Nathan Sturtevant is a Professor at the University of Alberta's Department of Computing Science, an Amii Fellow, and Canada CIFAR Chair. His research spans heuristic and combinatorial search problems, with applications in game AI and pathfinding algorithms. He collaborates with the games industry to implement his research in commercial products. Dr. Sturtevant's research explores search algorithms for single and multiple agents, covering areas such as bidirectional search, meta-learning for game theory, procedural content generation, and multi-agent pathfinding. His work integrates machine learning techniques with classical search algorithms to solve complex problems in game environments. Recent publications demonstrate innovations in search optimization, including novel frameworks for suboptimal bidirectional search, new puzzle difficulty metrics, and applications of transformer models to card game planning. His FarmQuest player telemetry dataset provides resources for studying player behavior in farming simulations.
Dr. Md Noor-A-Rahim is an Assistant Professor (Lecturer-Above the Bar) at the School of Computer Science and Information Technology, University College Cork (Ireland). He previously served as a Senior Researcher and Marie Curie Fellow at the same institution. His academic journey includes a PhD from the University of South Australia (2015) and the prestigious Michael Miller Medal for his outstanding thesis on wireless communication systems. His research focuses on Intelligent Transportation Systems, Machine Learning, IoT, Wireless Networks, and DNA-based data storage. He has published extensively on topics like 6G-V2X systems, time-sensitive networking, and error characterization in DNA storage. His work integrates cutting-edge technologies such as intelligent reflecting surfaces (IRS), federated learning, and ultra-reliable low-latency communication (URLLC). Research Interests : Dr. Rahim's research bridges theoretical advancements and real-world applications in vehicular networks, smart manufacturing, and next-generation communication systems. He explores challenges in autonomous driving, edge computing, and bio-constrained data storage. His contributions include novel coding schemes for anytime transmission and frameworks for mitigating big vehicle shadowing in V2X communications. Key Publications : His recent work includes a comprehensive survey on wireless TSN (2025), analysis of 6G-V2X systems (2022), and breakthrough studies on DNA data storage error modeling (2023). These publications highlight his expertise in both foundational research and industry-relevant solutions. Awards : Recipient of the Michael Miller Medal (2015) for doctoral research excellence. Grants & Labs : While specific grants are not listed in the text, his research portfolio suggests involvement in collaborative projects with industry partners and funding bodies. He leads interdisciplinary efforts in smart manufacturing and vehicular communication systems.
Dr. Anil Ufuk Batmaz is an Assistant Professor in the Department of Computer Science and Software Engineering at Concordia University. His research focuses on Virtual Reality (VR), Augmented Reality (AR), and Human-Computer Interaction, with emphasis on interaction techniques, immersive analytics, and motor skill training systems. He holds a BSc in Electrical and Electronics Engineering (2007-2011), an MSc in the same field (2011-2013), and a PhD in Biomedical Engineering (2015-2018). His work bridges engineering and cognitive science, investigating how visual and haptic feedback impact user performance in immersive environments. Research interests include: 3D interaction techniques for mid-air tasks Effects of display technologies on motor coordination Hybrid UI design for mixed reality systems Training systems for precision tasks using VR Recent publications emphasize evaluation of AR/VR interfaces in healthcare, sports training, and collaborative environments. His work has appeared in venues like IEEE TVCG, ACM CHI, and ISMAR, addressing challenges in spatial navigation, error feedback, and system reliability.
Keyvan Hashtrudi-Zaad is a Professor in the Department of Electrical and Computer Engineering at Queen's University, affiliated with the Smith School of Engineering and the Ingenuity Labs Research Institute. His expertise spans robotics and control systems, with a focus on haptics, telerobotics, tele-rehabilitation, autonomous vehicles, and medical robotics. He holds the email addresses keyvan.hashtrudi-zaad@queensu.ca and khz@queensu.ca, and his office is located in Walter Light Hall, Room 427. Research Interests: His research emphasizes human-robot interaction, haptic interfaces, autonomous systems, and mechatronics. Key areas include kinesthetic haptics, collaborative teleoperation systems, energy storage systems for electric vehicles, and medical robotics applications such as needle deflection estimation and rehabilitation robotics. His work bridges theoretical control systems with practical applications in healthcare and autonomous technologies. Publications: His recent work addresses challenges in haptic system stability, energy-efficient inverters for electric vehicles, and teleoperation networks. Notable projects include a cable-driven parallel robot for stroke rehabilitation and a study comparing DC/BLDC actuators for haptic feedback. His research often integrates sensor fusion, nonlinear control, and dynamic modeling to solve real-world problems. Awards and Recognition: While specific awards are not listed, his extensive publication record and leadership in interdisciplinary robotics initiatives highlight his contributions to the field. He is part of the Interactive Robotics and Intelligent Systems (IRIS) Laboratory, advancing innovations in medical robotics and autonomous systems. Grants and Collaborations: His work likely involves collaborations across engineering and medical disciplines, supported by grants focused on robotics, control systems, and healthcare technologies. The IRIS Lab serves as a hub for developing cutting-edge solutions in haptic training systems and assistive robotics.
