Find Academic Supervisors

Dionissios T. HristopulosView profile

Professor

• Geostatistics
• Spatial Random Fields
• Spartan Spatial Random Fields
• +15 more

Dionissios T. Hristopulos is a Professor and Head of the Geostatistics Laboratory at the School of Mineral Resources Engineering, Technical University of Crete, Greece. His research spans geostatistics, spatial random fields, environmental modeling, stochastic hydrology, and porous media mechanics. He has developed innovative Spartan Spatial Random Field (SSRF) models rooted in statistical physics, enabling efficient spatial interpolation and simulation. PhD in Physics, Princeton University (1991) MA in Physics, Princeton University (1988) Diploma in Electrical Engineering, National Technical University of Athens (1985) Hristopulos' research interests focus on the development and application of geostatistical methods in mineral resources, environmental monitoring, petroleum reservoirs, and GIS. He investigates spatial anisotropy, groundwater dynamics, earthquake return times, and the mechanical properties of heterogeneous materials. His work bridges statistical physics and geostatistics, particularly through SSRF models and renormalization group methods for upscaling transport properties in porous media. The most recent publications highlight his focus on geometric anisotropy detection in environmental data, non-parametric estimation methods, and applications of Spartan random fields in environmental time series and spatial data interpolation. His work increasingly integrates machine learning concepts with geostatistical modeling, especially for automatic mapping and ecological monitoring using remote sensing. Marie Curie success story (European Commission, 2010) for SPATSTAT project Hristopulos has secured research funding from national and EU programs, including the Marie Curie Transfer of Knowledge (SPATSTAT) and the INTAMAP STREP project. He has mentored several Master’s and PhD students, including Manos Varouchakis (PhD candidate on groundwater monitoring) and Manolis Petrakis (Master’s on anisotropy characterization). He collaborates with researchers in the US, France, Slovakia, and the UK. He serves on the editorial board of Stochastic Environmental Research and Risk Assessment and has contributed software tools for anisotropy detection in MATLAB and R. His research group, the Geostatistics Laboratory at TUC, focuses on machine learning and geostatistics, developing computational tools for environmental data analysis, spatial interpolation, and simulation. The lab emphasizes practical applications in hydrology, ecology, and mineral resources, supported by strong theoretical foundations in statistical physics and stochastic modeling.

• Data Science
• Geometric Morphometrics
• Disease Modeling
• +3 more

Dr. Kenneth Soda, Jr. serves as Assistant Professor of Mathematics and Data Science at Quinnipiac University's College of Arts & Sciences, where he has taught since 2019. His academic journey spans biology, computational science, and data applications. Education background: B.S. in Biology, Western Washington University (2009) M.A. in Biology, SUNY Stony Brook (2011) M.S. and Ph.D. in Computational Science, Florida State University Post-Doctoral Researcher, University of Notre Dame (2017-2019) His research integrates data science with biological applications , specializing in geometric morphometrics (statistical analysis of biological shapes) and disease modeling. Current projects examine ecological community structures through Euclidean process distances and pandemic response frameworks. As moderator of the international Morphmet listserv, he facilitates global collaboration in morphometric research. Recent publications demonstrate methodological innovation across epidemiology and ecology , with 2020-2025 works combining serological data, agent-based modeling, and spatial statistics to address vector-borne diseases and pandemic management. His work bridges theoretical computation with real-world public health challenges. Teaching leadership: He designs curriculum for Quinnipiac's Data Science Program and teaches courses including DS 110 (Introduction to Data Science), DS 310 (Algorithms for Data Science), and MA 285 (Applied Statistics), emphasizing career-relevant computational and statistical skills.

