Geospatial visualization Biomedical engineering applications Surgical navigation systems Robust geometric computation
Anthony Clark is an Assistant Professor of Computer Science at Pomona College, where he has been teaching since 2020. Previously, he served as an Assistant Professor at Missouri State University from 2016 to 2020. He directs the ARCS (Autonomous Robotics and Complex Systems) Lab, which focuses on improving the robustness and adaptability of autonomous robots, particularly small-scale systems that can navigate unpredictable terrain and adapt to potential damage. Clark earned his Ph.D. in Computer Science from Michigan State University in 2016, where he worked under Dr. Philip K. McKinley, and his B.S. in Computer Engineering from Kansas State University, graduating magna cum laude. His research centers on making autonomous robots more robust and adaptive through optimization algorithms and multimodal systems. He specializes in evolutionary robotics, computer vision, neural networks, and simulation methods for developing control systems that leverage multiple locomotion mechanisms. His recent work demonstrates strong trends across several domains: developing hybrid locomotion systems (wheel/leg transformations), applying deep learning to terrain classification and pathfinding, using simulation environments for training, and exploring pretraining techniques for evolutionary robotics. His research shows a consistent focus on bridging simulation and real-world applications while addressing challenges in robot adaptability and robustness. Faculty Excellence in Teaching, Missouri State University (2018) Best Paper Award, Workshop on Evolutionary and Reinforcement Learning (2013) Best Paper Award, ALIFE Conference, Behavior and Intelligence Track (2012) Outstanding Reviewer, Elsevier (2018) Master Advisor Certification, Missouri State University (2017) Clark has advised numerous undergraduate and graduate students through the ARCS Lab, with current research involving projects like the Adabot (a robot with multiple locomotion mechanisms) and thermal semantic segmentation for aerial field robots. His teaching portfolio includes courses on data structures, algorithms, neural networks, computer systems, and mobile robotics. He has also served as a Visiting Associate at Caltech's ARC Lab from 2023-2024, working with Dr. Soon-Jo Chung. The ARCS Lab develops simulation environments, optimizes control systems, and fabricates physical robots. Current projects include the Adabot with its geared coaxial shaft mechanism for hybrid locomotion, thermal semantic segmentation using satellite data, and creating dynamic simulation environments with Unreal Engine 5. The lab emphasizes practical applications of theoretical research while training students in both hardware and software aspects of robotics.
Jonas Rubenson is a Professor of Kinesiology in the Department of Kinesiology, College of Health and Human Development, at The Pennsylvania State University. His research focuses on the mechanics and energetics of locomotion, in vivo skeletal muscle function, and musculoskeletal structure-function relationships. Ph.D., 2005, Biomechanics, The University of Western Australia B.Sc. (Hon), 1998, Exercise Physiology, The University of Western Australia B.Sc., 1996, Biology and Human Kinetics, University of British Columbia His research integrates experimental and modeling approaches to study gait and skeletal muscle function during locomotion in both health and disease/impairment. Key areas include the relationship between joint and muscle mechanics and metabolic energetics, as well as mechanisms underlying locomotor adaptation and optimization. Recent publications emphasize locomotor plasticity, tendon stress in hopping kangaroos, and musculoskeletal modeling in birds and bipedal models. Rubenson collaborates with research centers such as the Integrative and Biomedical Physiology and the Center for Movement Science and Technology . His work often involves interdisciplinary approaches, combining biomechanics, physiology, and robotics. Current research projects investigate principles of muscle function during movement, with applications in understanding locomotion in extinct theropod dinosaurs and developing legged robots. His team also explores developmental plasticity of locomotor economy and swing-phase mechanics in avian models.