Maria Dolores Blanco Rojas is a Full Professor and Deputy Director of the Systems and Automatic Engineering Department at Universidad Carlos III de Madrid (UC3M). Her research focuses on robotics and biomedical engineering, particularly in the development of soft robotic exoskeletons, shape memory alloy (SMA) actuators, and rehabilitation technologies. She leads the Robotics Lab and has contributed to over 100 peer-reviewed articles. Affiliations : UC3M, Robotics Lab, Systems Engineering and Automation Department Education : Not explicitly stated in text Her research interests include: Soft Robotics : Design of wearable exoskeletons for pediatric and post-stroke patients Materials Science : SMA-based actuators for medical and robotic applications Control Systems : Adaptive control algorithms for rehabilitation devices Biomedical Engineering : Integration of sEMG signals for gesture classification in assistive technologies Recent articles explore topics like hyperparameter optimization for machine learning models, SMA actuator efficiency, and eye-hand coordination assessment systems. Projects include the development of pediatric rehabilitation robots (Discover2Walk) and soft exoskeletons for ankle and wrist mobility. Grants/Projects : SRAR (2024–2027): Soft robotics for ankle rehabilitation STRIDE-UC3M (2022–2024): Pediatric exoskeleton validation Advising : Supervised theses on SMA actuators, soft exoskeletons, and rehabilitation systems Her lab develops novel sensors and actuators, including a silver-coated polyamide sensor and multi-wire SMA actuators for high-displacement applications. Collaborations include Airbus and TechnoFusión facilities.
Federico Tombari is a Director of Research at Google Zurich and a Lecturer (Privatdozent) at the Chair of Computer Aided Medical Procedures (CAMP) at TUM. He leads applied research in Computer Vision and Machine Learning, focusing on 3D vision, robotics, augmented reality, autonomous driving, and healthcare applications. His work emphasizes unsupervised learning, large multimodal models, neural radiance fields, and scene graphs. Education & Professional Background: As PD Dr. Ing. Habil., he holds a habilitation in engineering and has been active in academic and industrial research for over a decade. His roles include Area Chair for top conferences like CVPR and ECCV, and Associate Editorships for journals like IJRR. Research Interests: Federico’s research spans 3D scene understanding, object recognition, SLAM, and novel view synthesis. He explores applications in surgical robotics, autonomous systems, and medical imaging. Recent trends in his work include generative models for scene generation and semantic scene graphs for holistic modeling. Grants & Industry Collaborations: He has led projects with Toyota, BMW, Audi, Zeiss, and others, focusing on 3D perception, autonomous driving, and medical vision. His work bridges academia and industry, emphasizing practical applications. Labs & Teams: He contributes to labs like DHM (Deutsches Herzzentrum München), NARVIS Lab, and RobUSt (Robotics and Ultrasound), advancing interdisciplinary research in healthcare and robotics.
Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.
Cheng Han is a tenure-track Assistant Professor in the School of Science and Engineering at the University of Missouri -- Kansas City (UMKC), where he conducts research in adaptable and sustainable intelligence, focusing on efficient AI systems and parameter-efficient fine-tuning methods for large-scale models. Ph.D., Rochester Institute of Technology (RIT) M.S., Pennsylvania State University (PSU) B.S., Tianjin University (TJU) His research interests center on creating energy-wise AI systems that empower communities and address environmental and social challenges. He focuses on multimodal and visual prompt tuning, transfer learning, and robust AI. His work bridges theoretical innovation with real-world deployment, particularly in efficient adaptation of vision and language models. His recent publications span top venues like NeurIPS, ICCV, CVPR, ICLR, EMNLP, and IEEE TPAMI. The research trends highlight a strong focus on parameter efficiency , prompt engineering , model robustness , and multimodal understanding . He investigates when and why prompt tuning outperforms full fine-tuning and develops novel frameworks like E^2VPT and M^2PT for efficient adaptation. Cheng Han actively contributes to the academic community as a reviewer and committee member. Program Committee, AAAI (2023–present) Program Committee, SIAM SDM (2024) Reviewer for NeurIPS, ICLR, CVPR, ICML, ICCV, TPAMI, TMLR, and others He advises Ph.D. students and teaches courses such as Deep Learning (COMP-SCI 5567). He has given invited talks at ICLR, ICCV, and seminars at NSF and Naval Research Laboratory. His research is supported by academic collaborations and likely grant funding, given his active publication and service profile. He leads a research group focused on sustainable and efficient AI, with code available on GitHub.