• Risk policy
• Disaster and emergency management
• Sociology of inequalities
• +7 more

Dr. Mara Torres Pinedo serves as a Lecturer (Teaching) at the Institute for Global Prosperity within the Bartlett Faculty of the Built Environment at University College London (UCL). She has held this position since 2023, having previously served as an Associate Lecturer (2021-2022) and Postgraduate Teaching Assistant (2017-2020) at UCL. For the 2024-2025 academic year, she shares her teaching time between the Institute for Global Prosperity and the UCL Arena Centre for Research-Based Education. Prior to her academic career, she worked for over eight years in international development practice across Mexico, the US, and Latin America. Her educational background includes: PhD in Social Science from University College London (2017-2023) MSc in Risk, Disaster and Resilience from University College London (2015-2016) PG Cert in Project Management from Western Institute of Technology and Higher Education (ITESO), Mexico (2010) BSc in International Relations from Western Institute of Technology and Higher Education (ITESO), Mexico (2004-2008) Dr. Torres Pinedo's research takes an interdisciplinary approach to urban inequalities and governance of social-ecological systems in the global south. Her work spans risk policy, disaster management, human geography, and development studies, with particular focus on intersectional approaches to vulnerability. Recently, she has expanded into research-based education and decolonizing teaching practices in higher education. She is currently co-leading a research project on syllabus diversity and inclusion to improve student experience in intercultural post-graduate education. Her research methodology often employs mixed methods to develop holistic understandings that can inform more inclusive risk management systems. Her recent publications demonstrate a consistent focus on transformative education, disaster risk governance, and methodological innovations. The 2023 article "Rethinking Transformative Education" explores community-based research practices, while her dissertation examines network governance in disaster risk management through an intersectional lens. Earlier works include methodological contributions to data analysis and spatial-temporal media analysis, showing her interdisciplinary approach that bridges social sciences with quantitative methods. Dr. Torres Pinedo has extensive teaching experience across multiple UCL programs. She serves as Module Convenor for "Research Methods for Natural and Planetary Prosperity" and "Dissertation in Prosperity, Innovation and Entrepreneurship," and provides guest lectures on various topics including urban futures, transformative entrepreneurship, and research methods. At the UCL Arena Centre, she teaches staff development programs for teaching assistants, postdoctoral researchers, and early career academics. Her teaching approach emphasizes research-based education and critical pedagogies that address urban inequalities and social-ecological systems. Her academic work bridges theory and practice, informed by her eight years of experience in international development. Fluent in English and Spanish, she brings a valuable Latin American perspective to her research on Mexico City and broader Latin American contexts. Her current projects focus on creating more inclusive educational spaces through syllabus diversification and advancing understanding of how institutional networks influence vulnerability to disaster risk.

Benjamin S. BaumerView profile

Professor

• Data Science
• Sports Analytics
• Network Science
• +5 more

Benjamin S. Baumer is a Professor in the Statistical & Data Sciences program at Smith College , where he has been teaching since 2012. He previously served as the first full-time Statistical Analyst for the New York Mets from 2004 to 2012, pioneering data-driven approaches in baseball operations. Education: Ph.D. in Mathematics, The Graduate Center of the City University of New York (2012) M.A. in Mathematics, University of California, San Diego B.A. in Mathematics, Wesleyan University His research spans multiple domains: Sports Analytics (sabermetrics, player evaluation, game simulation) Data Science Education (curriculum development, reproducible analysis tools) Network Science (graph theory, complex systems) Statistical Computing (R programming, tidyverse workflows) Recent publications highlight: Advancements in reproducible analysis (infer package, 'Fertile' framework) Educational innovations in team-based data science and ethics integration Tools for election data (fec16 package) and collaborative learning (Slack applications) Scientific Recognition: 2016 Contemporary Baseball Analysis Award (SABR) 2019 Waller Education Award (ASA) 2019 Significant Contributor Award (ASA Section on Statistics in Sports) Accredited Professional Statistician™ since 2014 (American Statistical Association) He co-organizes the annual ASA Five College DataFest and contributes to expanding data science pathways for community college students.