Andrew R. Jamieson is an Assistant Professor in the Lyda Hill Department of Bioinformatics at UT Southwestern Medical Center, where he leads a research team focused on developing advanced AI systems for medical education and clinical performance assessment. He was appointed in 2019 and serves as Principal Investigator of the Jamieson Group. Institution: UT Southwestern Medical Center School: School of Health Professions Department: Lyda Hill Department of Bioinformatics Academic Rank: Assistant Professor Dr. Jamieson earned his B.A. in Physics with honors (2006) and Ph.D. in Medical Physics (2012) from the University of Chicago. His early work in computer-aided diagnosis laid the foundation for his career in AI and machine learning. Education: University of Chicago (B.A., Ph.D.) Prior Experience: GE Healthcare, Big Data Analytics Startup (First Data Scientist) Dr. Jamieson's research lies at the intersection of artificial intelligence, medical education, and bioinformatics. His team leverages multimodal data—including video, audio, and text—from the UTSW Simulation Center to train frontier AI models for automated assessment of medical student performance. His work in computational image analysis spans label-free live-cell imaging, spatial biology, and highly multiplexed immunofluorescence, with applications in cancer biology and diagnostics. He has also made significant contributions to public health through the development of the UTSW COVID-19 forecast model. The most recent publications reflect a strong trend toward AI-driven medical education tools, particularly using large language models and multimodal AI for OSCE assessment. Earlier works focus on deep learning in medical imaging, dimensionality reduction, and computer-aided diagnosis in mammography. The research consistently emphasizes interpretability, automation, and clinical translation. Scientific recognition includes being featured on the cover of Cell Systems (July 2021) for work on melanoma cell analysis. His team's development of the first automatic AI grading system for medical student OSCE notes in 2023 marks a major innovation in educational assessment. Featured on cover of Cell Systems (2021) Developed UTSW COVID-19 forecast model Pioneered AI grading system for OSCE notes (2023) Dr. Jamieson is actively involved in mentoring and graduate education. He serves as Course Director for the Master’s in Health Informatics program and contributes to nanocourses at the Clinical Informatics Center. His team includes multiple advisees and collaborators working on NLP, LLMs, and AI/ML in healthcare. He is expanding his group and seeking researchers in AI, data science, and software development. His leadership in the Bioinformatics Core Facility (2018–2021) and ongoing collaborations with pathologists and radiation oncologists demonstrate strong interdisciplinary grant and project engagement. Course Director: Master’s in Health Informatics Mentor to multiple graduate students and researchers Collaborations: Pathology, Radiation Oncology, Surgery, Clinical Informatics The Jamieson Group is a dynamic, interdisciplinary research team at the forefront of applying cutting-edge AI to medical education and clinical data analysis. The lab focuses on natural language processing, multimodal learning, and computer vision, with strong ties to the UTSW Simulation Center and Clinical Informatics Center. The team develops custom pipelines for spatial biology and imaging data and is actively expanding to meet growing research demands.
Bert de Vries is a Professor at the Signal Processing Systems Group at Eindhoven University of Technology (TU/e), where he has been employed since January 2012. He maintains a dual career, also working at GN Hearing in the hearing aids industry since April 1999, where he holds both research and managerial roles. His academic journey began at TU/e, where he earned his MSc in Electrical Engineering in 1986, followed by a PhD from the University of Florida in 1991. Between 1992 and 1999, he worked at Sarnoff Research Center in Princeton, NJ, contributing to diverse signal and image processing projects. Professor de Vries's research centers on Bayesian Machine Learning, with particular focus on the Free Energy Principle and its applications to engineering problems. His work bridges theoretical neuroscience with practical signal processing systems, especially in biomedical applications. He directs the BIASlab research team at TU/e, which develops probabilistic programming tools including RxInfer.jl, ForneyLab.jl, GraphPPL.jl, ReactiveMP.jl, and Rocket.jl. His research spans active inference, variational message passing, probabilistic programming, and Bayesian neural networks, with applications ranging from hearing aids to multi-agent systems. Analysis of his recent publications reveals a strong trend toward practical implementations of Bayesian inference frameworks, particularly through Julia-based probabilistic programming tools. His work shows increasing focus on active inference applications, message passing algorithms, and the intersection of Riemannian geometry with probabilistic modeling. The research demonstrates consistent progression from theoretical foundations toward real-world engineering applications, particularly in biomedical signal processing and autonomous systems. Professor de Vries teaches a graduate-level course on Bayesian Machine Learning at TU/e and actively contributes to open-source software development through his GitHub profile (bertdv), with recent activity as recent as August 2025. His research team has developed several influential probabilistic programming libraries that have gained significant attention in the machine learning community. The BIASlab research group continues to advance the state of the art in Bayesian inference methods with applications in hearing technology, robotics, and signal processing.