David Daney is a Senior Researcher (Directeur de recherche) at Inria and HDR-qualified academic, currently serving as Head of Science for the Inria Center at the University of Bordeaux since July 2024. He is the team leader of the Auctus research group, focusing on robotics, cobotics, and human-robot interaction. He is affiliated with Inria and the École Nationale Supérieure de Cognitique (ENSC) at the University of Bordeaux, within the College of Engineering and the Department of Robotics. His research interests include Robotics, Cobotics, Human-Robot Interaction, Human Posture Analysis, Cable-driven Robots, Parameters Identification, Calibration, Interval Analysis, and Haptic Guidance. His work bridges theoretical robotics with industrial applications, particularly in aerospace, automotive, and sustainable agriculture. He has led and participated in numerous industrial collaborations with Airbus, Stellantis, Solvay, AKKA, and Farm3. His recent publications (2023–2025) demonstrate a strong focus on human-robot physical interaction, including real-time capacity estimation (Pycapacity), haptic guidance, model predictive control for dynamic environments, and musculoskeletal modeling for collaborative robotics. These works appear in top-tier journals such as IEEE Transactions on Robotics, Journal of Biomechanical Engineering, and Robotics and Autonomous Systems. HDR (Habilitation à Diriger des Recherches) Principal Investigator of ANR Pacbot Head of Science for Inria Center at University of Bordeaux Erdös number = 3 David Daney supervises multiple PhD students, including Alicia Barsacq, Ahmed-Manaf Dahmani, and Alexis Boulay. He has been principal investigator in several research projects such as LiChIE and ANR Pacbot, focusing on satellite production and human-robot collaboration. He also leads the SHAARE associate team with KAIST’s IRiS lab, advancing shared haptic control. His team develops tools for teleoperation, ergonomic analysis, and robot calibration, with applications in industrial and assistive robotics. He leads the Auctus team at Inria, which develops control and analysis techniques for human-robot physical interaction. The team collaborates with KAIST (SHAARE), ONERA, Pprime Institute, and industrial partners. The MOVER project studies human motor variability for ergonomics, and the Farm3 collaboration explores teleoperated vertical farming robotics.
Daniel Ventus is a Project Leader at Åbo Akademi University's Faculty of Education and Welfare Studies, specializing in interdisciplinary research across Psychology, Sexual Health, and Educational Technology. He leads the Experience Lab Health-related solutions and contributes to EU-funded projects like INTAKT and APOLLO2028 , focusing on academic procrastination interventions and healthcare worker resilience. Key research areas: Premature Ejaculation, High-Intensity Interval Training, Resilience Processes, Educational Robotics, and Mental Health interventions Recipient of the 2024 Best Poster Presentation Award at SWESrii for internet interventions His recent publications (2024-2025) examine: HIIT's impact on ejaculation control Real-time resilience assessment tools LLM-powered language learning systems for vulnerable children Physiological and psychological factors in sexual dysfunction As a peer reviewer for journals including Andrology and Scientific Reports , he contributes to multiple disciplines. Media visibility includes coverage in Finland-Swedish educational initiatives and sexual health research outreach (2018-2025).
Farshad Arvin is a Professor of Robotics in the Department of Computer Science at Durham University. Prior to this, he held academic positions at The University of Manchester (2018-2022) and worked as a Research Assistant at the University of Lincoln (2012-2015). He holds a BSc in Computer Engineering (2004), an MSc in Computer Systems Engineering (2010), and a PhD in Computer Science (2015). His research focuses on Swarm Robotics , Bio-inspired Swarms , and Autonomous Multi-agent Systems . He pioneered the Swarm & Computation Intelligence Laboratory (SwaCIL) at Durham, leading projects like H2020-FET RoboRoyale (€3.27M), Horizon Europe Sensorbees (€3.2M), and BioDiMoBot (€8M), with total funding exceeding £4M. Recent publications highlight advancements in swarm trajectory optimization (T-STAR), collision-free multi-robot coordination, and bio-hybrid environmental monitoring. His work integrates bio-inspired algorithms with practical applications in autonomous vehicles, aerial drones, and hazardous environments. Scientific Awards: Marie Skłodowska-Curie fellowship Notable Projects: EU H2020-FET RoboRoyale (2021-2026) Horizon Europe Sensorbees (2024-2029) Horizon Europe BioDiMoBot (2025-2030) H2020-FET Robocoenosis (2020-2025) Supervision: Mentors 8 postgraduate students at Durham, including Hanadi Alhamdan, Hang Wang, and Honghao Pan.
