Marko Tanasković is a researcher at Singidunum University's Faculty of Informatics and Computing, specializing in control systems, robotics, and electrical engineering. He holds a PhD from ETH Zurich (2015) in Information Technology and Electrical Engineering, following degrees from University of Belgrade (BEng, 2009) and ETH Zurich (MEng, 2011). His research focuses on adaptive control systems, machine learning applications in engineering, and sensorless motor control. Key contributions include: Development of predictive algorithms for traffic systems and industrial automation Innovations in rotor orientation determination for PMSM motors Integration of AI in fraud detection and building climate control Recent work includes a 2024 study on wearable health monitoring devices and a 2022 paper on drone forensics. He has authored/co-authored over 15 peer-reviewed articles and holds patents in motor control technologies. Current affiliations include Singidunum University's Department of Electrical Engineering and Collaboration with ETH Zurich alumni networks. Active in international conferences such as Sinteza and IEEE events.
Kursat Kara is an Assistant Professor in the Department of Mechanical and Aerospace Engineering at Oklahoma State University (OSU), leading the Kara Aerodynamics Research Laboratory. He holds a Ph.D. in Aerospace Engineering from Old Dominion University (2008), and has held academic positions including Assistant Professor at Khalifa University (2010–2018), where he received the President’s Faculty Excellence Award for Teaching (2015). His research focuses on fluid dynamics, computational aerodynamics, hypersonic flows, quantum computing, and flow separation control using techniques like CFD and miniPIV. He has advised numerous graduate and undergraduate students, and collaborates on projects such as hypersonic boundary-layer stability, quantum computing for fluid dynamics, and urban wind field modeling for UAS navigation. Dr. Kara’s expertise spans experimental and numerical fluid dynamics, including work on sweeping jet actuators, boundary-layer transition, and aerodynamic design optimization. He is a member of AIAA (Senior), APS, and ASME, and has contributed to facilities like the $3.5M Khalifa University Low-Speed Wind Tunnel. His teaching includes courses on computational fluid dynamics, quantum computing, and unsteady aerodynamics. Recent research highlights include applications of machine learning in wind field prediction and interdisciplinary projects like interface learning for multiphysics systems. Scientific achievements include publications on hypersonic flow stabilization, quantum solvers for Burgers’ equation, and reduced-order models for urban wind simulation. His lab engages students from high school to PhD levels, emphasizing project-based learning and computational tools. Key collaborations involve NASA, the DOD, and industry partners like Sikorsky Aircraft Corp.
Nils-Christian Detering is an Associate Professor in the Department of Statistics & Applied Probability at the University of California, Santa Barbara (UCSB). He also serves as the Undergraduate Diversity, Equity, and Inclusion Officer. His research focuses on financial mathematics, probability theory, and their applications in systemic risk analysis, energy markets, and machine learning. Detering has contributed to understanding default contagion in financial systems, stochastic processes in energy derivatives, and neural network applications in functional data analysis. Research Interests : Financial systemic risk: Analyzing default contagion using random graphs and systemic stability metrics Infinite-dimensional stochastic analysis: Modeling energy markets via stochastic PDEs and forward curves Machine learning: Developing neural network frameworks for functional spaces and financial applications Teaching includes courses on stochastic processes, mathematical finance, and probability theory at both undergraduate and graduate levels. His work has been recognized with awards such as the Best Paper Award at the ACM International Conference on AI in Finance (2023). Key Publications address topics like reinforcement learning in banking networks, neural network calibration of energy curves, and integrated fire sales models. His research bridges theoretical probability with applied financial engineering challenges.
