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Robert BerryView profile

Senior Lecturer

• Geocomputation
• Geographical Information Systems (GIS)
• Artificial Intelligence
• +7 more

Robert Berry is a Senior Lecturer at the Faculty of Computing, Engineering and Science at the University of South Wales. He holds a PhD in Geographical Information Systems, an MSc in GIS, and a BSc in Geography. His research focuses on geocomputation, integrating AI, NLP, and GIS to address geospatial challenges, with recent work in accessibility modeling and 3D landscape visualization. He is affiliated with the GIS Research Centre and WISERD, an interdisciplinary social science institute. Dr. Berry teaches modules in computing and information systems, including big data analytics, GIS, and project management. He has supervised numerous MSc and PhD students and contributed to over 50 research outputs, including peer-reviewed articles, conference papers, and technical reports. His expertise spans FOSS tools, programming (R, Python), and cloud computing for geospatial applications. Key projects include the 'People, Places and the Public Sphere' initiative (2024–2027) and evaluations of agricultural risk management policies. He has collaborated with UK government agencies, local authorities, and third-sector organizations on projects involving environmental planning, flood management, and cultural heritage preservation. Dr. Berry is a Fellow of the Royal Geographical Society and actively engages in advancing participatory technologies for environmental decision-making. His work emphasizes enhancing public participation through digital tools and addressing socio-economic challenges via geospatial analysis.

Sebastiano VASCONView profile

Associate Professor

• Artificial Intelligence
• Computer Vision
• Machine Learning
• +4 more

Sebastiano Vascon is an Associate Professor at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics (DAIS), and affiliated with the European Center for Living Technology. He earned his PhD in 2016 from the Italian Institute of Technology and University of Genoa, focusing on evolutionary game theory in pattern analysis and computer vision. His postdoctoral work spanned institutions like the Technical University of Munich and ETH Zurich, where he specialized in Active Learning and multi-object tracking. His research merges AI with interdisciplinary challenges, including climate change, environmental science, and cultural heritage preservation. Key areas include graph neural networks, computer vision, and game-theoretic models. He leads projects like RePAIR (AI for cultural heritage reassembly) and EasyWalk (AI-driven mobility solutions), and contributes to initiatives like MEMEX (digital storytelling). Teaching spans courses in Deep Learning, Machine Learning for Environmental Applications, and AI in Cultural Management. Research projects include: RePAIR: AI-driven 3D puzzle solving for artifact reconstruction EasyWalk: Socially-aware navigation systems MEMEX: AI for inclusive digital storytelling Climate modeling with IceBoost framework Publications highlight innovations in trajectory forecasting, environmental risk assessment, and graph-based methods. He actively reviews for top conferences (CVPR, ECCV) and journals.

• Real Estate Economics
• Housing Economics
• Urban Economics
• +8 more

Rainer Schulz is a Senior Lecturer at the Business School of the University of Aberdeen, where he conducts research and teaches in real estate economics, housing markets, and financial economics. He is actively accepting PhD students and supervises research in real estate. His affiliations include the Gillmore Centre for Financial Technology at Warwick Business School and the Berlin Center for AI Research in Economics and Management. Research Interests: Dr. Schulz's research focuses on urban real estate markets using econometric and machine learning methods. Key areas include automated valuation modelling (AVM), real estate investment vehicles, fund management, and location choice. He investigates the impact of teleworking on housing demand and the application of real options in real estate development. His work is interdisciplinary, combining urban economics, financial theory, and data science. Publication Trends: His recent publications (2022–2024) emphasize the effects of teleworking on housing markets, machine learning applications in real estate, and local housing market information systems. Earlier works focus on land value mapping, hurdle rates in investment, and the relationship between house prices and macroeconomic variables. His research consistently applies advanced quantitative techniques to real-world property market problems. Scientific Awards: Poster award for Semiparametric Estimation of Land Values, 2015 Aareal Award of Excellence in Real Estate Research, 2012 ARES Foundation Award, 2006 Deutsche Immobilien Akademie Research Prize for doctoral thesis, 2003 Advising and Grants: Dr. Schulz is currently accepting PhD students in Real Estate. He leads the compilation of the Aberdeen Housing Market Report, a knowledge exchange project with the Aberdeen Street Property Company (ASPC). While specific grants are not listed, his research is supported through institutional collaborations and publications in high-impact journals. Labs and Teams: He is affiliated with the Centre for Real Estate Research at the University of Aberdeen Business School. He collaborates with researchers at Humboldt University (Berlin) and participates in international research networks such as the Berlin Center for AI Research. His work involves interdisciplinary teams focusing on urban data, housing policy, and financial technology.

