Find Academic Supervisors

Aleksi LehikoinenView profile

Associate Professor

• Ornithology
• Population Monitoring
• Climate Change Ecology
• +4 more

Aleksi Lehikoinen is an Associate Professor at the University of Helsinki's Faculty of Biological and Environmental Sciences, holding a docentship and serving as Supervisor in the Doctoral Programme in Sustainable Use of Renewable Natural Resources and the Doctoral Programme in Wildlife Biology. He works in the Zoology Unit as Chief Superintendent and is based at the Biocenter 3 in Viikinkaari, Helsinki. His research focuses on ornithology, climate change impacts on biodiversity, and conservation biology, with extensive field work in Finnish ecosystems. Lehikoinen's research interests center on bird population monitoring, climate change effects on avian communities, and biodiversity conservation. His work examines how climate warming influences species distributions, community composition, and ecosystem functioning, particularly in boreal regions. He investigates the effectiveness of protected areas in mitigating climate change impacts and studies human-wildlife interactions through citizen science initiatives. His research combines long-term monitoring data with advanced statistical approaches to understand ecological patterns and processes. His recent publications (2024-2025) demonstrate a strong focus on climate change ecology, with particular attention to how warming temperatures affect bird communities in boreal regions. The research spans multiple scales from individual species responses to community-level changes, examining thermal niches, distribution shifts, and biodiversity-stability relationships. His work increasingly incorporates interdisciplinary approaches, connecting ecological monitoring with human dimensions of conservation through studies of citizen science and public engagement in bird monitoring. BirdLife Suomen kultainen ansiomerkki (2024) Environmental award of Sophie von Julins society to Hanko Bird Observatory (2010) Helsingin Seudun Lintutieteellisen yhdistyksen Tringan BirdLife -ansiomerkki (2011) Influencer of the year in the Finnish Museum of Natural History (2011) Newcomer of the year 2010 in the Museum of the Natural History (2010) Lehikoinen actively supervises doctoral students in sustainable resource use and wildlife biology programs. He leads multiple significant research projects including 'Lajien kumulatiiviset ja vuorovaikutteiset vasteet ilmastonmuutokseen' (2024-2028) funded by the Academy of Finland, 'HABITRACK RIA' (2024-2027) with the European Commission, and 'Digitaalinen kansalaistiedekeskus Erkko' (2024-2028) funded by the Jane and Aatos Erkko Foundation. His work combines scientific research with practical conservation applications. Lehikoinen is deeply involved in large-scale biodiversity monitoring initiatives, particularly bird population monitoring programs in Finland. He collaborates with the Finnish Museum of Natural History and participates in citizen science projects that engage the public in data collection. His work with the 'Lintuatlas' (Bird Atlas) project represents a significant contribution to understanding bird distribution changes across Finland, combining professional scientific monitoring with volunteer observations.

Marco L. Della VedovaView profile

Senior Lecturer

• Artificial Intelligence and Machine Learning
• Interpretable and Explainable AI
• Geospatial AI
• +7 more

