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Xiaoqiang WangView profile

Professor

• Numerical Analysis
• Applied Partial Differential Equations
• Mathematical Biology
• +6 more

Xiaoqiang Wang is a Professor in the Department of Scientific Computing at Florida State University (FSU). His research focuses on numerical analysis, applied partial differential equations, mathematical biology, image processing, and scientific computing. He holds a Ph.D. from Pennsylvania State University (2005). His work emphasizes phase-field modeling for elastic bending energy, biological microstructures, and computational methods for complex systems. Notable contributions include advancements in centroidal Voronoi tessellation algorithms for image segmentation and high-performance computing techniques for scientific visualization. Recent publications highlight innovations in topology-preserving phase-field models, neural network-based energy minimization, and stochastic resource competition models. His research bridges theoretical mathematics with practical applications in biophysics, materials science, and biomedical engineering. Wang collaborates actively with interdisciplinary teams, contributing to FSU's computational science initiatives. His lab focuses on developing novel numerical methods and simulations for biological and physical systems, reflecting a commitment to both foundational and applied research.

Kangkang YinView profile

Associate Professor

• Computer Animation
• Computer Graphics
• Humanoid Robotics
• +3 more

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).

• Artificial Intelligence
• Machine Learning
• Activity Recognition
• +12 more

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.

• Data Science
• Concept-Drift Management
• Database Systems
• +12 more

Tania Cerquitelli is a Full Professor in the Department of Control and Computer Science (DAUIN) at Politecnico di Torino, where she leads research in data science, concept-drift management, and inclusive AI technologies. She is a member of SmartData@PoliTO, the GEDI Observatory for Gender Equality, and serves in leadership roles related to social affairs and community policies at the university level. She also acts as a scientific advisor for the partnership with Accenture. Her research interests span Data Science , Concept-Drift Management , Database Systems , Conversational Data Science , and Industry 4.0 . She applies AI and machine learning to industrial, societal, and ethical challenges, particularly in promoting inclusive communication and gender equality in research. The most recent publications highlight her work in explainable AI, concept drift detection, multimodal diagnostics, and AI for social good. Her research integrates machine learning, natural language processing, and computer vision to address real-world problems in manufacturing, healthcare, agriculture, and education. She is an Associate Editor for several prestigious journals including Expert Systems with Applications , Computer Networks , Future Generation Computer Systems , and Knowledge and Information Systems . She has served on the program committees of major conferences such as ECML PKDD, EDBT/ICDT, and ACM KDD, and has been a reviewer and selection committee member for ETH Zurich and EMPA. She actively supervises PhD students and teaches a wide range of courses including Data Science and Database Technologies, Business Intelligence for Big Data, and Gender and Diversity in Research. She is involved in multiple national and international research projects such as E-MIMIC, WEBFARE, and EnABLES, focusing on inclusive AI, smart data, and industrial applications. Her lab affiliations include the DBDM - Database and Data Mining Group (DAUIN) and the Interdepartmental Center SmartData@PoliTO - Big Data and Data Science Laboratory , where she contributes to advancing data science methodologies and their societal impact.

Joseph MeadowsView profile

Associate Professor

• acoustics
• clean energy
• combustion and fire
• +3 more

Joseph Meadows is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering. His research focuses on combustion, heat transfer, and computational fluid dynamics, with applications in rotating detonation engines and thermoacoustic instability mitigation. He leads the Advanced Propulsion and Power Laboratory. Education: Ph.D., M.S., and B.S. in Mechanical Engineering from the University of Alabama and University of Memphis. Professional History: Associate Professor since 2025, Assistant Professor (2017–2025), and Combustion Design Engineer at Siemens Energy Inc. (2014–2017). His work bridges experimental and computational domains, emphasizing dynamic injector response , fuel inhomogeneity , and mesoscale wood combustion modeling . Recent publications highlight 2D/3D CFD comparisons and acoustic diagnostics in high-temperature environments. While no explicit awards are listed, his research impacts gas turbine design and clean energy systems.