Qi ChenView profile

Professor

• Remote Sensing
• LiDAR Applications
• Geographic Information Systems
• +5 more

Qi Chen is a Professor in the Department of Geography at the University of Hawaii at Mānoa, specializing in remote sensing and geospatial technologies. His office is located in Saunders Hall, and he teaches undergraduate and graduate courses including GEO 370 (UAV and Aerial Photography), GEO 388 (Introduction to GIS), GEO 470 (Remote Sensing), GEO 489 (Applied GIS), and GEO 762 (Research Seminar: Remote Sensing. His research focuses on transforming earth observation data into actionable knowledge for environmental monitoring. Primary interests include: LiDAR applications for vegetation analysis and biomass estimation Climate change impacts on land cover and coastal systems Machine learning integration with geospatial data High-resolution mapping of agricultural and forest ecosystems Drone and satellite-based environmental assessment Chen's recent publications (2020-2025) demonstrate a strong focus on advancing remote sensing methodologies, particularly through: AI-driven approaches (GANs for vegetation indices, deep learning for marine debris) Multi-sensor fusion (LiDAR with camera systems, hyperspectral-multispectral integration) Novel applications in precision agriculture and infrastructure monitoring Hawaii-specific environmental studies incorporating indigenous knowledge systems He leads the Smart Remote Sensing Lab (smartremotesensing.org) where he mentors graduate students in developing cutting-edge geospatial solutions for ecological and societal challenges.

Mitchel LangfordView profile

Professor

• Spatial Analysis
• Geoinformatics
• Applied Computational Geography
• +5 more

Mitchel Langford is a Professor and Co-Director of the Wales Institute of Socio-Economic Research and Data (WISERD). He holds an academic position within the Faculty of Computing, Engineering and Science, focusing on spatial analysis, geoinformatics, and computational geography. His research spans over 35 years, emphasizing geographical accessibility, dasymetric mapping, and software engineering solutions for spatial problems. Langford earned his first degree in Physical Geography and Geology, followed by a PhD in software development for palynology using FORTRAN. He has extensive teaching experience in software engineering (C#, SQL, Python) and geoinformatics (PostgreSQL/PostGIS, web mapping). His key research contributions include pioneering work in dasymetric areal interpolation and multi-modal accessibility modeling, notably the Enhanced Two-Step Floating Catchment Area (E2SFCA) method. He has published over 119 peer-reviewed articles and consulted for international organizations like CIAT. Notable awards include the 2019 Impact Awards for contributions to spatial accessibility research. Current projects include investigating accessibility to public services (transport, healthcare, childcare) using GIS and multi-modal transport networks. Langford is also involved in policy-oriented research, contributing to Welsh Senedd inquiries on banking and healthcare access. His software engineering skills enable bespoke solutions for spatial analysis, emphasizing modern languages like Python and JavaScript.

Sara Benjamin NeelonView profile

Professor

• Child Nutrition
• Obesity Prevention
• Early Life Predictors of Chronic Disease
• +12 more

Sara Benjamin-Neelon is a Professor at the Johns Hopkins Bloomberg School of Public Health, with a primary appointment in the Department of Health, Behavior and Society and a joint affiliation with the Department of International Health. She is also the Director of the Lerner Center. Holding a PhD, JD, and MPH, her interdisciplinary expertise spans child nutrition, public health law, and chronic disease prevention. Educational Background: JD, Mitchell Hamline School of Law, 2017 PhD, University of North Carolina at Chapel Hill, 2006 MPH, University of North Carolina at Chapel Hill, 2002 BS, Ithaca College, 1995 Dr. Benjamin-Neelon’s research centers on early life predictors of obesity and chronic diseases, with a strong emphasis on health equity. Her work explores community-based interventions for children and families, food insecurity, policy evaluation (especially in nutrition and physical activity), epigenetic and environmental influences on child growth, and the social determinants of early life feeding, physical activity, sleep, and stress. She examines both domestic and global contexts, contributing significantly to understanding how policy changes can create healthier environments. Her extensive publication record includes over 196 research outputs, with recent articles focusing on childcare environments, breastfeeding policies, cannabis use during pregnancy, university beverage contracts, and infant feeding regulations. The trends in her recent work highlight a strong focus on policy analysis, health equity, and the evaluation of natural experiments in public health. She frequently investigates how structural and environmental factors shape child health outcomes. She has not received any explicitly mentioned scientific awards in the provided text. Dr. Benjamin-Neelon advises students and leads significant research projects, including evaluations of state nutrition and physical activity policies, the impact of childcare on obesity risk, and initiatives to improve healthy feeding in early childhood. Her work often involves collaboration with interdisciplinary teams and has significant implications for public health grants and policy development. She leads or contributes to key research initiatives such as the evaluation of South Carolina’s nutrition and physical activity policies, studies on licensing laws for infant and toddler care, and the Maryland Collaborative to reduce excessive college drinking. Her research environment is collaborative, involving multiple institutions and public health agencies.