Marco L. Della Vedova is a Senior Lecturer in Applied Artificial Intelligence at Chalmers University of Technology, Sweden. He works in the Vehicle Engineering and Autonomous Systems division within the Department of Mechanics and Maritime Sciences, as part of Prof. Mattias Wahde's research group. Since 2025, he has served as Director of the Data Science and AI master's programme (MPDSC) at Chalmers, where he teaches courses including Introduction to Artificial Intelligence and Digitalization in Sports. Dr. Della Vedova earned his academic foundation at the University of Pavia, Italy, where he completed his BSc (2006), MSc (2009), and PhD (2013) in Computer Engineering. His doctoral research focused on "Real-Time Physical Systems and Electric Load Scheduling" under Prof. Tullio Facchinetti. During his PhD studies, he spent a year at U.C. Berkeley hosted by Prof. Francesco Borrelli at the Model Based Predictive and Distributed Control Lab. His research spans multiple AI domains with a strong emphasis on interpretability. Dr. Della Vedova develops interpretable methods for conversational AI, naturalness evaluation of forests using canopy height models, and geospatial applications. His work bridges theoretical AI with practical societal benefits, particularly in environmental monitoring, transportation systems, and orienteering. He has previously contributed to cloud computing, hate speech detection, and cyber-physical energy systems, demonstrating his interdisciplinary approach to AI research. Dr. Della Vedova's publication record reveals a consistent trajectory of impactful research across multiple domains of artificial intelligence. His recent work shows a strong focus on interpretability in AI systems, with significant contributions to natural language processing, geospatial analysis, and causal inference. The research demonstrates both theoretical depth and practical applications, particularly in environmental monitoring and social media analysis. His methodology often combines traditional machine learning approaches with novel interpretability techniques, creating bridges between complex AI systems and human understanding. Dr. Della Vedova has received several prestigious recognitions for his work: Best PhD thesis award from the Order of the Engineers of Bergamo (2013) Italian champion of Il Cervellone (2012) Top Italian performer in IEEEXtreme 6.0 programming competition (148th overall globally, 2012) Premio Arturo Schena award from Fondazione Credito Valtellinese (2010) With over 50 students supervised through bachelor's and master's theses, Dr. Della Vedova has established himself as a dedicated mentor in the AI community. His current PhD students include Minerva Suvanto working on interpretable NLP and Vivien Lacorre developing AI for railway infrastructure inspection. His supervision spans diverse topics from forest naturalness evaluation to hate speech detection and transportation optimization. Beyond formal supervision, he actively contributes to educational initiatives including serving as Director of Chalmers' Data Science and AI master's program and developing innovative teaching methods that connect theoretical concepts with real-world applications. Dr. Della Vedova is deeply embedded in both academic and professional communities. He leads the Applied Artificial Intelligence research group at Chalmers while maintaining strong connections with European research networks through projects like the ERASMUS+ EUrienteering initiative. His interdisciplinary approach is reflected in collaborations across computer science, environmental science, and social sciences. Notably, he applies his AI expertise to orienteering both as a researcher developing localization methods and as a licensed Event Advisor for the International Orienteering Federation, demonstrating how his professional and personal interests converge in innovative ways.

Greg J. EvansView profile

Professor

• Urban Air Pollution
• Traffic-Related Air Pollutants
• Air Pollutant Toxicity
• +14 more

Greg J. Evans is a full Professor in the Faculty of Applied Science and Engineering at the University of Toronto , where he also serves as Director of the Southern Ontario Centre for Atmospheric Aerosol Research (SOCAAR) and the Institute for Studies in Transdisciplinary Engineering Education and Practice (ISTEP) . In addition, he is Principal Investigator of the Evans Research Group . Education: B.A.Sc. – Bachelor of Applied Science M.A.Sc. – Master of Applied Science Ph.D. – Doctor of Philosophy P.Eng. – Professional Engineer FCAE – Fellow of the Canadian Academy of Engineering FAAAS – Fellow of the American Association for the Advancement of Science Research Interests: Professor Evans leads interdisciplinary research that bridges atmospheric aerosol science, environmental health, and engineering education. His work in urban air pollution focuses on understanding traffic-related emissions, developing low-cost sensor networks, and elucidating the oxidative toxicity of particulate matter under climate change scenarios. Within engineering education, he investigates transdisciplinary competencies, metacognitive development, and evidence-based pedagogies that prepare students for lifelong learning and societal impact. Research Trends in Recent Publications: Across his 2021-2023 publications, a dominant theme is the health impact of fine particulate matter (PM2.5) and its oxidative potential. Studies integrate large-scale epidemiological datasets with detailed chemical analyses to reveal how specific PM constituents—such as transition metals and sulfur—mediate cardiovascular and respiratory outcomes. Methodological innovations include mobile “lab-on-wheels” platforms and harmonized acellular assays for oxidative potential, enabling high-resolution spatiotemporal exposure assessment and cross-study comparability. Scientific Awards & Honors: CIC Environment Division Research and Development Dima Award (2022) NSERC Brockhouse Canada Prize for Interdisciplinary Research (2021) Fellow of the Canadian Engineering Education Association (2020) 3M National Teaching Fellowship (2017) Faculty of Applied Science & Engineering Research Leadership Award (2017) President’s Teaching Academy, University of Toronto (2015) Fellow of the Canadian Academy of Engineering (2015) Ontario Colleges and Universities Faculty Association Teaching Award (2015) Fellow of the American Association for the Advancement of Science (2015) Allan Blizzard Award (STLHE) (2014) Northrop Frye Award (2013) ASEE St. Lawrence Section Outstanding Teaching Award (2010) Engineers Canada Medal for Distinction in Engineering Education (2010) Ontario Professional Engineers Award – R&D Medal (2009) Joan E. Foley Student Quality of Student Experience Award (2008) Graduate Advising & Funding: Professor Evans is currently potentially accepting both PhD and MASc students. His research grants support interdisciplinary projects ranging from mobile air-quality monitoring campaigns to large-scale educational data-mining initiatives. Labs & Teams: He directs the Evans Research Group , housed within SOCAAR, which operates the MAPLE “lab-on-wheels” for real-time aerosol characterization. Through ISTEP, he oversees a cohort of 11 faculty members advancing engineering education scholarship.

