Find Academic Supervisors

Matthias KatzfussView profile

Professor

• Computational Spatial Statistics
• Spatio-Temporal Statistics
• Gaussian Processes
• +5 more

Matthias Katzfuss is a Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on computational spatial and spatio-temporal statistics, Gaussian processes, uncertainty quantification, and data assimilation, with applications in environmental science and satellite remote sensing. He has received funding from NSF, NASA, NOAA, USDA, Sandia National Laboratory, and Jet Propulsion Laboratory. His scientific achievements include awards such as the NSF Career Award, a Fulbright Scholarship, and the Early Investigator Award from the American Statistical Association’s Section on Statistics and the Environment. His work bridges theoretical advancements and real-world applications, particularly in environmental monitoring and large-scale data fusion. Recent research trends include scalable methods for high-dimensional spatial data, nonstationary covariance modeling, and Bayesian transport maps. His publications address challenges in remote sensing, climate science, and machine learning, emphasizing computational efficiency and methodological innovation. Matthias collaborates extensively with institutions like JPL and NASA, contributing to global-scale environmental studies. His lab focuses on developing open-source software tools, such as the GPgp package, to enable reproducible research in spatial statistics.

Hyunseung KangView profile

Associate Professor

• Causal Inference
• Instrumental Variables
• Econometrics
• +8 more

Hyunseung Kang is an Associate Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Educational Psychology (Quantitative Methods), Department of Biostatistics and Medical Informatics (BMI), Center for Demography and Ecology (CDE), and Center for Demography of Health and Aging (CDHA). He completed his PhD in Statistics at the Wharton School (2015), with postdoctoral training at Stanford University (2015-2016). Education: PhD in Statistics, University of Pennsylvania (2010-2015) MS in Statistics, Stanford University (2009-2010) Bachelor of Science in Mathematical and Computational Science, Stanford University (2006-2010) Research: Focuses on causal inference methods using instrumental variables, econometrics, semi/nonparametric techniques, network analysis, and machine learning. Applications span genetics, epidemiology, health policy, education, and microeconomics. Recent grants include support from NSF, NIH, and UW-Madison initiatives. Teaching: Teaches advanced courses like Causal Inference (Stat 992), emphasizing theoretical foundations and practical applications. Software: Developed R packages like ivmodel for instrumental variables analysis, published in Observational Studies . Actively contributes to open-source tools for causal inference. Awards: Recognized through grants and editorial roles, including Associate Editor for Biometrics and Journal of the American Statistical Association .

Raul Zurita-MillaView profile

Professor

• Geospatial Analytics
• Machine Learning
• Environmental Science
• +4 more

Raul Zurita-Milla is a Full Professor at the University of Twente, holding dual appointments in the Department of Geo-information Processing and the Digital Society Institute. His research focuses on spatiotemporal analytics, geospatial machine learning, and environmental modeling, with applications in public health, climate change, and sustainability. Key areas include wastewater-based epidemiology, phenological analysis, tick dynamics modeling, and open science practices. He co-founded the Open Science Community Twente in 2020, promoting reproducible research and collaborative frameworks. His expertise spans geoinformatics, machine learning, and environmental science, contributing to UN Sustainable Development Goals related to health, climate action, and biodiversity. Notable achievements include the 1st place TU Delft Water for Impact Best Paper Award (2024) and leadership in developing open-source tools like the CGC Python package for geodata clustering. His work integrates interdisciplinary approaches, blending computational methods with real-world environmental and societal challenges. Zurita-Milla’s research outputs include over 155 peer-reviewed publications and 16 datasets, emphasizing reproducibility and open science. Collaborations span global institutions, addressing topics such as false spring detection, agent-based disease modeling, and sustainable wastewater surveillance systems.

• Geostatistics
• Big Data Processing
• Computer Systems
• +6 more

Dr. John Zigman is an academic affiliated with the Faculty of Engineering at The University of Sydney. His research spans interdisciplinary fields including geostatistics, big data processing, and computer systems optimization. He has contributed to advancements in geological boundary modeling, memory management frameworks for hybrid systems, and Java performance analysis. Key research interests include: Geological modeling using Bayesian approaches and dynamic time warping Development of holistic memory management systems (e.g., Panthera framework) Optimization of parallel computing architectures and distributed garbage collection Integration of geophysical data with autonomous spatial modeling algorithms Recent work focuses on photonic integrated circuits (2025), Java performance re-engineering (2025), and large-scale geological boundary rectification (2023). His publications demonstrate expertise in both theoretical computer science and applied geosciences. Notable collaborations include work with Prof. Arman Melkumyan on geostatistical applications and Prof. Stephen Blackburn on persistent Java systems. His research bridges computational methods with earth sciences, addressing challenges in resource exploration and data-intensive systems.