Dr. Ian Walker is a Professor in the Department of Geography at the University of California, Santa Barbara (UCSB). He specializes in physical geography and geomorphology, with expertise in sediment transport, coastal and aeolian processes, environmental fluid dynamics, and dune ecosystem restoration. He holds a B.Sc. in Geography and Environmental Science from the University of Toronto and a Ph.D. in Geography from the University of Guelph, Canada. Prior to UCSB, he served as faculty at Arizona State University and the University of Victoria (Canada). His research focuses on coastal and desert environments, employing field and lab methods such as terrestrial laser scanning (TLS), unmanned aerial systems (UAS), wind tunnel simulations, and computational fluid dynamics (CFD). Key projects include dust emissions mitigation at Oceano Dunes (collaborating with California Department of Parks) and invasive plant removal studies in Humboldt Bay. He has secured over $6M in research funding and advised over 40 students/post-docs. Education: B.Sc. (University of Toronto), Ph.D. (University of Guelph) Editorial Roles: Earth Surface Processes & Landforms , Annals of the American Association of Geographers , Journal of Coastal Research Professional Affiliations: Member of NASEM Scientific Advisory Panel for Owens Valley Dust Studies His articles emphasize coastal morphodynamics, restoration strategies, and climate change impacts. Notable work includes comparing UAS/TLS methods for geomorphic change detection and modeling airflow over dunes using CFD. He teaches courses on physical geography, geomorphology, and remote sensing. Grants and partnerships include collaborations with US Fish & Wildlife Service, NOAA, and state agencies. His lab focuses on integrating geospatial technologies with field experiments to address coastal sustainability challenges.
Dr. Rachel Parkinson is a Research Fellow at Wolfson College, University of Oxford, and holds a Lecturer position in Biology at Keble College. She is also an Eric & Wendy Schmidt AI in Science Postdoctoral Fellow. Her research focuses on insect sensory processing, particularly how pollinators like bees perceive environmental stressors such as pesticides. She develops AI-driven tools to diagnose sublethal toxicity and leads projects using large language models for systematic reviews of pesticide risks. Her work aims to assess environmental threats to pollinators and devise mitigation strategies. Her research interests include neuroethology, pesticide impacts on insect behavior, and AI applications in ecological research. She collaborates with the Bee Lab to advance understanding of pollinator health and ecological resilience. Her interdisciplinary approach bridges biology, neuroscience, and computational methods to address global environmental challenges. Awards: Eric & Wendy Schmidt AI in Science Postdoctoral Fellow Labs/Teams: Bee Lab, University of Oxford
Timo Sprekeler is an Assistant Professor in the Department of Mathematics at Texas A&M University's College of Arts & Sciences. He joined Texas A&M in 2024 after serving as a Peng Tsu Ann Assistant Professor at the National University of Singapore (2021-2024). Sprekeler completed his Ph.D. in Mathematics at the University of Oxford (2017-2021) following a MASt from the University of Cambridge and BSc from TU Dortmund University. His research specializes in numerical multiscale methods, homogenization theory, and finite element techniques for partial differential equations. Recent publications focus on developing computational frameworks for elliptic equations and optimization problems, with applications to materials science and control systems. Sprekeler maintains an active research group and teaches graduate-level courses in computational mathematics. His office is located in Blocker 608L, and he can be contacted via email.
Kangkang Yin is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on computer animation, computer graphics, humanoid robotics, machine learning, and multimedia analysis. He teaches courses such as Computer Animation and Scientific Computing, and holds a PhD from the University of British Columbia (2007), MSc from Zhejiang University (2000), and BSc from Zhejiang University (1997). His work bridges robotics and animation through projects like physics-based character controllers, motion diffusion models, and robotic manipulation. Key contributions include the SIMBICON biped locomotion framework and research into emotion-driven dance animation. Recent efforts emphasize reinforcement learning applications in motion synthesis and robust visual navigation for unmanned ground vehicles. Yin's publications span over two decades, addressing challenges in motion control, physics-based simulation, and machine learning applications. His lab contributes to both academic advancements and practical robotics solutions. Current research trends show strong emphasis on combining generative AI with traditional animation techniques, as seen in recent work on auto-regressive motion models (AAMDM) and physics-augmented reinforcement learning (PARC).
