Dr. Maria Anna Polak is a Professor in the Department of Civil and Environmental Engineering at the University of Waterloo, cross-appointed in the Faculty of Engineering. Her research focuses on advanced structural analysis, composite materials, and innovative construction technologies. She specializes in concrete structures, fiber-reinforced polymers (FRP/GFRP), and nondestructive evaluation methods. Her work addresses challenges in structural safety, durability, and sustainable design through computational modeling and experimental validation. Research Interests: Finite Element Analysis of Reinforced Concrete Structures FRP/GFRP Reinforcement for Concrete Applications Punching Shear Behavior and Design 3D-Printed Construction Materials Nondestructive Testing (NDT) with LiDAR and Ultrasonics Time-Dependent Behavior of Polymers and Composites Her recent publications emphasize advancements in computational modeling (FEA) for complex structural systems, material characterization of novel composites, and optimization of concrete components under seismic and environmental loads. Notable trends include integration of emerging technologies like smartphone LiDAR for on-site assessment and development of predictive models for FRP-degraded systems. Grants & Collaborations: Her work has been supported by industry partnerships (e.g., Horizontal Directional Drilling research) and academic initiatives. She contributes to international standards through experimental validation of design methods for modern construction materials. Labs/Teams: Active in the University of Waterloo's Structural Engineering and Applied Mechanics research group, focusing on smart infrastructure and material innovation.
Dr. Zhenyu Zhang is a Lecturer in Surveying and Spatial Science at the School of Surveying and Built Environment , University of Southern Queensland (Springfield Campus). With over 20 years of tertiary teaching experience, he specializes in geomatic engineering, GIS programming, remote sensing, and machine learning applications for geospatial data analysis. His work focuses on LiDAR technologies (terrestrial and airborne) for environmental management, 3D modeling, and high-resolution DEM generation. Member of Surveying and Spatial Science Institute (SSSI), Australia Member of Modelling and Simulation Society of Australia and New Zealand Member of International Global Navigation Satellite Systems (IGNSS) His research integrates geomatics with environmental geoscience, emphasizing forest biomass estimation, carbon accounting, and BIM development using laser scanning. He teaches foundational and advanced courses in surveying, geodetics, GIS programming, and research projects at both undergraduate and postgraduate levels.
Xuhui Lee is the Sara Shallenberger Brown Professor of Climate Science at Yale University's School of the Environment. He maintains offices at Kroon Hall (195 Prospect Street) and laboratory facilities at the Class of 1954 Environmental Science Center (21 Sachem Street, Room 300) in New Haven, Connecticut. Professor Lee is an active researcher and educator specializing in the interactions between the terrestrial biosphere, atmosphere, and anthropogenic drivers, with particular expertise in boundary-layer meteorology and climate science. He is currently on leave for the Fall 2025 semester but continues to accept doctoral students. Professor Lee received his B.S.C. and M.S.C. from Nanjing Institute of Meteorology in China, followed by a Ph.D. from the University of British Columbia. His academic journey has positioned him as a leading expert in climate science, particularly in the areas of land-atmosphere interactions and urban climate systems. Professor Lee's research focuses on boundary-layer meteorology, micrometeorological instrumentation, remote sensing, and carbon cycle science. His work examines biophysical effects of land use on the climate system, greenhouse gas fluxes in terrestrial environments (including forests, cropland, and lakes), isotopic tracers in carbon dioxide and water vapor cycling, and urban climate adaptation and mitigation strategies. His lab employs diverse methodologies including field observations (eddy covariance, optical isotope instruments, and greenhouse gas analyzers), mathematical models (land surface models, large-eddy simulation, WRF, and earth system models), and environmental remote sensing (satellites and drones). The Lee Lab investigates phenomena across multiple scales from micro (urban greenspaces) to global (land wet-bulb temperature, historical deforestation). Analysis of Professor Lee's recent publications reveals a strong focus on urban climate systems, greenhouse gas emissions, and land-atmosphere interactions. His 2024-2025 work demonstrates increasing application of advanced remote sensing technologies and machine learning approaches to climate problems, with significant attention to urban heat islands, methane and CO2 emissions monitoring, and the impacts of land use change on climate systems. His research shows a clear trajectory toward more sophisticated integration of observational data with modeling approaches to address critical climate challenges. Sara Shallenberger Brown Professor of Climate Science (named professorship) Professor Lee actively mentors doctoral students and has established the Lee Lab as a hub for climate research at Yale. His lab group conducts field observations, mathematical modeling, and remote sensing analysis to advance understanding of climate systems. The lab's research infrastructure supports investigations from micro-scale urban environments to global climate patterns, with particular emphasis on urban heat mitigation and greenhouse gas monitoring. The Lee Lab at Yale, located in Room 300 of the Class of 1954 Environmental Science Center, serves as the primary research facility for Professor Lee's team. The lab deploys an array of research methodologies including field observations with eddy covariance systems and optical isotope instruments, mathematical modeling using land surface models and earth system models, and environmental remote sensing with satellites and drones. The lab's research spans multiple spatial scales from micro (urban greenspaces) to global (land wet-bulb temperature patterns), addressing critical questions about climate change impacts and mitigation strategies.