Mojtaba Naghdyzadegan JahromiView profile

Researcher

• Crop Modeling
• Remote Sensing
• Machine Learning
• +3 more

Mojtaba Naghdyzadegan Jahromi is a postdoctoral researcher at the University of Oulu in the Hydrosystems Engineering & Management (HEM) research group within the Water, Energy, and Environmental Engineering Research Unit . He holds a Ph.D. in Agriculture-Water Engineering from Shiraz University (2023) and has previously worked at Shiraz University, Ghent University, and Georg-August University of Göttingen. B.Sc. and M.Sc. in Water Engineering , Shiraz University Ph.D. in Agriculture-Water Engineering , Shiraz University His research focuses on crop modeling , remote sensing , and machine learning for agricultural and environmental applications. Recent work includes wheat yield prediction , inland water body depletion analysis , and precision agriculture techniques. Publications span journals like European Journal of Agronomy and Water , book chapters on remote sensing and machine learning, and conferences including EGU and Tropentag. Ph.D. Student Travel Grant (2021), Ministry of Science, Iran Student Travel Grant (2017), ICTP, Italy Tropentag 2015 Grant Award He has contributed to interdisciplinary projects involving Google Earth Engine , MODIS data , and GLEAM evapotranspiration models , with applications in water resource management and climate change impact assessment.

• Development Economics
• Measurement of Poverty and Inequality
• Applied Microeconometrics
• +4 more

David Garces Urzainqui is a Postdoctoral Research Fellow at the Department of Economics, University of Copenhagen. His work focuses on development economics, poverty dynamics, and inequality measurement. Current Affiliation: Department of Economics, University of Copenhagen Research Group: Development Economics Research Group (DERG) Research Interests : Development Economics Poverty Dynamics and Vulnerability Applied Microeconometrics Social Networks and Spatial Inequality Tax Policy Impact Analysis Geospatial Poverty Mapping Recent Work Trends : His publications analyze poverty transitions in South Asia, tax reforms in Africa, and spatial inequality patterns using synthetic panel methods and geospatial techniques. Contact : dagu@econ.ku.dk

Kiarri N KershawView profile

Professor

• Social Epidemiology
• Health Disparities
• Maternal Health
• +4 more

Kiarri N Kershaw is the Eileen M. Foell Professor and a full Professor in the Department of Preventive Medicine (Epidemiology) at Northwestern University Feinberg School of Medicine. She is affiliated with multiple institutes including the Center for Diabetes and Metabolism, Institute for Public Health and Medicine (IPHAM), Northwestern University Clinical and Translational Sciences Institute (NUCATS), and Simpson Querrey Institute for Epigenetics. Dr. Kershaw holds advanced degrees including a Master of Science in Epidemiology from University of Michigan (2004) Master of Public Health from University of Michigan (2006) PhD in Epidemiology from University of Michigan (2010) She completed postdoctoral training in cardiovascular epidemiology at Northwestern University Feinberg School of Medicine (2012). As a social epidemiologist, her research focuses on structural and social drivers of health inequities, particularly examining Racial residential segregation Discrimination and chronic stress Maternal and cardiovascular health disparities Geospatial and statistical methodologies Community-led interventions She employs advanced geospatial analysis and primary data collection through ecological momentary assessments to study how environments influence health outcomes. Her work includes significant clinical trials like the DREAM Study addressing Black maternal mortality disparities and NIH-funded research on stress-environment interactions. Recent publications demonstrate expertise in Cardiovascular risk in segregated neighborhoods Discrimination's physiological impacts Maternal health determinants Longitudinal cohort analysis Behavioral and biological risk factors Scientific recognition includes: 2022 Fellow of Academy of Behavioral Medicine Research 2017 Scott Grundy Fellowship 2014 Sandra Daugherty Award She serves as Chair, AHA Social Determinants of Health Committee (2022-2024) Vice Chair, AHA Committee (2020-2022) Chair, Psychosocial Working Group, CARDIA Study (2015-Present) and contributes to editorial boards including the American Heart Association Journals Equity Diversity and Inclusion Board. Dr. Kershaw leads the Social Environment and Health Lab and collaborates with community organizations to combat health inequities through participatory research.