Diego PatiñoView profile

Assistant Professor

• Machine Learning
• Geometric Computer Vision
• 3D Reconstruction
• +7 more

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.

• Databases
• Spatial Data Management
• Geospatial Systems
• +3 more

Mohamed Sarwat is an Associate Professor at Arizona State University specializing in databases , spatial data management , and recommender systems . His research focuses on GeoSpark —a cluster computing framework for spatial data—and its extensions like GeoSparkViz for visualization and GeoSparkSim for traffic simulation. Key Contributions: LARS* (Location-Aware Recommender System), Horton* (Graph Reachability), Sindbad (GeoSocial Platform), and Riso-Tree (Graph Database Indexing) Research Themes: Integration of spatial/temporal data with machine learning, efficient indexing for big geospatial datasets, and scalable frameworks for mobility data science His work spans collaborations with 23+ co-authors across institutions like University of Minnesota, University of Melbourne, and University of Salzburg. Current projects emphasize GeoTorchAI —a spatiotemporal deep learning system—and mobility data science infrastructure.

Hsiao-Dong ChiangView profile

Professor

• Nonlinear System Theory
• Power System Stability and Control
• Optimal Power Flow
• +12 more

Hsiao-Dong Chiang is a Professor in the School of Electrical and Computer Engineering at Cornell University. He holds a Ph.D. in Electrical Engineering from the University of California, Berkeley, and has made significant contributions to nonlinear system theory and power system stability. His research spans theoretical development and practical applications in electric power systems, nonlinear optimization, and machine learning. B.S., Electrical Engineering, National Taiwan University, 1979 M.S., Electrical Engineering, National Taiwan University, 1981 Ph.D., Electrical Engineering, University of California, Berkeley, 1986 Chiang's research interests focus on nonlinear system theory , power system stability and control , nonlinear optimization , and their applications to modern power grids with high penetration of inverter-based resources. He is renowned for developing the BCU method and TRUST-TECH methodology , which have enabled fast direct stability assessment and global optimization in complex systems. His work bridges fundamental theory with industrial deployment through his companies, Bigwood Systems, Inc. and Global Optimal Technology, Inc. His recent publications (2024–2025) reflect a strong trend toward integrating machine learning and deep neural networks with power system analysis , particularly in state estimation, optimal power flow, and voltage control. There is a clear emphasis on handling uncertainty, non-convexity, and multi-scale dynamics in active distribution networks and integrated energy systems . His work increasingly focuses on resilience , real-time control , and user-centered methodologies for modern grid operations. Chiang has received numerous scientific honors, including: IEEE Fellow (1997) United States Presidential Young Investigator Award (1989) Multiple DOE Grid Optimization Challenge Awards (2020–2023) Best Paper Awards from IEEE Transactions and Conferences Outstanding Education Award, Cornell University (1990) He has successfully managed over 100 research projects and holds 28 U.S. and international patents. As the founder of Bigwood Systems, Inc., he has commercialized advanced software for utility companies across the U.S. and Japan. His team has published over 480 refereed papers and received more than 17,500 citations. He advises a large research group and leads innovations in computational methods for energy systems. His lab is actively involved in developing next-generation tools for grid security, optimization, and machine learning integration.

