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Yalong YangView profile

Assistant Professor

• Immersive Analytics
• Virtual Reality
• Data Visualization
• +3 more

Yalong Yang is an Assistant Professor at the School of Interactive Computing at Georgia Institute of Technology. His research focuses on immersive analytics, virtual reality (VR), and augmented reality (AR) interfaces, with a particular emphasis on spatial interaction, hybrid user interfaces, and data visualization techniques. He explores how embodied interactions and immersive environments enhance understanding in domains like education, sports analytics, and collaborative decision-making. Key research themes include hybrid immersive systems (combining VR/AR with physical devices), asymmetric collaboration in mixed environments, and AI-driven tools for programming education. His work spans both theoretical frameworks and applied systems, such as SPHERE for scalable personalized feedback in coding classrooms and VizGroup for collaborative learning analytics. Yang’s recent publications highlight trends in spatial hybrid interfaces, navigation in immersive environments, and the integration of generative AI in educational technologies. His projects often involve evaluating interaction techniques (e.g., label placement in AR) and comparing performance across desktop and VR platforms. Notable contributions include the SportsXR initiative for immersive analytics in sports, and systems like CompositingVis for creating complex visualizations in 3D spaces. His work addresses challenges in situated analytics, wearable technologies for outdoor activities, and audience analysis in VR exhibitions.

• Artificial Intelligence
• Extended Reality
• Computer Science
• +3 more

Pasquale Cascarano is a fixed-term Assistant Professor at the University of Bologna , affiliated with the Department of the Arts . His research bridges computer science with creative industries (cinema, art, fashion) and biomedical applications, focusing on Artificial Intelligence and Extended Reality paradigms. He collaborates with national and international institutions on interdisciplinary projects. Institutional Affiliation: University of Bologna Academic Role: Assistant Professor (fixed-term) Research Interests center on integrating AI and XR into creative sectors and healthcare, with emphasis on: Generative AI for immersive environments Medical imaging and diagnostics Digital heritage preservation XR-based educational tools 3D visualization techniques Creative technology applications Recent Publications highlight trends in: Medical XR for surgical training AI-driven image restoration LLM integration with AR/VR Privacy in collaborative AR Fashion and cultural heritage digitization Deep learning for video enhancement

• Fluid Mechanics
• Aerodynamics
• Heat Transfer
• +3 more

Dr. Dana Dabiri is a Professor in the Department of Aeronautics and Astronautics and an Adjunct Professor in Mechanical Engineering at the University of Washington. He specializes in experimental fluid dynamics, developing advanced techniques like Digital Particle Image Velocimetry (DPIV) and Particle Tracking Velocimetry (PTV). His research focuses on fluid flow visualization, turbulence, aerodynamics, and heat transfer. Education: Ph.D., Aerospace Engineering, University of California, San Diego (1992) M.S., Mechanical Engineering, University of California, Berkeley B.S., Mechanical Engineering, University of California, San Diego (1985) Dr. Dabiri's work emphasizes innovative diagnostic methods for studying fluid dynamics, including DPIV, DPITV (temperature-velocity measurement), and 3D PTV. His contributions span aerodynamics, hydrodynamics, boundary layers, flow control, and cardiovascular flows. He co-authored the book Particle Tracking Velocimetry (2019), detailing advancements in flow measurement techniques. He has advised students like Lillie LaPlace, who received the Sigma Gamma Tau honor. His research has been supported by grants for projects such as film evaporation experiments on the ISS. He maintains active collaborations in fluid mechanics and experimental approaches to turbulence modeling.

Rachel ParkinsonView profile

Research Fellow

• Insect Neuroethology
• Environmental Toxicology
• AI in Biology
• +3 more

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

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).

Sohail K. MirzaView profile

Professor

• Spinal Biomechanics
• Orthopaedic Surgery
• Image-Guided Surgery
• +3 more

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.