Kaiguang ZhaoView profile

Associate Professor

• Remote sensing
• GIS
• Spatial modeling
• +7 more

Kaiguang Zhao is an Associate Professor of Environmental Modeling and Spatial Analysis at The Ohio State University's School of Environment and Natural Resources. His research focuses on mapping, monitoring, modeling, and managing terrestrial environments across scales, especially in the context of global environmental changes. He employs a combined toolset including geotechnology, spatial analysis, machine learning, biophysical & climate modeling, ecological modeling, Bayesian statistics, and eddy-covariance to characterize ecosystem status and changes. Ph.D. in Forestry, Texas A&M University, 2008 M.S. in Geography, Beijing Normal University, 2004 B.S. in Physics, Beijing Normal University, 2001 Dr. Zhao's research interests span remote sensing & GIS, spatial modeling, data analytics, environmental modeling, climate change, and machine learning applications to environmental systems. His work particularly emphasizes developing geospatial applications using hyper spectral imaging, high-resolution imagery, hyper-temporal remote sensing, and lidar to characterize ecosystem structure and functioning. His research addresses critical questions about carbon storage in forests, ecosystem disturbance patterns, vegetation community mapping, climate change impacts on vegetation activities, and climate regulation services from forestry and land-use activities. His extensive publication record demonstrates strong trends in applying advanced statistical and machine learning methods to environmental remote sensing data. His work increasingly focuses on integrating multiple data sources and developing novel algorithms for environmental monitoring, with particular emphasis on crop modeling, water quality assessment, carbon cycling, and climate change impacts. The research shows a clear trajectory toward more sophisticated modeling approaches that combine physical principles with data-driven techniques. 2022 Best Paper award for 'Mapping crop types in fragmented arable landscapes using AVIRIS-NG imagery and limited field data' published in the International Journal of Image and Data Fusion Dr. Zhao actively seeks self-motivated graduate students with quantitative and computational backgrounds to apply their skills to environmental issues in natural or human-dominated ecosystems. His research has been supported by numerous grants focused on environmental monitoring, climate change impacts, and sustainable resource management. While specific grant details aren't listed in the provided text, his extensive publication record suggests substantial research funding across multiple environmental domains. His work involves collaboration with various research teams and laboratories focused on remote sensing, environmental modeling, and climate change research. He has developed significant computational tools including the Rbeast package for Bayesian Change-Point Detection and Time Series Decomposition, indicating leadership in developing methodological approaches for environmental data analysis.

• Air Pollution and Health
• Sustainable Cities
• Agent-Based Modelling
• +6 more

Tabea Sonnenschein is a Researcher at Utrecht University , affiliated with the Faculty of Geosciences and Human Geography and Spatial Planning . She is also a PhD Candidate in the Department of Population Health Sciences under the Faculty of Veterinary Medicine . Her work bridges environmental science, urban planning, and public health through advanced computational modeling. Coordinated EU-funded EXPANSE (Horizon 2020) and EXPOSOME-NL (NWO) projects. Research Associate at the MRC Epidemiology Unit at University of Cambridge, contributing to DARe Hub and UBD Policy initiatives. Research Interests Air Pollution and Health: Modeling health impacts of urban pollutants. Agent-Based Simulation: Developing tools like GenSynthPop and CellAutDisp . Urban Sustainability: Assessing interventions for resilient, low-emission cities. Recent Publications highlight her work on urban exposome, synthetic population modeling, and subway expansion impacts. She contributes to journals like Environmental Modeling and Software , Autonomous Agents and Multi-Agent Systems , and Semantic Web .

Günter SchneckenreitherView profile

Researcher

• Computational Statistics
• Epidemiological Modeling
• Agent-Based Simulation
• +3 more

Günter Schneckenreither is a researcher affiliated with the Computational Statistics research area at Technische Universität Wien (TU Wien). His work spans interdisciplinary applications of computational methodologies to epidemiology, public health, and biomedical modeling, with a focus on agent-based simulations and cellular automata. Research Interests: Heterogeneity in disease transmission, age-dependent SIR models, synthetic data generation for pandemics, and fractional diffusion processes in structured populations. Publications: Recent research includes epidemiological modeling of SARS-CoV-2 immunization levels, melanocytic proliferation simulations, and comparative studies of epidemic modeling frameworks. Projects: Active participant in ARGESIM benchmark studies, integrating cellular automata and differential equations for epidemic simulations. Methodologies: Specializes in dynamic causal modeling, fractal topological structures, and multiplex social network analysis.