Peter Kazanzides is a Research Professor in the Department of Computer Science at the Whiting School of Engineering, Johns Hopkins University, where he joined the faculty in 2002. His research focuses on robotics, medical robotics, augmented reality, and computer-assisted interventions with primary applications in computer-integrated surgery. His educational background includes multiple degrees from Brown University: ScB (1983) in Electrical Engineering AB (1983) in Computer Science ScM (1985) in Electrical Engineering ScM (1987) in Applied Mathematics PhD (1988) in Electrical Engineering Kazanzides is a member of the Robotics, Vision, and Graphics research group and directs the Sensing, Manipulation, and Real-Time Systems (SMARTS) laboratory. His work spans surgical robotics, mixed reality, and systems engineering, with emphasis on computer-assisted surgery in extreme environments including minimally invasive surgery, microsurgery, and space teleoperation. The SMARTS lab develops real-time sensing systems, augmented/mixed reality interfaces using head-mounted displays, high-performance motor control, and sensor fusion technologies, with strong focus on system integration and open-source platforms like the da Vinci Research Kit (dVRK). Analysis of his recent publications (2024-2025) reveals dominant trends in surgical robotics autonomy, augmented reality navigation, force estimation, and digital twin technologies. Key themes include AI-driven task automation, haptic feedback enhancement, real-time instrument segmentation, and simulation environments for surgical training, primarily leveraging the da Vinci Research Kit framework. As director of the SMARTS lab within the Laboratory for Computational Sensing and Robotics (LCSR), Kazanzides leads a collaborative ecosystem including the Computer Integrated Interventional Systems (CIIS) Lab, Advanced Medical Instrumentation and Robotics (AMIRO) Lab, Dynamical Systems and Controls Lab (DSCL), Computer Aided Medical Procedures (CAMP) Lab, Medical UltraSound Imaging & Intervention Collaboration (MUSiiC) Lab, and Photoacoustic & ULtrasonic Systems Engineering (PULSE) Lab. His lab maintains responsibility for the development and support of the open-source da Vinci Research Kit, a critical resource for surgical robotics research worldwide.
Dr. Mingfeng Wang is a Senior Lecturer in Robotics and Autonomous Systems at Brunel University London, affiliated with the Department of Mechanical and Aerospace Engineering within the College of Engineering, Design and Physical Sciences. His research focuses on specialized robotic systems including continuum, legged, soft, precision farming, and miniaturized robots. Chartered Engineer (CEng) with Engineering Council UK Fellow of the Higher Education Academy (FHEA) Member of IEEE, IEEE-RAS, IMechE, and IFToMM Editorial roles: Associate Editor of International Journal of Advanced Robotic Systems (JCR-Q3); Associate Editor of Frontiers in Robotics and AI (JCR-Q2); Editor of Information Processing in Agriculture (JCR-Q1), Biomimetic Intelligence and Robotics (JCR-Q1), and STEM Education Research expertise includes: Continuum Robotics : Design of extra-slender continuum robots (diameter-to-length ratio Legged Robotics : Parallel mechanism-based biped and hexapod robots for extreme environments Miniaturized Robotics : Active locomotion and drug delivery in capsule endoscopes Soft Robotics : Compliant end-effectors and bio-inspired designs Precision Farming : Laser weeding systems and agricultural automation Key scientific awards: BRIEF award (2022) TAROS Best Paper Post Nomination (2022) IFToMM Asian-MMS Best Paper Award (2014) Recent publications focus on: Cochlear implant surgery robotics Passive compliance in train fluid servicing Snake-biomimetic sealing surfaces Parallel kinematic manipulators Capsule endoscope image enhancement Professional services include conference organization (TAROS 2023/2024 Steering Committee; TAROS 2024 Programme Chair) and journal refereeing for IEEE-ASME Transactions on Mechatronics and Scientific Reports.
Andrea Simonetto is a Research Professor at the Applied Mathematics Unit (UMA) , ENSTA Paris, Institut Polytechnique de Paris. His work spans optimization, control theory, and learning algorithms for large-scale and streaming data , with applications in smart grids, intelligent transportation, personalized health, and quantum computing. Current research focuses on online algorithms for time-varying optimization , personalized optimization for cyber-physical systems , and variational quantum algorithms . Past contributions include theoretical and algorithmic advances in convex/non-convex optimization, distributed optimization (robotic networks, smart grids), and signal processing for sparse reconstructions and parallel computing in particle filtering. Key application domains include renewable energy integration , quantum state preparation , and human-in-the-loop control systems . His research is published in journals like ACM Transactions on Quantum Computing , IEEE Control Systems Letters , and Automatica .