• Information Visualization
• Data Visualization
• Human-Computer Interaction
• +4 more

Dr. Stuart Marshall serves as Associate Dean of Academic Development in the Faculty of Science and Engineering at Victoria University of Wellington - Te Herenga Waka, where he also holds the academic rank of Senior Lecturer. Previously, he served as Head of School for the School of Engineering and Computer Science from 2015-2021 after joining the institution as a Lecturer in 2003. His academic journey includes completing his BSc, MSc, and PhD in Computer Science at Victoria University of Wellington, with his doctoral research focusing on software reuse. Marshall's research interests center on information and data visualization, particularly exploring how visualization can function as collaborative tools in team environments rather than single-user applications. He also investigates explainable AI and mobile user interface design with emphasis on promoting healthy device usage patterns. His work bridges theoretical computer science with practical applications in education and environmental analysis. Analysis of his publication record reveals a consistent focus on visualization techniques, with recent work shifting toward immersive analytics and virtual reality applications for complex data analysis, particularly in ecosystem services. Earlier publications concentrated on mobile learning applications grounded in transactional distance theory, software visualization for large codebases, and graph layout algorithms. Marshall has supervised six PhD students and eight Masters students to completion, with current doctoral supervision spanning immersive environments for reading assistance, medical diagnosis interfaces, and cardiac procedure simulation. His grant portfolio includes multiple ACM ISS 2022 projects with industry partners including Sensor Holdings Limited, Niantic, Autodesk, and Northeastern University, along with an Australian Research Council grant for digital games preservation. Teaching responsibilities encompass foundational programming, safety-critical systems, human-computer interaction, and data visualization across undergraduate and graduate courses. He teaches subjects including SWEN326 (Safety-Critical Systems), CGRA151 (Introduction to Computer Graphics and Games), and DATA301 (Data Science in Practice), with teaching assignments scheduled through 2026.

• Volcanology
• Volcanic Hazard Assessment
• Computational Geophysics
• +2 more

Sara Barsotti is a researcher at the National Institute of Geophysics and Volcanology (INGV) specializing in volcanology and volcanic hazard assessment. Her key roles include: Associate Editor for Volcanology at Frontiers in Earth Science Review Editor for Geohazards and Georisks at Frontiers in Earth Science Contributor to the EU Center of Excellence for Exascale in Solid Earth (ChEESE) Member of the EUROVOLC citizen-science initiative Collaborator with European volcano observatories Her research focuses on computational geophysics, volcanic hazard modeling, and operational monitoring systems. She investigates tephra dispersion dynamics, lava flow behavior, and probabilistic hazard assessment using high-performance computing. Her work emphasizes European volcanic systems—particularly Icelandic eruptions—and integrates multidisciplinary approaches to improve crisis management protocols and public safety during volcanic events. Analysis of her recent publications reveals a strong trend toward operationalizing advanced computational methods for real-time hazard assessment. Key developments include exascale computing applications for solid earth simulations, refinement of the Aviation Colour Code system for aviation safety, and citizen-science data integration for eruption monitoring. These efforts consistently target practical risk mitigation strategies during active volcanic crises like the 2021 Fagradalsfjall eruption. Scientific awards: None mentioned in the provided text. While specific advised students or individual grants aren't documented, her leadership in major European projects (ChEESE, EUROVOLC) demonstrates substantial involvement in funded research initiatives. These projects coordinate transnational collaborations involving observatories, research centers, and emergency management agencies across Europe. Barsotti actively participates in integrated European volcano infrastructure teams, contributing to standardized monitoring protocols, hazard communication frameworks, and crisis response systems. Her work bridges institutional observatories (e.g., Icelandic Meteorological Office, INGV sections) with community-based monitoring tools, enhancing data collection and public engagement during volcanic unrest.