Mila N. Koeva is a Vice Dean Research and senior Associate Professor at the University of Twente's Faculty of Geo-Information Science and Earth Observation (ITC), Department of Urban and Regional Planning and Geo-Information Management. Her research focuses on 3D modeling and Digital Twins for land management and urban planning, integrating geospatial technologies, UAV data, and AI/ML methods. PhD in architectural photogrammetry MSc in Engineering (Geodesy) Research Themes: Digital Twinning for urban ecosystems AI-driven cadastral boundary extraction 3D modeling with LiDAR and satellite data Global partnerships in Rwanda, Kenya, and Ethiopia Interoperability standards for local digital twins Scientific Contributions: Geospatial World Innovation Award 2021 Copernicus Masters Competition (3rd place 2016) Editorial roles in Photogrammetric Records and MDPI journals Keynote speaker at 3D GeoInfo, GI Forum, and FIG events Her educational impact includes developing courses, lecturing, and supervising students whose work has received top awards in The Netherlands and international competitions.
David Wanik is an Assistant Professor in the Department of Operations and Information Management and Associated Faculty in the Department of Civil and Environmental Engineering at the University of Connecticut. He serves as Academic Director for Business Data Analytics at the Stamford campus and conducts research in the Eversource Energy Center, focusing on data science, natural hazards, remote sensing, and IoT applications in utility systems. PhD, MS, and BS in Environmental Engineering from University of Connecticut His research bridges natural hazard prediction, power grid resilience, and environmental data science. Key themes include: Machine learning for power outage prediction Climate change impact on energy demand Remote sensing for population and environmental monitoring IoT-enabled infrastructure hardening Recent publications emphasize deep learning for nighttime light imagery analysis, hybrid physics-data-driven models for grid resilience, and climate-integrated demand forecasting. His work integrates satellite data, LiDAR, and utility infrastructure records for predictive analytics. Teaching includes courses in business analytics, Python-based data science, and deep learning for the MS Business Analytics and Project Management program.
Luis Sentis is a Professor in the Department of Aerospace Engineering and Engineering Mechanics at The University of Texas at Austin and holds the Frank and Kay Reese Endowed Professorship in Engineering . He leads the Human Centered Robotics Laboratory, focusing on control systems, human-robot interaction, and exoskeleton robotics. His affiliations include UT Austin's Good Systems initiative and Apptronik Systems as an innovation advisor. Ph.D. , Electrical Engineering, Stanford University B.S. , Telecommunications and Electronics Engineering, Polytechnic University of Catalonia His research spans humanoid robotics , agile manipulation , autonomous systems , and human-robot teaming . Recent work emphasizes FAIR datasets , EEG monitoring , and collision detection for legged robots, with applications in industrial automation and ethical AI. Scientific awards include the NASA Elite Team Award and La Caixa Foundation Fellowship . Funding sources include DARPA , NSF , NASA , and ONR .