Mireille E. Broucke is a Professor in the Department of Electrical and Computer Engineering at the University of Toronto, where she is a member of the Systems Control Group within the Faculty of Applied Science and Engineering. She teaches various undergraduate and graduate courses including Adaptive Control and Reinforcement Learning, Robot Modeling and Control, and Introduction to Nonlinear Systems, demonstrating her commitment to education in control systems engineering. Professor Broucke's research focuses on mathematical system theory with particular emphasis on Systems Neuroscience, Reach Control Problems, and Patterned Linear Systems. Her work bridges theoretical control theory with applications in neuroscience and robotics. She has developed theoretical frameworks for understanding neural adaptation through control theory principles and has applied reach control theory to robotics problems including motion control of quadrocopters. Her research demonstrates how control theory can provide insights into biological systems while also advancing engineering applications. Her recent publications show a clear trend toward applying control theory to neuroscience, particularly in understanding adaptive internal models in the brain. The publications span from theoretical reach control problems on simplices and polytopes to practical applications in robotics and neural systems. Her work increasingly focuses on the intersection of control theory and neuroscience, examining how the brain implements adaptive control mechanisms for motor functions. This represents a significant shift from her earlier work which was more focused on pure control theory problems. Professor Broucke has advised several PhD students including Fatima Ghadieh, Erick Mejia Uzeda, and Mohamed Hafez. Her research has been supported by various grants that enable her work in control theory and its applications to neuroscience and robotics. She maintains an active research program with numerous publications in top control theory journals including IEEE Transactions on Automatic Control, Automatica, and Systems and Control Letters.
Prof. Dr. Gökhan Kiper is a faculty member in the Department of Mechanical Engineering at Izmir Institute of Technology , Turkey. His research focuses on Mechanism Science , Machine Design , and Deployable Structures , with particular emphasis on Polyhedral Geometry applications. Teaches courses: ME332 (Mechanisms), ME402 (Machine Design), ME577 (Advanced Mechanism Design) Active in IFToMM (International Federation for the Promotion of Mechanism and Machine Science), including roles in the Technical Committee for Computational Kinematics and the Turkey Branch (MakTeD) Co-organized the IFToMM Summer School on Mechanism Design for Medical Applications (2018) Research interests span kinematic synthesis of mechanisms, deployable architectural structures, and medical robotics. Key projects include a rollable ramp for temporary use, finger exoskeletons for rehabilitation, and remote-center-of-motion manipulators for minimally invasive surgery. His work integrates theoretical analysis with practical prototyping, reflected in publications across robotics, structural mechanics, and geometric design. Affiliates with the Rasim Alizade Mechatronics Laboratory (RAML) and the IzTech Kinetic Designs in Architecture Group . Presented at international conferences like International Symposium of Mechanism and Machine Science (ISMMS-2017) in Baku, Azerbaijan, where he chaired sessions on mechanism kinematics.
Alexandre PARANT is a Researcher at the University of Reims Champagne-Ardenne, affiliated with the School of Engineering and Digital Tools. His work focuses on cyber-physical systems, digital twins, and industrial automation, with a strong emphasis on the IEC 61499 standard for control architecture development. Research Themes: Model-driven engineering for production systems Digital twin implementation IEC 61499 standard application Modular cyber-physical systems Article Trends: Alexandre's publications span model-based development, robotics synchronization, and PLC identification. His work bridges theoretical modeling with practical automation solutions, particularly in educational contexts and industrial manufacturing. Labs & Teams: LINEACT research team Collaboration with CESI Campus Reims
Mehrtash Harandi is an Associate Professor in the Department of Electrical and Computer Systems Engineering at Monash University. He joined Monash in 2018 after five years at Canberra Research Laboratory-NICTA working with Prof. Richard Hartley and Prof. Fatih Porikli, and earlier at Queensland Research Laboratory-NICTA with Prof. Brian Lovell. His research focuses on machine learning, computer vision, and geometric learning with applications in medical imaging and diffusion models. Recent Research Trends (2025): 3D Gaussian splatting compression, diffusion transformers for visual correspondence, hyperbolic geometry in hierarchical structures, and robust learning from noisy labels. Scientific Awards: Outstanding Reviewer, CVPR'21 Advising Highlights: Mentored students contributing to papers at ICCV'24, CVPR'25, ICLR'25, and Nature Machine Intelligence. Labs & Teams: Collaborates with Data61-CSIRO, ARC, and US Air Force Research Laboratory.