Jonathan FrameView profile

Assistant Professor

• Hydrology
• Machine Learning for Earth Systems Modeling
• Geospatial Modeling and Prediction
• +4 more

Dr. Jonathan Frame is an Assistant Professor of Artificial Intelligence/Machine Learning in Geological Sciences at the University of Alabama (2024–present) and a Faculty Fellow at the Alabama Water Institute (2024–2027). He holds a PhD in Geological Sciences from the University of Alabama (2022), an MS in Civil Engineering from the University of California, Irvine (2011), and a BS in Earth Systems Science, Technology, and Policy from California State University, Monterey Bay (2010). His research focuses on advancing hydrologic modeling through machine learning, including deep learning for streamflow forecasting, geospatial modeling, and flood prediction systems. Notable projects include improving the National Water Model with LSTM networks and developing rapid inundation mapping techniques using satellite data. He has contributed to over 30 peer-reviewed publications and actively participates in conferences like AGU and NeurIPS. His engineering experience spans flood risk mitigation, groundwater analysis, and pipeline transient modeling across California, Texas, and Washington. Research Interests Machine learning integration in hydrological systems Operational flood forecasting and inundation mapping Data-driven approaches for ungauged basins Climate nonstationarity and model adaptability Hydraulic transient analysis in water infrastructure Recent Contributions Frame’s recent work emphasizes NextGen water modeling frameworks, combining physics-based models with AI to enhance predictive accuracy. His 2025 paper on heterogeneous water modeling frameworks and 2024 studies on rapid inundation mapping highlight innovations in integrating satellite observations with hydrologic models. He also explores topics like mass conservation constraints in rainfall-runoff models and evapotranspiration prediction using deep learning. Grants & Projects FEMA partnership for near-real-time flood damage prediction systems NOAA-funded research on AI in environmental sciences NASA snowpack analysis for water resources forecasting Development of the Tarsier environmental modeling framework Labs & Collaborations Frame collaborates with the Alabama Water Institute and contributes to interdisciplinary teams advancing hydrologic AI. His work intersects with climate science, environmental engineering, and computational hydrology to address global water challenges.

Alfred SteinView profile

Professor

• Spatial Statistics
• Image Analysis
• Remote Sensing
• +7 more

Prof. Alfred Stein is a Full Professor in Spatial Statistics and Image Analysis at the Department of Earth Observation Science, Faculty ITC, University of Twente. He earned his MSc in Mathematics and Information Science from Eindhoven University of Technology and a PhD in Spatial Statistics from Wageningen University. His career spans roles at Wageningen University (1988–2002), ITC (2002–present), including leadership positions as department head, vice-rector research, and portfolio holder for education. Education: MSc (Eindhoven University of Technology), PhD (Wageningen University) Leadership: Department Head (Earth Observation Science), Vice-Rector Research (2008–2012), Portfolio Holder Education (2012–) His research focuses on Spatial and Spatio-Temporal Statistics , emphasizing Bayesian inference , data quality , image analysis , and fuzzy techniques . Key application domains include agriculture, health, urban land use, coastal systems, hazards, and wildlife. He has mentored over 30 PhD students since 1998, with 11 currently under supervision. Recent research trends highlight AI-driven remote sensing for glacier mapping, urban livability, and disease modeling. Publications span Deep Learning for SAR tomography, Bayesian hierarchical models for health data, and multitemporal SAR analysis for environmental monitoring. Awards include the Best Paper Award (2019) and ISARA Founder's Award (2020) . Scientific Awards Best Paper Award (2019) ISARA Founder's Award (2020) As Editor-in-Chief of Spatial Statistics and associate editor for multiple journals, he leads academic discourse. Collaborations include the University of Cape Town and University of Pretoria as Honorary Professor. His work contributes to UN Sustainable Development Goals, particularly in climate action and sustainable cities.