• Artificial Intelligence
• Robotics
• Data Science
• +4 more

Cecilio Angulo Bahón is a full Professor at the Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Industrial Engineering (ETSEIB) and the Department of Systems, Automatics and Industrial Informatics Engineering . He leads research in Artificial Intelligence and Robotics , with significant contributions to healthcare data analytics, digital twins, and human-robot collaboration. His research spans machine learning for medical data harmonization, generative adversarial networks in health informatics, and evolutionary algorithms for control systems. Recent publications focus on synthetic healthcare data generation, climate-resilient agriculture , and UMAP-based data analysis . His work bridges AI theory with practical applications in industrial and healthcare domains. Scientific awards include the Sant Jordi 2023 Digital Polytechnic Initiative Award . He has supervised doctoral candidates like Carlos Flores-Vázquez and N. Raya, with key collaborations at the IDEAI-UPC Intelligent Data Science and AI Research Group and the Institute of Robotics and Industrial Informatics (CSIC-UPC).

Ellen RathjeView profile

Professor

• Geotechnical Engineering
• Earthquake Engineering
• Seismic Response Analysis
• +5 more

Ellen Rathje is a Professor and Janet S. Cockrell Centennial Chair in Engineering at the University of Texas at Austin's Department of Civil, Architectural, and Environmental Engineering within the Cockrell School of Engineering. Her expertise spans geotechnical engineering with a focus on earthquake engineering, seismic response of earth structures, and liquefaction evaluation. She leads research initiatives such as DesignSafe cyberinfrastructure and TexNet Seismological Network, advancing geohazard risk assessment and computational modeling. Education: Ph.D. (1997), M.S. (1994), and B.S. (1993) in Civil Engineering from University of California, Berkeley and Cornell University. Research Interests: Dr. Rathje investigates earthquake-induced ground failures, including lateral spreading, liquefaction, and slope instabilities. Her work integrates machine learning, finite element analysis, and geospatial data to enhance predictive models for infrastructure resilience. Recent projects address induced seismicity in energy-producing regions, site amplification in Central and Eastern North America, and tailings dam failure mechanisms. Awards: Recipient of the 2022 Ralph B. Peck Award, 2018 William B. Joyner Lecture Award, and 2016 ASCE Fellow distinction. Her work has advanced open science through DesignSafe's cyberinfrastructure, supporting natural hazards research collaboration. Technical Contributions: Developed hybrid finite-element/material point methods for granular collapse modeling, probabilistic frameworks for regional landslide assessments, and neural network-based site amplification models. Active in post-earthquake reconnaissance through GEER (Geotechnical Extreme Events Reconnaissance) and TexNet operations.

• Scientific Machine Learning
• Climate Science
• Earthquake Seismology
• +3 more