Lennie Moore serves as a faculty member in the Technology and Applied Composition (TAC) program at the San Francisco Conservatory of Music (SFCM), where he teaches Applied Lessons, Tools, Techniques and Analysis, and Orchestration for the Media Composer. With decades of experience as a composer, arranger, and orchestrator for video games, commercials, film, and television, Moore brings real-world industry expertise to his academic role. His teaching extends beyond SFCM, having developed and taught courses in Composing for Video Games at USC and UCLA Extension. Moore's research interests focus on the complex puzzle of video game scoring, emphasizing the deconstructionist approach required for interactive media. His work explores how to break down compositions into vertical layers and horizontal procedural components that can be implemented into music playback systems in real-time based on player choices. He specializes in open-world environments where music must smoothly transition through different game areas. His passion for building computers and video games outside of traditional music informs his innovative approach to media composition. His recent work includes the 2024 release of Outcast: A New Beginning , a sequel to his groundbreaking 1999 score for the original Outcast game, which was one of the first live orchestra and choir scores in gaming history. This new project involved approximately two hours and fifteen minutes of music with complex interactive elements, recorded during the pandemic with remote sessions. Best Soundtrack Album, G.A.N.G. Awards, 2011 Best Interactive Score, G.A.N.G. Awards, 2011 Best Audio — Other, G.A.N.G. Awards, 2008 Moore actively mentors students in the TAC program, with notable alumni including Kyle Randall (winner of the American Prize in composition), Shengyuan Li (Top 10 Finalist in the Berlin International Film Scoring Competition 2023), and Shawne Workman (now SFCM TAC faculty). His 2023 album Mentors pays tribute to his influences including Weather Report, Steps Ahead, Arif Mardin, Michael Gibbs, Don Grolnick, David Mash, Nick & Andy, Ken Kraintz, and Toshiko Akiyoshi. Moore emphasizes to students that 'Failure is an option' and encourages them to consider 'Who am I as an artist?' early in their development.
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.
Dr. Yu Xiang is an Assistant Professor of Computer Science at the University of Texas at Dallas (UT Dallas), leading the Intelligent Robotics and Vision Lab (IRVL) . He holds a Ph.D. in Electrical and Computer Engineering from the University of Michigan (2016) and prior roles include Senior Research Scientist at NVIDIA (2018–2021) and postdoctoral research at the University of Washington. Research Focus : His work centers on robotics and computer vision , particularly enabling robots to perceive 3D environments, plan actions, and interact autonomously in human-centric spaces. Key areas include unseen object segmentation, 6D pose estimation, manipulation trajectory optimization, and lifelong learning through robot-environment interaction. Key Contributions : Developed datasets like MultigripperGrasp and HO-Cap , and pioneered methods such as DeepIM for 6D pose estimation. His lab’s robot Ramp focuses on tasks like object manipulation and human-robot collaboration. Grants : NSF SMILE grant ($750K), DARPA Perceptually-enabled Task Guidance (co-PI), Sony Research Award (PI). Awards : NVIDIA Academic Grant (2024), Sony Research Award (2022), ECCV Best Paper (2018). Lab Activities : Engages in STEM outreach, including mentoring high school students in the 2024 Summer Bridge Camp. Current projects emphasize self-supervised learning and embodied AI for robotic systems.
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.
Professor Antony Darby holds a faculty position in the Department of Architecture & Civil Engineering at the University of Bath , where he serves as Director of Research and Knowledge Exchange . His research focuses on structural dynamics and concrete structure assessment, supported by facilities like the world-unique VSimulators (a collaboration with the University of Exeter). He works with interdisciplinary teams to address motion serviceability criteria and develops passive vibration control methods using impact dampers. Expert in structural strengthening with advanced composites (e.g., carbon fiber) Lead author of Concrete Society’s TR55 design guidance UK principal expert on Eurocode 2 committee for FRP guidelines His work aligns with UN Sustainable Development Goals (SDGs) related to sustainable cities and climate action. Recent publications span topics like structural stone scaling, offshore wind foundations, and occupant comfort in tall buildings. He actively supervises doctoral students and engages in industry knowledge exchange activities.
Prof. Dr. Katrijn KLINGELS is a Senior Lecturer at the Faculty of Rehabilitation Sciences , Hasselt University , Belgium. Her work centers on Motivational Control , Developmental Neuroscience , and Pediatric Rehabilitation , with a focus on neurodevelopmental disorders like Cerebral Palsy and Developmental Coordination Disorder . Supervision of 11 ongoing doctorates, including studies on balance control heterogeneity , intensive balance training , and somatosensory deficits in children with motor disorders. Leadership roles in the OMT Bachelor ReKi program and the Examination Board for Rehabilitation Sciences. Her research integrates brain imaging , neuromechanics , and functional assessments to understand motor impairments and design targeted therapies. Current projects include CO-OP interventions for autism , ICP-Move implementation for intellectual disabilities, and muscle fatigue studies in cerebral palsy. She teaches courses like Evidence-Based Clinical Reasoning and Advanced Pediatric Rehabilitation .
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.