Dr. Philippe Dixon is an Assistant Professor in the Department of Kinesiology & Physical Education at McGill University, with a division in Biomechanics and Neuroscience. He holds adjunct professor roles at the University of Montreal (School of Kinesiology and Physical Activity Sciences) and the University of Laval (Department of Kinesiology). His research focuses on human movement biomechanics using motion capture systems and wearable sensors, combined with machine learning for health and athletic performance optimization. He has expertise in gait analysis, muscle coactivation patterns in cerebral palsy, and predictive modeling of physiological states. Dr. Dixon earned a Post-doctoral fellowship in Public Health at Harvard University, a PhD in Engineering Science from the University of Oxford, and dual degrees in Biomechanics and Physics from McGill University. His education includes a Bachelor of Education in Mathematics and Physics (McGill), a Master of Science in Biomechanics (McGill), and a PhD in Engineering Science (Oxford). He has received grants such as the NSERC Discovery Grant (2022–2027) and the FRQSC AUDACE Grant (2022). His work emphasizes wearable sensor integration, with contributions to datasets like NACOB and tools like OpenOFM. He currently supervises Master’s and PhD students in biomechanics and machine learning applications. Key research themes include gait adaptations on uneven surfaces, machine learning for cough detection via smart garments, and musculoskeletal coordination in clinical populations. His articles span biomechanical modeling, wearable sensor validation, and neuro-musculoskeletal analysis, reflecting interdisciplinary innovation in human movement science.
Associate Professor Jason Thompson holds an Associate Professor position in the Department of Psychiatry at the University of Melbourne. He is affiliated with the Faculty of Medicine, Dentistry and Health Sciences and previously served as Co-Director of the Transport, Health and Urban Systems (THUS) Research Laboratory at the Melbourne School of Design. He earned a PhD in Medicine (2015) from Deakin University, a Master's in Clinical Psychology, and a Bachelor of Science with Honours. His research focuses on computational social science applied to injury rehabilitation, compensation systems, and healthcare design. He has attracted over $5M in research funding and published over 100 articles. Key areas include agent-based modeling, systems dynamics, and policy analysis for public health challenges such as pandemic response and urban mobility. Thompson currently leads the NHMRC Centre of Excellence in Compensable Injury after Road Crashes. Grants: ARC Future Fellowship (2022), DECRA (2017) Awards: Best Paper Award (Computational Social Science Society of the Americas, 2017) Labs: THUS Research Lab (until 2024) His work bridges epidemiological modeling (e.g., influencing Victoria's 2020 pandemic exit strategy) and complex systems analysis for injury prevention and health system design.
David Schlipf is a Professor at the Fachbereich Energy and Life Science, Hochschule Flensburg, leading the Wind Energy Technology Institute. His expertise spans lidar-assisted control systems, floating offshore wind turbines, and aeroelastic modeling. He actively collaborates with international initiatives like IEA Wind Task 32 and contributes to projects such as the 'Lidar Knowledge Europe (LIKE)' network. His research focuses on enhancing wind turbine efficiency through advanced control strategies and sensor technology integration. He has been instrumental in developing the TorqTwin open-source framework for multibody modeling and has published extensively on topics including wind field reconstruction, load mitigation, and floating platform dynamics. His work bridges academic research with industrial applications, emphasizing practical solutions for offshore wind energy challenges. Notable projects include the evaluation of lidar-assisted control performance, optimization of floating turbine designs, and contributions to wind energy education's role in climate resilience. His research outputs span over 200 publications, highlighting his global impact in advancing renewable energy systems.