Karianne Bergen is an Assistant Professor of Data Science and Earth, Environmental & Planetary Sciences at Brown University, with a courtesy appointment in Computer Science. She leads the Scientific Machine Learning (SciML) Research Group, affiliated with the Data Science Institute (DSI), DEEPS, and the SciAI Center. Her research focuses on scientific machine learning (SciML), including surrogate models for climate science, explainable AI (XAI), and foundation models. She holds a Ph.D. and M.Sc. in Computational and Mathematical Engineering from Stanford University and a B.Sc. in Applied Mathematics from Brown University. Her postdoctoral training included a Harvard University HDSI fellowship in Computer Science. Education: B.Sc. Applied Mathematics, Brown University (2009) M.Sc. Computational and Mathematical Engineering, Stanford University (2015) Ph.D. Computational and Mathematical Engineering, Stanford University (2018) Research Interests: Dr. Bergen’s work bridges machine learning and Earth sciences, emphasizing scalable methods for climate modeling and geophysical data analysis. Her group develops emulators for Antarctic ice sheet dynamics, XAI frameworks for climate data interpretation, and scientific foundation models for geoscience applications. Recent projects include GAN-based sea ice resolution enhancement and flow-based neural networks for sea level projections. Awards: Harvard Data Science Initiative Postdoctoral Fellowship (2018–2020) Stanford Graduate Fellowship in Science and Engineering (2011–2015) Outstanding Student Paper Award, American Geophysical Union (2015) Advising & Collaborations: She mentors PhD students in Earth, Environmental, and Planetary Sciences and collaborates with institutions like MIT-Lincoln Laboratory, SciAI Center, and international geoscience groups. Her lab includes postdocs (e.g., Hilarie Sit) and alumni from data science practicum programs. Labs/Teams: The SciML group at Brown University focuses on multidisciplinary projects at the intersection of AI and Earth sciences, with recent presentations at AGU Fall Meetings and the AI for Science Workshop at ICML.

• Software Engineering
• Spatial Information Systems
• Accessibility Technology
• +3 more

David McMeekin is a Senior Lecturer at Curtin University's School of Elec Eng, Comp and Math Sci (EECMS), part of the Faculty of Science and Engineering. He holds a PhD in Software Engineering focusing on the human aspects of software development and has extensive experience in both academia and international NGOs. His research emphasizes applying technology to solve societal challenges, including accessibility for vision-impaired individuals, autism support through tech, cultural heritage preservation, and spatial data infrastructure. Education and professional background are not explicitly detailed in this profile, though his career spans over three decades in education and tech sectors. Research interests include semantic web technologies for spatial data, assistive technologies for disabilities, and digital humanities. He has been recognized with the 2014 Western Australian Spatial Educator of the Year award. Teaching spans topics like software engineering, programming languages (C, Java, Python), cross-cultural communication, and spatial data processing. His work bridges technical innovation with social impact, particularly in enhancing accessibility and cultural preservation through digital tools.

Fan-Rui MengView profile

Professor

• Hydrology
• Ecological Modeling
• Watershed Management
• +6 more

Dr. Fan-Rui Meng is a Professor at the Faculty of Forestry and Environmental Management, University of New Brunswick (UNB). He holds an adjunct professorship at Fujian Agriculture and Forestry University, China. His expertise spans hydrology, ecological modeling, watershed management, and climate change impacts. Dr. Meng earned his PhD in forest ecology from UNB and has held research leadership roles at institutions like the Noranda/Avenor and Bowater/Nexfor Forest Watershed Research Centres. He has extensive experience in soil conservation, GIS applications, and drone technology for environmental protection. Education: Bachelor and Master of Forestry Engineering, Northeast Forestry University, China PhD in Forest Ecology, UNB Research Interests: Hydrological processes in agricultural and forested watersheds Climate change adaptation in forest ecosystems Soil erosion and conservation strategies Integration of drone technology for environmental monitoring Carbon cycling in forest systems Modeling forest growth under climate projections Recent Research Trends: His recent work focuses on nitrate dynamics in agricultural watersheds, impacts of climate change on crop yields, and improving SWAT model accuracy through detailed land-use data. He has explored innovative methods for riparian zone mapping using UAVs and photogrammetry, emphasizing precision in environmental assessments. Awards: None explicitly listed in the provided texts. Grants & Advising: Dr. Meng has led numerous research projects but no specific grants or student advisees are mentioned. His work has been published in journals like Hydrological Processes , Journal of Hydrology , and Water Resources Management . Labs & Teams: He has directed research at the Noranda/Avenor and Bowater/Nexfor Forest Watershed Management Centres, focusing on applied ecological and hydrological research.