Mirosław BełejView profile

Professor

• Real Estate Economics
• Housing Market Analysis
• Spatial Distribution
• +5 more

Mirosław Bełej is a Professor at the University of Warmia and Mazury in Olsztyn, affiliated with the Faculty of Geoengineering and the Department of Real Estate and Urban Studies within the Institute of Spatial Management and Geography. His academic profile demonstrates extensive contributions to social and economic geography and spatial management with 84 publications and an h-index of 9 (Scopus). Professor Bełej's research primarily focuses on housing market dynamics, property valuation, and spatial analysis of real estate markets across Poland. His research spans multiple dimensions of real estate economics with particular emphasis on housing price dynamics using innovative approaches including catastrophe theory, harmonic oscillations, and Google Trends data analysis. He has extensively studied how factors like airport proximity, points of interest, and urban amenities affect housing markets, with growing attention to pandemic impacts and sustainable development considerations. His methodological contributions bridge geography, economics, and data science to provide comprehensive insights into real estate market behavior. Analysis of Professor Bełej's recent publications reveals a strong focus on spatial analysis using advanced geospatial techniques with increasing incorporation of digital footprints like Google Trends. His work shows clear progression from theoretical models toward practical applications in specific Polish regions, consistently addressing how external factors influence property values. The interdisciplinary nature of his research demonstrates innovation in methodology while maintaining practical relevance for urban planning and policy. Professor Bełej has achieved significant bibliometric recognition with an h-index of 9 according to Scopus citations and 8 according to Web of Science. His publications have accumulated a total impact factor of 36.864 and a ministerial score of 2,237, indicating substantial scholarly impact in his field. With 50 promoted theses, Professor Bełej has mentored numerous graduate students in real estate economics and spatial management. Though only one project is explicitly mentioned in his profile, his extensive publication record suggests involvement in multiple research initiatives across disciplinary boundaries relevant to urban planning, economic policy, and geographic information science. His research likely involves interdisciplinary collaboration within the Institute of Spatial Management and Geography, combining expertise in geography, economics, and data science. Publications indicate engagement with both national and international scholarly communities in real estate research, with applications relevant to urban planning and property valuation practices.

Evelyn WallorView profile

Lecturer

• Forest Information Technology
• Statistics
• Environmental Data Analysis
• +2 more

Dr. Evelyn Wallor is a Lecturer and the academic coordinator of the Master’s programme Forest Information Technology (FIT) at the University for Sustainable Development Eberswalde (HNEE), Germany. She is based at the Waldcampus in Eberswalde and is responsible for the strategic and administrative oversight of the FIT curriculum, while also delivering statistics courses to graduate students. Her research and teaching interests revolve around the intersection of environmental sciences and information technology , with a particular focus on statistical methods applied to forest and environmental data. She supports students in acquiring robust quantitative skills necessary for analysing complex ecological and forestry datasets using modern geospatial and data-science tools. Office hours for statistical consultation are held every Tuesday and Thursday from 14:00 to 16:00 , providing personalised guidance to students and researchers.

Franz-Josef BehrView profile

Professor

• Spatial Data Infrastructures
• Geospatial Standards
• Open Source Geospatial Software
• +5 more

Prof. Dr.-Ing. Franz-Josef Behr serves as Senior Professor of Geospatial Information at Stuttgart University of Applied Sciences, specializing in Spatial Data Infrastructure (SDI), Standards, Open Source solutions, data modelling, and disaster mapping. His academic leadership includes serving as Study Dean for the Master Programme Photogrammetry and Geoinformatics (2009-2022) and founding the internationally recognized AGSE conference series focused on geoinformatics for societal and environmental challenges. Behr's research centers on geospatial interoperability, with expertise spanning SDI implementation, OGC standards evolution, and open source geospatial technologies. His work bridges theoretical frameworks with practical applications in disaster management, urban planning, and international development, particularly through capacity-building initiatives in Indonesia and India. Current investigations examine OpenStreetMap integration for official cadastres and modern API-based service architectures replacing traditional OGC web services. Recent publications reveal strong trends toward practical implementations of open standards in governmental systems, UAV data processing for developing nations, and disaster response applications. His work consistently emphasizes interoperability, open data principles, and technology transfer to address real-world challenges in geospatial information management. Scientific recognition includes: Best Poster Award at ISPRS Conference, Prague (2016) Academic leadership extends globally through roles as Co-chair of the ICA Commission on SDI and Standards (2017-2023), membership in ISO/TC 211 standards committees, and service on Kerala's Digital University Board of Studies. He has significantly advanced geospatial education through the AGSE conference network spanning 12 countries and numerous international workshops. As founder and chair of the AGSE conference series since 2009, Behr has cultivated a global network connecting academia, government, and industry across India, Peru, Kenya, Malaysia, Germany, Iran, Namibia, and Nepal. This initiative specifically focuses on applying geoinformatics to societal challenges including disaster management, sustainable development, and environmental monitoring through practical open-source implementations.