Bo Chen is a Professor in the Department of Mechanical Engineering – Engineering Mechanics and the Department of Electrical & Computer Engineering at Michigan Technological University. She directs the Intelligent Mechatronics and Embedded Systems (IMES) Laboratory, focusing on advanced controls, optimization, and artificial intelligence for connected and autonomous vehicles, electric vehicle–smart grid integration, and smart mobility. PhD in Mechanical and Aeronautical Engineering from the University of California, Davis (2005) Visiting Professor at Argonne National Laboratory (2014–2015, 2016) Sabbatical at Oak Ridge National Laboratory (2022–2023) Dr. Chen's research spans Mechatronics , Embedded Systems , Hybrid Electric Vehicles , and Cyber-Physical Systems . Her work includes vehicle-to-grid integration , battery control systems , and cybersecurity for automotive systems . Recent publications highlight advancements in predictive control algorithms for hybrid vehicles, consensus-based frequency regulation , and plausibly deniable encryption systems for mobile devices. Funded by the National Science Foundation, Department of Energy, and industry partners, her research has secured over $10 million in grants. ASME Fellow Best Paper Award (2008 IEEE/ASME MESA Conference) Top Cited Article Award (Journal of Computers & Graphics) Best Survey Paper Award (IEEE Transactions on ITS) Co-recipient of four Best Student Paper Awards Dr. Chen has held leadership roles as Chair of the Technical Committee on Mechatronics and Embedded Systems (IEEE ITS Society), Chair of the ASME Design Engineering Division's Technical Committee, and Associate Editor for IEEE Transactions on Intelligent Transportation Systems (2012–2019). She organized multiple international conferences and co-edited special issues on intelligent transportation systems.
Denny Yu is an Associate Professor at the Edwardson School of Industrial Engineering, Purdue University. His work bridges human factors, neuroergonomics, and healthcare safety through advanced sensor systems and AI. Primary Affiliation : Edwardson School of Industrial Engineering, Purdue University Research Themes : Surgical ergonomics, autonomous vehicle human factors, cognitive workload assessment, multimodal physiological sensing Dr. Yu's research focuses on neuroergonomics and human-robot interaction , particularly in surgical and transportation contexts. His team develops sensor-based systems for workload monitoring, including: EEG-eye tracking fusion for situation awareness Wearable exoskeletons for surgical posture support Computer vision tools for lifting task risk analysis Smart infusion pump usability frameworks AI-driven surgical coaching systems Recent publications emphasize deep learning applications in soft tissue deformation estimation and real-time adaptive systems for robotic surgery augmentation. His work spans both occupational health (veterinary surgeons, airport workers) and medical device innovation domains.
Jon Heiselman is a Research Assistant Professor in the Department of Biomedical Engineering at Vanderbilt University School of Engineering. He serves as Associate Director of the Master of Engineering in Surgery and Intervention Program and leads research in image-guided surgical technologies. His work focuses on soft tissue deformation modeling, augmented reality applications, and computational frameworks for precision surgery. Education: PhD in Biomedical Engineering (Vanderbilt University, 2020) Advisor: Michael Miga, Harvie Branscomb Professor Research interests span image-guided surgical navigation, deformable registration algorithms, and digital twin modeling for therapeutic forecasting. Articles highlight advancements in soft tissue deformation correction, augmented reality integration, and machine learning approaches for real-time surgical guidance. Current affiliations include Vanderbilt's Biomedical Modeling Laboratory (BML) and the VISE Steering Committee. He contributes to NIH-funded training programs and has received recognition for his work in surgical data science and computational oncology.
Dr. Daniel Berio is a researcher at Goldsmiths, University of London, specializing in computational models for human-like movement in digital art and robotics. His work bridges computer graphics, cognitive psychology, and robotic manipulation, focusing on stylized stroke generation, graffiti analysis, and kinematic modeling. He collaborates with Frederic Fol Leymarie and Rejean Plamondon, utilizing the Sigma Lognormal model to simulate human handwriting dynamics. Education : Doctoral thesis on AutoGraff (2021), exploring computational understanding of graffiti and calligraphy. Research Themes : Human-like motion in digital art, kinematic reconstruction from static traces, robotic graffiti generation, and perceptual fluency in aesthetic evaluation. Publications : 15+ works since 2015, spanning ACM Transactions on Graphics, British Journal of Psychology, and conferences like MOCO and IROS. Applications : Font stylization tools, synthetic graffiti generation, compliant robot control, and semantic typography systems.