Hannu HyyppäView profile

Researcher

• Laser Scanning
• 3D Modeling
• Geoinformatics
• +5 more

Hannu Hyyppä is a Research Director and Project Employee at Aalto University's Department of Built Environment, affiliated with the MeMo research group. He leads the Research Institute of Measuring and Modelling for the Built Environment, focusing on advanced laser scanning, 3D modeling, and geoinformatics. His work spans interdisciplinary collaborations across engineering, geography, and arts, with a strong emphasis on applications in cultural heritage preservation, urban planning, and environmental monitoring. Education: Doctoral degree (D.Sc.) in Engineering and Technology, Helsinki University of Technology (2000) Licentiate degree in Engineering and Technology, Helsinki University of Technology (1989) Master's degree in Engineering and Technology, Helsinki University of Technology (1986) Research Interests: Laser scanning technologies, point cloud utilization in forestry and urban mapping, virtual reality for cultural heritage, and sustainable infrastructure modeling. His expertise includes photogrammetry, geographic information systems (GIS), and decision support systems for environmental management. Recent Contributions: Over 550 publications and 30+ active projects, including the Centre of Excellence in Laser Scanning Research (2014-2019) and the Pointcloud project (2015-2021). His work advances applications in autonomous road inspection, 3D cultural reconstructions, and smart city technologies. Awards: Recipient of the 2019 Kansallinen avoimen tieteen palkinto for innovative open science contributions. Grants & Leadership: Principal Investigator for projects like DICA (Digital Cultural Heritage) and ToToRo (Automatic Road Inspection). Active in organizing workshops and international conferences on 3D technologies and laser scanning. Labs/Teams: Oversees the MeMo group and collaborates with national organizations like the Finnish Geospatial Research Institute. Develops tools for real-time 3D mapping and virtual environments.

Melanie KrügerView profile

Professor

• Embodiment Research
• Cognitive Aging
• Motor Decision Making
• +7 more

Prof. Dr. Melanie Krüger is a faculty member at Leibniz University Hannover , where she serves as Head of the Research Unit Sports and Cognition within the Institute of Sports Science. Her academic career spans institutions including the Technical University of Munich, University of Tasmania, and Ludwig Maximilian University. She leads research initiatives in embodiment, cognitive aging, motor decision-making, and the impact of physical activity on neurocognitive and motor functioning. Current Position: Professor, Institute of Sports Science, Leibniz University Hannover Prior Appointments: Post-doctoral roles at Technical University of Munich, University of Tasmania, and Pennsylvania State University Education: PhD in Systemic Neurosciences, Ludwig Maximilian University; Diploma in Sports Science, University of Leipzig Krüger's research focuses on: Embodiment Research: Systemic interactions between cognitive and motor development Cognitive Aging: Age-related changes in motor decision-making and coordination Human Locomotion: Collision avoidance, mixed reality interactions, and real-world movement studies Open Science: Advocacy for research data management in sports science Her recent publications explore mixed reality applications ( 2024 ), stress effects on motor function ( 2023 ), and methodological innovations in locomotion studies ( 2021-2025 ). Current projects include Moving Forward (2021-2022) and two Innovation Plus initiatives (2022-2024) developing experimental frameworks for human movement analysis. Krüger actively participates in academic governance: Spokesperson, German Society of Sport Science's Ad Hoc Committee on Research Data Management (since 2022) Board member, DVS Section 'Sports Motor Skills' (since 2022) Doctoral Committee, Faculty of Humanities Examination Board, MSc Sustainable Health Promotion in Sports Science

Maria Cristina RamosView profile

Assistant Professor

• Computational Social Science
• Network Analysis
• Social Psychology
• +7 more

Dr. Maria Cristina Ramos is an Assistant Professor at Florida State University, specializing in Computational Social Science. She is affiliated with the Interdisciplinary Social Science program, where her teaching and research focus on applying network science and computational methods to address complex social issues such as health inequalities, reproducibility in science, and social-ecological systems. Ph.D. in Sociology, Duke University M.A. in Sociology, Duke University B.A. in Organizational Psychology, Pontificia Universidad Católica del Ecuador Her research integrates Social Network Analysis, Computational Social Science, and interdisciplinary approaches to study identity structures, sustainable fisheries, and organizational collaboration. She emphasizes reproducibility in social science research and uses data visualization to enhance understanding of societal challenges. Dr. Ramos' recent publications highlight trends in network science applications to moral psychology, environmental policy, and scientific methodology. Her work bridges quantitative and qualitative techniques, contributing to fields like Social Psychology and Marine Policy. Contact: mcr22c@fsu.edu | mc@mariacramos.com | Personal Website