Rebecca ShakespeareView profile

Lecturer

• Geographic Information System (GIS)
• Urban Geography
• Housing Policy
• +5 more

Rebecca Shakespeare is a Lecturer in Urban & Environmental Policy & Planning at Tufts University's School of Arts and Sciences. She holds a Ph.D. in Geography and GIS from the University of Illinois at Urbana-Champaign (2019), an M.A. in International Communication from American University (2007), and a B.A. in History from Indiana University Bloomington (2005). Her research focuses on urban housing affordability, neighborhood dynamics, and spatial equity, employing qualitative GIS, interviews, and policy analysis. Notably, her NSF-funded dissertation explored middle-income renters' mobility decisions in New York City, Beijing, and Chicago. She has also conducted fieldwork on environmental exposure and urban transportation patterns in Massachusetts. Her work bridges urban geography and policy, emphasizing how residents perceive and are impacted by urban environments. Recent publications address housing opportunity perceptions, public transportation trends, and allergy-friendly urban food access. Prior to academia, Shakespeare served as a Presidential Management Fellow and digital media analyst at the U.S. Agency for Global Media. Her research has been supported by the National Science Foundation, reflecting her expertise in linking spatial analysis to socio-economic urban challenges. While no formal advisees are listed, her teaching and research mentorship contribute to the next generation of urban planners and geospatial analysts.

• Data-Driven Optimization
• Large-Scale Data Analytics
• Machine Learning
• +4 more

Hai Wang is an Associate Professor at the School of Computing and Information Systems, Singapore Management University, and a Visiting Professor at Carnegie Mellon University's Heinz College. His research bridges data-driven optimization, machine learning, and urban systems, with applications in smart cities, transportation, and shared economies. He holds a PhD in Operations Research from MIT and a BS from Tsinghua University. Research Focus: Wang pioneers methodologies in large-scale data analytics and stochastic optimization to address challenges in ride-sourcing, food delivery logistics, and urban mobility. His work emphasizes practical implementations, including collaborations with industry leaders like Meituan and DiDi. Publications: His recent articles (2023-2025) predominantly explore transportation economics, platform operations, and behavioral analytics in service systems, using advanced computational techniques to model complex urban networks. Honors: World's Top 1% Scientist (2024), Transportation Science Meritorious Service Award (2022-2024), INFORMS Best Paper Award (2024) Grants: Principal Investigator for SG$4M National Research Foundation project on AI-empowered urban systems; collaborations with Tokka Labs (SG$156k) and Mobility21 (US$186k) Students: Advises PhD candidates YANG Jingfeng and Rajiv Ranjan KUMAR on optimization and machine learning applications. Leadership: Serves as Associate Editor for Transportation Science , IISE Transactions , and Service Science , and co-leads INFORMS Transportation Science and Logistics Society initiatives.

Kostas MagoutisView profile

Professor

• Distributed computer systems
• Scalable data stream processing
• Internet of Things
• +3 more

Kostas Magoutis is Professor and Chair of the Computer Science Department at the University of Crete and collaborating researcher with FORTH-ICS. His research focuses on distributed systems, scalable data processing, IoT, and cloud computing, with projects including GreenInCities for urban regeneration and STREAMSTORE for stateful stream processing systems. Research interests include: Distributed computer systems architecture Elastic stream processing platforms Quantum-enhanced computing applications Multi-cloud application lifecycle management Recent publications demonstrate strong focus on federated data systems, quantum computing applications, and IoT-enhanced infrastructure, with consistent output in high-impact conferences and journals. Awards and distinctions: Multiple best paper awards from USENIX conferences Grand Challenge Audience Award at DEBS 2022 Marie Curie Fellowship and IBM Research awards Advises over 20 PhD and MSc students in distributed systems research. Leads multiple EU-funded projects and serves on program committees for top conferences including SOSP, EuroSys, and IEEE BigData. Directs research groups in distributed systems and cloud computing at FORTH-ICS.

Nirwan AnsariView profile

Professor

• 6G Networks
• Wireless Charging
• Machine Learning
• +5 more

Nirwan Ansari is a Distinguished Professor in the Department of Electrical and Computer Engineering at New Jersey Institute of Technology (NJIT). His research focuses on cutting-edge advancements in 6G networks , wireless charging , machine learning , and Internet of Things (IoT) . He has contributed extensively to AI-driven network optimization and sustainable energy solutions for next-generation communication systems. Ph.D., Electrical Engineering, Purdue University (1988) M.S., Electrical Engineering, University of Michigan-Ann Arbor (1983) B.S., Electrical Engineering, NJIT (1982) His recent work explores AI-native network slicing , UAV-assisted edge computing , and holographic communication . Publications highlight synergies between terrestrial and non-terrestrial networks , digital twin integration, and energy-efficient IoT systems . He serves as an Honorary Chair and contributes to major conferences like IEEE INFOCOM and IWCMC.