Prof. Dr. Frank Kirchner is a leading expert in robotics and artificial intelligence, serving as Executive Director of DFKI Bremen and Professor of Robotics at the University of Bremen since 2002. He founded the Robotics Innovation Center (DFKI Bremen) and co-founded the Brazilian Institute of Robotics (2013) and Institute for Maritime Technologies (MarTech, 2010). Education: Doctorate in Informatics (Dr. rer. nat, Bonn, 1999); Diploma in Informatics and Neurobiology (Bonn, 1994) Research Interests focus on biologically inspired robotic motion, highly redundant systems, AI-driven autonomous robotics, and cross-domain applications in space, maritime, and industrial contexts. His work bridges theoretical and applied robotics, emphasizing simulation-reality gaps and adaptive control frameworks. Recent Publications highlight advancements in co-design optimization for manipulators, planetary exploration robotics, and sensor-based industrial assembly learning, reflecting interdisciplinary trends in AI, mechatronics, and environmental adaptation. Scientific Awards: Honorary Doctorate (2017, SENAI CIMATEC, Brazil) BBAW Membership (2015–) Advising includes supervising 65 doctoral theses (38 completed, 27 in progress) and mentoring over 250 bachelor’s/master’s students at the University of Bremen. He leads major projects like AI-REEFSHIELD and FieldCoBots , with funding from EU, DFG, BMBF, and DLR.
Truong Nghiem is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), part of the College of Engineering and Computer Science. He holds a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania and previously served as an Assistant and Associate Professor at Northern Arizona University (2018–2024). His research focuses on intelligent cyber-physical systems, digital twins, physics-informed machine learning, and distributed optimization/control, with applications in smart buildings, autonomous vehicles, and robotics. He leads the intelligent Cyber-Physical Systems (iCPS) Lab, advancing foundational research in these areas. Notable awards include the NSF CAREER Award (2023) for composite physics-informed learning and the NSF ERI Award (2022) for building HVAC system research. He is a Senior Member of the IEEE and a member of the ACM. Recent work emphasizes safe machine learning for control systems, communication-efficient optimization algorithms, and physics-constrained motion prediction for autonomous systems. His publications span journals like IEEE Robotics and Automation Letters and conferences such as the American Control Conference. Grants and research initiatives include projects on HVAC system modeling, distributed trajectory planning for multi-vehicle systems, and adaptive sampling strategies for mobile sensor networks. Collaborations involve industry and academic partners in energy systems, robotics, and control engineering. The iCPS Lab’s work integrates theory, simulation, and real-world validation to address complex cyber-physical challenges.
Dr. Andrew Nordin is an Assistant Professor in the Department of Biomedical Engineering at the University of Houston's Cullen College of Engineering. His research focuses on neuromechanics, neural engineering, and biomechanics of human movement control during gait and locomotion. He holds degrees from Lakehead University (BS, HBK, MS) and the University of Nevada, Las Vegas (PhD), with postdoctoral training at the University of Michigan. Previously, he was a Research Assistant Scientist at the University of Florida and an Assistant Professor at Texas A&M University. Dr. Nordin's academic training spans biomechanics, physiology, neural engineering, and signal processing. His lab, the Nordin Neuromechanics Laboratory, develops methods for measuring electrophysiological signals (EEG/EMG) during dynamic movements. Current projects include studying brain-muscle dynamics during obstacle avoidance in virtual reality, bodyweight-supported treadmill walking, and dual-task gait asymmetries in older adults. His research integrates wearable sensors and machine learning to analyze movement biomechanics and neural control in real-world settings. Key areas of focus include neuromuscular adaptation, gait variability, and injury mechanisms. Dr. Nordin collaborates across disciplines to advance wearable technologies for clinical and sports performance applications. His publications emphasize EEG/EMG signal processing innovations to mitigate motion artifacts, and explore how gravitational environments (e.g., low-gravity simulations) affect balance control and cortical activity. Despite prolific publishing, no specific awards are listed in his profile. Lab activities include: 1) Biomechanics & Neural Engineering - hardware validation for mobile EEG/EMG systems; 2) Locomotion Neuromechanics - kinematic/kinetic analysis during walking/running; 3) Applied Neuromechanics - injury prevention strategies using wearable sensors. Office located in SERC 2020 with lab website at nordinlab.com .