Lars BernardView profile

Professor

• (Geo-)Information Infrastructures
• Geoinformation Usability
• Distributed Geoprocessing
• +7 more

Lars Bernard is Full Professor for Geoinformatics at the Faculty of Environmental Sciences, Technische Universität Dresden since 2015. He also holds the position of Chief Digitalization and Information Management Officer (CDIO) at TU Dresden since 2020 and serves as member of the TUD Rectorate. PhD in Geoinformatics (University of Münster, 2001) Diploma in Physical Geography (University of Münster, 1995) His research focuses on (Geo-)Information Infrastructures , Distributed Geoprocessing , and Smart Environmental Monitoring . He has extensive experience in developing geospatial standards through roles in AGILE, EuroSDR, and INSPIRE initiatives. His publications demonstrate a consistent emphasis on: Metadata provision and data quality frameworks Interoperability solutions for geospatial systems Climate information service architectures User-driven provenance mechanisms As co-chair of RFII (2020-) and spokesperson for NFDI4Earth (2019-), he leads major German research data initiatives. He has advised numerous students and coordinated TU Dresden's Master Geoinformation Technologies program until 2018. Current leadership roles include serving on scientific editorial boards for Environmental Modelling & Software and International Journal of Spatial Data Infrastructures Research .

• Signal Processing
• Remote Sensing
• Geospatial Data Analysis
• +5 more

Alexandre Hippert-Ferrer is an Associate Professor at Université Gustave Eiffel, where he is a permanent member of the LASTIG lab working in the Strudel team. He primarily teaches at the National School of Geographic Sciences (École Nationale des Sciences Géographiques). Dr. Hippert-Ferrer received his PhD from University Savoie Mont Blanc, Annecy, France in 2020, supervised by Philippe Bolon and Yajing Yan. His doctoral research focused on reconstruction of missing data in displacement time series by remote sensing. Following his PhD, he was a post-doctoral researcher at L2S (Université Paris-Saclay) working with Florent Bouchard and Frédéric Pascal. He maintains active collaborations with researchers including Arnaud Breloy and Nabil El Korso from LEME at University of Paris Nanterre. Hippert-Ferrer's research focuses on robust statistical methods for signal processing, particularly for geospatial data with missing values. His work bridges the fields of remote sensing, signal processing, and statistics, with a strong emphasis on practical applications in earth observation and geodesy. He has developed innovative approaches using Empirical Orthogonal Function (EOF) analysis, Expectation-Maximization (EM) algorithms, and Riemannian geometry for handling incomplete data in displacement measurement time series. His methods have been applied to Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) data for monitoring ground displacement. His publication record shows a clear progression from basic gap-filling techniques to sophisticated statistical models. Starting with EOF-based approaches, his work has evolved to incorporate advanced methods including elliptical distributions, low-rank covariance estimation, and graphical factor models with Riemannian optimization. The consistent theme across his publications is developing mathematically sound yet practical algorithms for real-world geospatial data challenges, particularly when data is incomplete or corrupted. Dr. Hippert-Ferrer has made significant contributions through publications in top journals including IEEE Transactions on Signal Processing and IEEE Transactions on Geoscience and Remote Sensing. His open-source implementation of the EM-EOF algorithm on GitHub demonstrates his commitment to reproducible research. As an educator at the National School of Geographic Sciences, he trains the next generation of geospatial professionals, bridging theoretical statistical methods with practical applications in earth observation.