Vincent van HinsbergView profile

Associate Professor

• Mineral-Fluid Interactions
• Geochemistry
• Subduction Zone Processes
• +2 more

Vincent van Hinsberg is Associate Professor and Osisko Faculty Scholar at McGill University, specializing in fluid-mineral interactions and experimental petrology. His laboratory develops methods to extract fluid composition data from mineral signatures. Research examines element cycling in subduction zones, volcanic fluid chemistry, and early Earth environments. Current projects focus on tourmaline as a geochemical recorder and metal transport in hydrothermal systems. He directs the FlexPet experimental facility simulating crust to mantle conditions and teaches metamorphic petrology and geostatistics. Fieldwork spans Greenland, Indonesia, and the French Massif Central.

Geoff NichollsView profile

Associate Professor

• Bayesian Inference
• Computational Statistics
• Monte Carlo Methods
• +4 more

Geoff Nicholls is an Associate Professor of Statistics at the University of Oxford, affiliated with St Peter's College as a Fellow and Tutor. He holds a B.Sc. in Physics from the University of Canterbury (New Zealand) and a PhD in High-Energy Physics from the University of Cambridge. His research focuses on Bayesian inference, computational statistics, and Monte Carlo methods applied to interdisciplinary problems in geoscience, linguistics, genetics, and archaeology. Notable contributions include semi-modular Bayesian inference frameworks and embedded diachronic sense change models for ancient languages. He has advised students like Lorenzo Pacchiardi and Jessie Jiang, and collaborates on tools like the TraitLab package for binary tree-like data analysis. Key research areas include statistical methods for partial orders, biclustering forensic data, and scalable Bayesian techniques for high-dimensional problems. His work bridges theoretical developments with practical applications, such as modeling historical power dynamics using 12th-century documents and analyzing linguistic evolution in ancient Greek texts.

• Statistical Genetics
• Biomedical Statistics
• Bayesian Inference
• +4 more

Dr. George Nicholson is a Postdoctoral Researcher in the Department of Statistics at the University of Oxford , specializing in statistical genetics , biomedical data analysis , and methodological innovation . His work bridges probabilistic modeling with real-world health applications. Research Focus: Development of advanced statistical methods for analyzing complex biomedical datasets, including longitudinal clinical trials, UK Biobank, and SARS-CoV-2 surveillance systems. Key Contributions: Creation of composable statistical inference frameworks, generalized Bayesian approaches for model misspecification, and hierarchical models for population differentiation studies. Methodological Expertise includes Bayesian networks , Markov chain Monte Carlo techniques , spatio-temporal modeling , and multivariate factor analysis . His recent publications address critical challenges in: Public Health: Designing robust methodologies for SARS-CoV-2 prevalence estimation through wastewater surveillance and clinical data integration Genomics: Characterizing genetic determinants of adiposity progression and metabolic disease trajectories Clinical Trials: Developing novel latent factor models for treatment response evaluation in autoimmune disorders

• Spatial data management
• Spatio-temporal statistics
• Machine learning in GIS
• +3 more

Felix Kunde is a Researcher at the Beuth University of Applied Sciences Berlin, contributing to the ExCELL and MAGDa projects. He holds a Bachelor's degree in Geography and a Master's degree in Geoinformation Science. His professional experience includes roles as a database developer for 3D city models and teaching geodatabase systems at universities. He actively participates in conferences such as FOSSGIS, FOSS4G, and WhereCamp, presenting work on topics like traffic forecasting, geospatial data management, and PostGIS extensions. Research interests focus on spatial data management (RDBMS/NoSQL/HDFS), spatio-temporal statistics, linear referencing, machine learning in GIS, and 3D city modeling. Current projects address short-term traffic forecasting with STARIMA/SVM/ANN methods, handling missing data in time series, and scalable geostatistical algorithms on Hadoop. He also works on open data models for traffic in OpenStreetMap and mobile databases using GeoPackage. Education: Bachelor of Geography Master of Geoinformation Science Key Contributions: Co-developer of pgMemento for spatial database auditing Contributor to the 3DCityDB project Lead researcher on ExCELL mobility platform Teaching: Lectures on geodatabases at multiple universities PostGIS workshops at conferences His publications span topics from geodatabase systems to traffic prediction algorithms, with notable work in spatial-temporal data analysis and geoinformatics applications. He advises multiple students on projects related to mobility analytics, data mining, and geospatial technology integration.