Zhao NaView profile

Assistant Professor

• Artificial Intelligence
• Augmented Intelligence
• Data Science
• +7 more

Dr. Zhao Na is a tenure-track Assistant Professor at the Singapore University of Technology and Design (SUTD), affiliated with the Institute of Sustainable Technology and Design (ISTD). She holds a Ph.D. in Computer Science from the National University of Singapore (NUS), where her thesis on 3D point cloud semantics earned the IMDA Excellence Prize. Her research bridges computer vision and machine learning, focusing on scene understanding, data-efficient learning, and domain generalization. Education: Ph.D. in Computer Science (NUS, 2021); Prior roles include Research Fellow at NUS. Research interests emphasize 3D scene analysis, object detection, semantic segmentation, and robust learning under noisy or limited data. Her work addresses challenges in multi-modal learning, continual learning, and open-world scenarios. Recent projects include geometry-semantics synergy in neural fields and cross-modal augmentation for visual grounding. Publications span top-tier venues like CVPR, ECCV, and ICCV, with a focus on 3D vision and AI. Key contributions include the PCTeacher framework for semi-supervised segmentation and Static-Dynamic Co-Teaching for incremental learning. Scientific Awards: IMDA Excellence Prize (2021). Active grants include a DSO Research Grant (2023–2026) and A*STAR MTC Grant (2023–2026). She leads the SUTD-ZJU Thematic Grant on 3D scene understanding (2022–2024). Laboratory/Team: Research group at ISTD/SUTD focuses on advancing AI-driven 3D perception and scene understanding systems.

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.

Dong GongView profile

Senior Lecturer

• Computer Vision
• Machine Learning
• Deep Learning
• +3 more

Dr. Dong Gong is a Senior Lecturer and ARC DECRA Fellow (2023-2026) at the School of Computer Science and Engineering (CSE), UNSW. He holds an adjunct position at the Australian Institute for Machine Learning (AIML), University of Adelaide. His research focuses on machine learning challenges in dynamic environments, including continual learning, foundation models, generative models, and applications in interdisciplinary areas like mining and agriculture. Research interests include learning with non-ideal supervision, foundation model adaptation, generative models, and interdisciplinary problems combining CV/ML with domain-specific applications. His work often addresses real-world scenarios such as mineral exploration and soil trait analysis using CV/ML technologies. Outstanding Reviewer: NeurIPS 2018 Outstanding Area Chair: ACM MM 2024 ARC DECRA Fellowship (2023-2026) Advising and grants: Actively supervises PhD/MPhil students in computer vision and ML. Collaborates with industry and government on research projects. Utilizes advanced infrastructure like UNSW's Katana supercomputing cluster and Gadi (NCI). Labs/Teams: Involved in interdisciplinary research groups at UNSW CSE and AIML, focusing on dynamic learning paradigms and real-world applications of AI.

Mehmet Uğur KAHRAMANView profile

Assistant Professor

• Interior Design Education
• Artificial Intelligence in Design
• Sustainable Materials
• +3 more

Mehmet Uğur KAHRAMAN is an Assistant Professor at the Department of Interior Architecture and Environmental Design at Antalya Bilim University, where he has served since 2017. Previously, he held a faculty position at Kayseri Nuh Naci Yazgan University (2015–2017). His academic journey includes a Doctorate from Hacettepe University (Interior Architecture and Environmental Design), a Master of Design (Interior Design) from Swinburne University of Technology (Australia), and a Bachelor’s degree from Hacettepe University’s Department of Interior Architecture and Environmental Design. Before academia, he worked as a construction site manager at KG Architecture in Istanbul, co-founded the food and beverage brand 'Shot&Bite' in Ankara, and later served as a designer at QUBİ Design Office. His research focuses on integrating artificial intelligence into design education, neurocognitive aspects of spatial design, sustainable materials, and pedagogical innovations in interior architecture education. He has authored over 20 peer-reviewed articles on topics ranging from AI-driven furniture design to multisensory hospitality spaces. His work bridges theoretical research with practical applications, such as developing curriculum models for design studios and exploring waste-to-art construction techniques. KAHRAMAN’s studies also address health impacts of building materials and the psychological dimensions of housing during crises like the COVID-19 pandemic. He maintains active research collaborations, particularly in Turkey and Australia, and has contributed to public infrastructure projects involving material conservation and adaptive reuse. His educational philosophy emphasizes student-centered learning, interdisciplinary approaches, and leveraging digital tools for contemporary design challenges.