Tarmo Lipping is a Professor in the Department of Computer Science and Engineering at the Faculty of Information Technology and Electrical Engineering, University of Oulu. His work bridges computing sciences with biomedical engineering, environmental modelling, and data-driven societal applications. Doctor of Science (Technology), Information Technology – Awarded 14 Feb 2001 Master of Science (Technology), Information Technology – Awarded 10 Sept 1993 His research focuses on electroencephalography (EEG) , mental workload assessment , depth of anesthesia monitoring , and machine learning applications in healthcare and human-computer interaction. He also contributes to environmental informatics , particularly in land uplift modelling and radionuclide transport , aligning with UN Sustainable Development Goals. Recent publications highlight trends in transformer networks for EEG analysis , wearable HCI systems , data-driven food safety , and participatory municipal governance . His work integrates deep learning, signal processing, and real-world deployment. Scientific awards include: CIMO opettajavaihto (2017) Lipping has supervised numerous master’s students and served as an examiner in diverse topics including data vault modelling , telecom revenue estimation , and EEG hyperscanning . He has evaluated funding applications, acted as a journal reviewer (65 times), and contributed to editorial work. His activities reflect strong engagement in academic service and interdisciplinary research mentorship. He has contributed datasets on Fennoscandian land uplift , lake isolation , and archaeological shorelines to PANGAEA, supporting open science in geosciences and environmental history.
Luis Merino Cabañas is a Professor at the Universidad Pablo de Olavide , affiliated with the Deporte e Informática department and leading the SRL Service Robotics Laboratory . His research focuses on robotics, systems engineering, and automation, with a specialization in human-robot interaction and path planning. Education : PhD in Systems Engineering from the Universidad de Sevilla (2007), where his thesis explored cooperative perception techniques for multiple unmanned aerial vehicles in forest fire detection. Research Trends : Recent work (2023–2025) emphasizes 3D path planning, sensor fusion (LiDAR, radar, inertial systems), neural distance fields for safe navigation, and socially aware robotics. His studies integrate AI, genetic programming, and multi-modal perception for applications in construction, healthcare, and GNSS-denied environments. Labs & Teams : He leads the SRL Service Robotics Laboratory , contributing to projects like the Skyeye team and BIM2ROS integration for construction robotics.
Seongjin Choi is an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities , where he began his role in January 2024. His research bridges Urban Mobility Data Analytics , Spatiotemporal Modeling , and Deep Learning to advance transportation systems. Affiliated with the Center for Transportation Studies , Minnesota Robotics Institute , and Data Science Initiative , he leads the Choi Research Group . Education: Ph.D., Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST), 2021 M.S., Civil and Environmental Engineering, KAIST, 2017 B.S., Civil and Environmental Engineering, KAIST, 2015 His research focuses on Urban Mobility Data Analytics and Deep Learning to optimize transportation systems. Key areas include: Spatiotemporal Data Modeling for forecasting and imputation Generative AI applications in transportation data Reinforcement Learning for Connected Automated Vehicles (CAV) Cooperative Intelligent Transport Systems (C-ITS) Recent publications in Transportation Science and Transportation Research Part C highlight his work on probabilistic traffic forecasting , deep generative models , and vision-language-action frameworks for autonomous systems. His methodologies often combine AI-driven analytics with real-time mobility optimization . Dr. Choi serves as: Associate Editor of The Journal of the Korean Society of Transportation (JKST) , 2023–Present Guest Editor for Journal of Advanced Transportation special issue on "Advanced Data Intelligence Theory and Practice in Transport 2023", 2023–2024 He actively seeks PhD students/postdocs for 2025 cohorts focused on machine learning for transportation challenges. Current projects include AI-enhanced traffic forecasting, CAV control, and urban air mobility (UAM) integration studies.
Catherine Neish is an Associate Professor in the Department of Earth Sciences at The University of Western Ontario. She serves as the Associate Director of Research for Western Space and is a Co-Investigator on NASA's Dragonfly mission to Titan. Her research focuses on planetary radar observations, impact cratering processes, and the geological evolution of planetary surfaces, particularly on the Moon, Titan, and other Solar System bodies. Ph.D. in Planetary Sciences, University of Arizona (2008) B.Sc. in Combined Honours Physics and Astronomy, University of British Columbia (2004) Her recent publications highlight studies on lunar impact crater thermophysics, Titan's impact melt dynamics, and radar-based analyses of planetary surfaces. She has contributed to missions including Lunar Reconnaissance Orbiter (Mini-RF), Cassini RADAR, and Dragonfly. Scientific Awards: College of New Scholars, Royal Society of Canada (2021) Early Researcher Award, Ontario (2017) Minor Planet 16972 Neish (2017) AGU Ron Greeley Award (2014) NASA Postdoctoral Fellowship (2012) NASA Group Achievement Award (2010) NSERC Postgraduate Scholarship (2005-2008) Julie Payette-NSERC Research Scholarship (2004-2005) Dr. Neish supervises a dynamic lab with current and former students investigating planetary geology, impact cratering, and remote sensing. Her work bridges field studies (e.g., Earth analogs), laboratory experiments, and spacecraft data analysis.