• Artificial Intelligence
• Intelligent Agents
• Software Engineering
• +4 more

Claudio Ulises Cortes Garcia is a Professor at the Department of Computer Science , Technical University of Catalonia, and leads the IDEAI-UPC (Intelligent Data Science and Artificial Intelligence Research Group) and KEMLG (Knowledge Engineering and Machine Learning Group). He is affiliated with the Barcelona Supercomputing Center (BSC-CNS) and the Barcelona School of Informatics (FIB). With a Doctor en Informática (PhD in Computer Science) and Ingeniero Industrial y de Sistemas (Industrial and Systems Engineering) degrees, his work spans Artificial Intelligence , Intelligent Agents , and Assistive Technology . His research integrates European Programs and Internet with applications in Second Life and Software . His recent publications focus on Post-COVID cognitive effects , AI ethics , and agent-based modeling for urban water management. He received the Doctor Honoris Causa from Universitat de Girona in 2024 and has collaborated on projects like DIGITAfrica and HUB D'INNOVACIÓ PEDIÀTRICA . His work bridges Neuroscience , Environmental Modeling , and Digital Humanities , with over 650 activities recorded in his academic career. ORCID : 0000-0003-0192-3096 WoS Researcher ID : B-7284-2009 Scopus Author ID : 7004065770

Benjamin EvansView profile

Assistant Professor

• Computational Neuroscience
• Spiking Neural Networks
• Convolutional Neural Networks
• +7 more

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.

Clark N. TaylorView profile

Associate Professor

• Computer Engineering
• Navigation Systems
• Sensor Fusion
• +12 more

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.

Wei-Chen HungView profile

Professor

• Problem Solving
• Human-Computer Interaction
• Performance Support Systems
• +7 more

Wei-Chen Hung is a Professor and Department Chair in the Department of Educational Technology, Research and Assessment at Northern Illinois University’s College of Education. He holds a Ph.D. in Instructional Systems Technology from Indiana University-Bloomington and is actively engaged in teaching, research, and leadership in educational technology. Ph.D., Instructional Systems Technology, Indiana University-Bloomington (with Minor in Information Science) His research focuses on problem solving, human-computer interaction (HCI), and performance support systems in educational contexts. He explores how technology can scaffold learning, enhance problem-solving skills, and support teachers in instructional design and classroom management. His work spans K–12, higher education, and professional training environments. The recent publications highlight a strong trend in developing and evaluating technology-enhanced learning environments, including intelligent tutoring systems, 3D virtual worlds, educational simulations, and performance support tools. These works span disciplines such as STEM education, nursing, and social sciences, reflecting an interdisciplinary approach to solving instructional challenges through technology. Wei-Chen Hung has secured significant research funding from agencies including the National Science Foundation, NASA, and the Illinois Department of Education. He has served as Principal Investigator and Co-Principal Investigator on multiple grants focused on educational simulations, research methods training, and assistive technologies. National Science Foundation: Web-based Interactive Landform Simulation Model (WILSIM), $236,041, 2013–2016 (Senior Faculty Expert) Elgin Community College: Information security awareness, $10,000, 2011–2012 (Principal Investigator) National Science Foundation: Intelligent tutoring system for research methods, $150,000, 2008–2012 (Co-Principal Investigator) NASA: MARS Valley Networks Project, $20,000, 2005–2007 (Faculty Expert) Illinois Department of Education: Assistive technology for blind and visually impaired children, $75,000, 2005–2007 (Faculty Expert) He has contributed to the development of software tools and systems such as the Cause and Consequence behavior management system and performance support systems for educators. His lab and research initiatives emphasize applied design, usability testing, and evaluation of educational technologies in real-world settings.