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.

Kristen S. KurlandView profile

Professor

• Geographic Information Systems (GIS)
• Health Informatics
• Urban Design
• +8 more

Kristen S. Kurland is a University Professor at Carnegie Mellon University (CMU), holding appointments in the H. John Heinz III College of Information Systems and Public Policy, the School of Architecture, and a courtesy appointment in the Civil and Environmental Engineering Department. She also serves as an adjunct faculty member at the University of Pittsburgh School of Medicine. Her roles include chairing the Geographical and Geospatial Sciences Committee of The National Academies of Sciences, Engineering, and Medicine, and serving on the Board on Earth Sciences and Resources. Her teaching focuses on GIS, Building Information Modeling (BIM), CAD, and 3D visualization, with courses at Heinz College targeting executive physicians and healthcare management students. Research interests span health and urban systems, geospatial analytics, machine learning, and smart cities. She actively collaborates with healthcare, government, and industry partners globally. Notable awards include the 2020 Carlow University Women of Spirit Award, 2012 Esri Health Communications Award, and multiple teaching accolades. Her funded research includes projects on urban quality of life, 3D visualization, and pediatric healthcare access funded by The Heinz Endowments, Deloitte Foundation, and others. Kurland advises numerous PhD students across architecture, civil engineering, and public policy, and leads grants addressing spatial health equity, urban design, and geospatial innovation. She is co-author of bestselling GIS workbooks like GIS Tutorial for Health and a frequent keynote speaker on geospatial technologies.

James S. CottonView profile

Professor

• Thermo-Fluid Sciences
• Sustainable Energy Systems
• Heat Transfer
• +4 more

Professor James S. Cotton is a faculty member in the Department of Mechanical Engineering at McMaster University , specializing in Thermo-Fluid Sciences with a focus on energy sustainability and thermal management. Current research explores thermal energy harvesting , non-thermal plasma flue gas cleaning , and smart electrohydrodynamic heat exchangers . Active in community energy planning as a member of the Burlington Climate Action Plan (2019-2021) and Green Venture board (2016-2021). Research spans both fundamental and applied domains, including two-phase flow , electrohydrodynamic heat transfer modulation , and flow accelerated corrosion analysis. His work integrates modeling and experimental validation for real-world thermal systems. Scientific Contributions: Developed two novel patents for advanced thermal management and soot removal systems during his industrial career at Dana Corp. (until 2007). Current projects involve community energy corridors and next-generation sustainable energy solutions , including the 2025 Hamilton Energy Harvesting Study . Active mentor in graduate education, teaching courses like MECH ENG 4O04: Sustainable Energy Systems and MECH ENG 708: Two Phase Flow and Heat Transfer .

Hamidreza MahyarView profile

Assistant Professor

• Machine Learning
• Network Science
• Natural Language Processing
• +3 more

Hamidreza Mahyar is an Assistant Professor at the Faculty of Engineering , McMaster University , and an Associate Member of the Computing and Software department. His academic journey includes postdoctoral work at Boston University and TU Wien , and a Ph.D. in Computer Science from Sharif University of Technology . Research Focus: Mahyar's work bridges machine learning and network science , emphasizing graph neural networks for applications in social networks , recommendation systems , drug discovery , and generative AI . His research spans industrial AI (Industry 4.0 projects at Infineon Technologies), biomedical engineering (organoid morphology analysis), and semiconductor manufacturing (wafermap modeling). Scientific Recognition: McMaster Teaching Merit Award (2022) Vector Scholarship in AI (2023) NSERC USRA Award (2022) Google Cloud Platform for Research Award (2018) Best Paper Selection, Complex Networks (2018) Academic Leadership: He mentors PhD students (Taraneh Ghandi) and MSc students (Reza Namazi, Mohammad Khodadad, Ali Shiraei), while leading AI initiatives at Mind Lab 56 and BrainMaven . Former mentees include industry leaders at Google, Accenture, and ETH Zurich.