Mikael Rinne serves as Associate Professor in Rock Mechanics within the Department of Civil Engineering at Aalto University, Finland. Holding a Doctor of Science in Technology (D.Sc. Tech.), he brings extensive industry experience from Finnish and Swedish consulting firms (1988-2008) where he specialized in rock engineering and project management for tunneling and geological disposal of radioactive waste. His research focuses on rock and fracture mechanics with direct applications to rock engineering, mining, and tunneling. Current investigations center on digital characterization methods including photogrammetry, videogrammetry, and virtual reality systems for both practical engineering solutions and educational advancement. His work addresses critical challenges in fracture hydro-mechanics, rock mass characterization, and sustainable mining practices. Analysis of his 15 most recent publications (2023-2025) reveals a strong emphasis on digital transformation in rock mechanics. Key trends include non-contact surveying techniques for rock mass characterization, scale effects in fracture properties, and virtual learning environments for engineering education. His research bridges theoretical modeling with field applications in tunneling, mining, and radioactive waste disposal, demonstrating consistent innovation in measurement technologies and computational methods. No scientific awards were mentioned in the source materials. While specific advising details and grant information were not provided, his leadership of the Mineral-based materials and mechanics research group indicates active supervision of graduate students and management of research projects. His industry background suggests strong connections with tunneling and mining sectors for applied research collaboration. He directs the Mineral-based materials and mechanics research group at Aalto University, which develops advanced methodologies for rock characterization and engineering applications. Current initiatives integrate digital tools like smartphone LiDAR, 360-degree cameras, and virtual reality systems to enhance both field practices and educational outcomes in rock engineering.
Kurt Keutzer is a Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, and a key member of the Berkeley AI Research Lab (BAIR). He holds a Ph.D. in Computer Science from Indiana University (1984) and was previously Chief Technical Officer at Synopsys, Inc. His research focuses on systems issues in deep learning, particularly for computer vision, speech recognition, NLP, and finance. He has published over 250 refereed articles and six books, and is a highly cited author in hardware and design automation. Keutzer has received multiple IEEE Fellowships, DAC awards, and best paper accolades at conferences like Embedded Vision Workshop and ICPP. Educations: 1984, PhD, Computer Science, Indiana University Kurt Keutzer's research interests span Artificial Intelligence , Computer Architecture , and Scientific Computing , with a focus on computational efficiency in AI systems. His work explores hardware-aware neural architecture search, domain adaptation, and quantization techniques to optimize models from edge to cloud. Recent publications highlight advancements in vision transformers , LLM inference efficiency , and autonomous driving . He also contributes to multimodal AI and self-supervised learning frameworks. Scientific Awards: Institute of Electrical & Electronics Engineers (IEEE) Fellow (1996) DAC's Most Influential Paper Award (2023) Top Ten Cited Author and Paper at DAC Best Paper Awards at Embedded Vision Workshop and ICPP Kurt Keutzer has advised numerous Ph.D. and Master’s students, including Forrest Iandola (co-founder of DeepScale), Sheng Shen, and Michael Murphy. His research teams have pioneered hardware-efficient deep learning solutions like SqueezeNet and FireCaffe. Current projects include optimizing large language models (LLMs) for edge deployment and advancing 3D reconstruction for autonomous vehicles. He is also involved in diffusion models , sparse attention mechanisms , and multi-agent coordination for complex tasks.