Ayesha AliView profile

Professor

• Graphical Markov models
• Pollination networks
• Multivariate statistics
• +7 more

Ayesha Ali is a Professor of Statistics and Director of the Master of Data Science program at the University of Guelph. She holds a PhD in Statistics from the University of Washington (2002) and has expertise in statistical methods for complex high-dimensional systems, including ecological networks, causal inference, and bioinformatics. Her research integrates graphical Markov models, machine learning, and statistical computing to address challenges in plant-pollinator networks, livestock genetics, and disease risk modeling. Education: B.Sc. Honours in Statistics and Actuarial Science, University of Western Ontario (1996) M.Sc. in Statistics, University of Toronto (1998) Ph.D. in Statistics, University of Washington (2002) Research Interests: Graphical Markov models and ecological networks Causal inference and longitudinal data analysis Machine learning and high-dimensional predictive modeling Statistical methods for livestock genetics and animal health Computational statistics and bioinformatics Articles Trends: Her recent work spans interdisciplinary applications, including veterinary oncology biomarker discovery, remote sensing for agricultural suitability, and pipeline development for cross-species transcriptomics. She emphasizes graphical structure exploitation in regression and predictive modeling, with contributions to both theoretical and applied statistical methodologies. Awards: Canadian Journal of Statistics Award (2020) for groundbreaking work on doubly sparse regression NSERC Discovery Grant (2018) NSERC Collaborative Research and Development Grant (2015) Advising & Grants: She has supervised numerous graduate and undergraduate students on projects ranging from plant-pollinator network analysis to bioinformatics. Her grants include NSERC-funded research on milk fatty acid genetics and statistical methods for clustered data. Labs/Teams: Involved in the Bioinformatics program at the University of Guelph, contributing to interdisciplinary research collaborations in ecology and animal science.

• Natural Language Processing
• Machine Learning
• Medical Language Models
• +4 more

Syrielle Montariol is a Researcher and Course Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Natural Language Processing Lab (NLP) under the School of Computer and Communication Sciences (IC). She holds a postdoctoral position and teaches courses related to computational linguistics and AI applications. Her research focuses on advancing NLP, medical language models, multimodal learning, and AI ethics. She works in the INR 240 office and maintains collaborations across EPFL's academic divisions. Research Interests: Her work spans interpretability of AI systems, cross-modal reasoning, medical domain adaptation, sustainability text analysis, and the societal impact of AI. Recent projects include developing explainable models (e.g., global mixture-of-experts frameworks) and benchmarking tools like Vinabench for visual narratives. Publications: Her recent work addresses critical challenges in AI, including vulnerability of higher education to LLMs, medical language model adaptation (Meditron), and robust geo-localization systems. Key themes include ethical AI, multimodal learning, and domain-specific NLP applications. Labs & Teams: She contributes to the NLP lab's initiatives on visual-language models and collaborates with interdisciplinary teams on projects like PAN-RSVQA for remote sensing and PICLe for low-resource NER systems.

Jochen LinßenView profile

Professor

• Energy Systems
• Renewable Energy
• Hydrogen Technology
• +3 more

Prof. Dr.-Ing. Jochen Linßen is a Professor at the Institute of Climate and Energy Systems (ICE) , specifically within the Jülich Systems Analysis (ICE-2) group at the Research Center Jülich GmbH. His work focuses on advancing energy systems modeling, renewable energy integration, and hydrogen technology. Key research areas include the techno-economic analysis of energy transition pathways, hydrogen cost-potentials, and the interplay between energy systems and transportation networks. His expertise spans climate policy, infrastructure planning, and the development of frameworks for long-term mobility demand modeling. He has contributed to high-resolution assessments of renewable energy resources and the evaluation of green hydrogen’s role in decarbonizing industries. Recent work emphasizes AI-driven risk mitigation in energy systems and the impact of natural hazards on energy infrastructure resilience. Research Interests: Modeling of energy systems and hydrogen networks Renewables integration and curtailment challenges Transportation electrification and hydrogen fueling infrastructure Climate policy and decarbonization strategies Key Projects: ETHOS. MODE. regional framework for mobility demand analysis Global assessments of wind and solar power potentials Hydrogen supply chain optimization for Germany and Europe His collaborative approach bridges engineering, economics, and environmental science to address systemic challenges in energy transitions. Ongoing efforts include participatory mapping of green hydrogen potentials in sub-Saharan Africa and analysis of automated driving’s impact on road networks.

Xin ZhangView profile

Assistant Professor

• Smart/Digital Agriculture
• Artificial Intelligence (AI) in Agriculture
• Computer Vision
• +5 more

Dr. Xin Zhang is an Assistant Professor in the Department of Agricultural and Biological Engineering at Mississippi State University. His research focuses on smart agriculture, AI applications, agricultural robotics, and unmanned aerial systems. He leads the Sensing & Automation in Agrisystems (SAAS) Lab, which develops technologies for crop monitoring, robotic harvesting, and machine vision systems. Dr. Zhang holds a Ph.D. from Washington State University and has held postdoctoral positions at UC Davis. He is a FAA-certified UAS pilot and actively collaborates on projects involving machine learning, computer vision, and precision agriculture. Research interests include digital agriculture frameworks, crop prediction models, and the integration of AI into farming systems. Key achievements include pioneering work in robotic cotton-picking simulations and UAV-based yield estimation. His lab emphasizes interdisciplinary approaches combining robotics, data science, and environmental engineering. Awards include the 2020 Giuseppe Pellizzi Prize for agricultural engineering research and multiple ASABE accolades. Current projects involve scalable field extraction frameworks using satellite imagery and deep learning models for plant phenotyping. He has published over 50 peer-reviewed articles, with a focus on advancing automation in crop handling and environmental sensing technologies. Education: Ph.D., Biological & Agricultural Engineering (WSU), M.S., Agriculture (Northwest A&F University), B.S., Agronomy (Gansu Agriculture University).

Stephen J. LeiszView profile

Professor

• Land Change Science
• Remote Sensing
• Geographical Information Systems (GIS)
• +4 more

Stephen J. Leisz is a Professor in the Department of Anthropology and Geography at Colorado State University (CSU). As a leading scholar in land change science and remote sensing, he directs the Land Change Science and Remote Sensing Laboratory and co-directs the Center for Archaeology and Remote Sensing. His research integrates remote sensing, GIS, and participatory methods to study human-environment interactions in Southeast Asia, Melanesia, and West Africa. Key areas include land-use transitions, peri-urbanization, agricultural systems, and climate change impacts. Leisz holds a Ph.D. in Geography from the University of Copenhagen (2007), an M.Sc. in Environmental Monitoring from the University of Wisconsin-Madison, and a B.A. in American Studies from Georgetown University. Leisz’s recent work focuses on agrarian transitions in Vietnam’s Mekong and Red River Deltas, impacts of transportation corridors on rural transformations, and applications of LiDAR in archaeology. He has led projects funded by NASA’s Land Cover/Land Use Change program and co-founded the Earth Archive Initiative to digitally preserve global landscapes. His fieldwork spans over 30 years in Southeast Asia and Melanesia, emphasizing interdisciplinary collaboration and policy-relevant outcomes. Leisz advises graduate students in CSU’s International Development Studies program and the Graduate Degree Program in Ecology (GDPE). He teaches courses on remote sensing, spatial analysis, and land change science. His career includes Peace Corps service in Senegal (1980s) and recognition as a First-Generation College Graduate.

Philipp LensingView profile

Professor

• Virtual Reality
• Augmented Reality
• Mixed Reality
• +5 more

Prof. Dr.-Ing. Philipp Lensing serves as a Professor in the Faculty of Engineering and Computer Science at Osnabrück University of Applied Sciences. His academic work focuses on cutting-edge developments in virtual and augmented reality systems, computer graphics, and game programming. His research interests span Virtual Reality , Augmented Reality , Mixed Reality , Game Programming , Computer Graphics , and Natural User Interfaces . Prof. Lensing has pioneered work in real-time global illumination techniques, avatar calibration systems, and the integration of virtual content with real environments. His research has been applied across diverse domains including landscape planning, physics education, medical rehabilitation, and industrial engineering. Prof. Lensing's recent publications reveal a strong trend toward practical applications of VR/AR technologies in scientific, educational, and industrial contexts. His work increasingly focuses on multimodal interaction, haptic feedback systems, and the integration of VR with complex scientific instrumentation like scanning probe microscopy. He has supervised numerous student projects focused on VR/AR applications, game development, and 3D modeling. His teaching includes courses on Computer Graphics, 3D Game Programming, Virtual and Augmented Realities, and 3D Modeling and Animation. Prof. Lensing leads several research projects including GROWTH (funded by BMBF), VRnano (BMBF), VRFlow Suite, VR-Physio-BOX, and MoDal-MR, all exploring innovative applications of immersive technologies in various practical contexts.

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.