Tom RavensView profile

Professor

• Arctic Coastal Hazards
• Environmental Engineering
• Renewable Energy
• +3 more

Dr. Tom Ravens is a Professor in the Civil Engineering Department at the University of Alaska Anchorage (UAA), serving as former Associate Dean of Research for the College of Engineering. His research focuses on Arctic coastal hazards, including erosion, flooding, permafrost thaw, and saltwater intrusion, alongside hydrokinetic renewable energy. He leads the NSF-funded Arctic Coastal Risk Network (ACRN), a collaborative effort to address Arctic coastal risks through interdisciplinary approaches. Education: Ph.D., Civil and Environmental Engineering, MIT (1997) M.A., Philosophy, University of Massachusetts (1990) M.E. & B.E., Engineering Sciences, Thayer School of Engineering, Dartmouth College (1983) B.A., Engineering Sciences, Dartmouth College (1982) Research Interests: Development of Arctic Xbeach models and process-based forecasting Coastal protection strategies (thermal, mechanical, nature-based) Hydrokinetic energy resource assessment and environmental impacts Risk communication and community adaptation Teaching: Courses in Water Resources Engineering, Coastal Engineering, and Fluid Mechanics. Service: Fluid Mechanics Lab Director at UAA Reviewer for journals like Journal of Waterway, Port, Coastal, and Ocean Engineering Proposal evaluator for NSF and Sea Grant programs Labs/Teams: Leads the Arctic Coastal Risk Network and collaborates with interdisciplinary teams on coastal resilience projects.

• Economic Geology
• Structural Geology
• Tectonics
• +5 more

Associate Professor Ioan Sanislav is an academic at James Cook University (JCU), serving as Director of the Resources and Critical Minerals Trailblazer Projects. His research focuses on economic geology, structural geology, and tectonics with a particular emphasis on mineral deposit exploration, structural controls, and tectonic evolution. Current research interests include Proterozoic copper mineralization in northern Queensland, critical metals in NE Queensland, gold mineralization in the Archean Tanzania Craton, and REE/Nb/Ta mineralization in NE Egypt. He employs multidisciplinary approaches integrating geochemistry, field geology, and numerical modeling, collaborating closely with the mining industry on sponsored projects. Research Areas: Structural geology, metamorphic petrology, geochronology, and mineral exploration. Teaching: Lecture and coordinate Structural Geology and Tectonics (EA5211) at JCU. Projects: Lead initiatives like the North Queensland Proterozoic Metallogenesis and Critical Minerals Trailblazer Programs. Dr. Sanislav has advised numerous PhD students on topics such as ductile shear zones, sulfide mineral geochemistry, and seismic interpretations of mineral systems. He has authored over 60 peer-reviewed articles, with recent work emphasizing geochemical anomaly detection and crustal evolution studies. His collaborations with industry ensure applied relevance to resource exploration challenges.

Stefano PelusoView profile

Associate Professor

• Bayesian Statistics
• Network Analysis
• Spatio-temporal Modeling
• +3 more

Stefano Peluso is an Associate Professor in the Department of Statistics and Quantitative Methods at the University of Milano-Bicocca. His academic appointments include: Current: Associate Professor, University of Milano-Bicocca Former: Assistant Professor, Catholic University of the Sacred Heart (2015-2020) Peluso's research centers on Bayesian statistics and network analysis with applications in epidemiology and biostatistics. He develops advanced spatio-temporal models for public health crises, specializing in dependency structures in mixed-type data and longitudinal networks. His methodological innovations bridge machine learning and statistical inference to address pandemic forecasting and cardiovascular event prediction, supporting evidence-based healthcare decision-making. His 2024 publications reveal a cohesive research trajectory focused on Bayesian network structures for dependency learning and spatio-temporal prediction. Five articles demonstrate consistent application of these methods to real-world health challenges—from COVID-19 forecasting using neural networks to cardiovascular event modeling—highlighting an interdisciplinary approach that transforms complex health data into actionable public health insights during emergencies. Peluso leads significant research initiatives funded by the Lombardy Region and Italian Ministry of University and Research, including the 'InPreSa' project for SARS-CoV-2 detection and Bayesian network models for pandemic risk forecasting. These projects reflect his commitment to deploying statistical science against societal threats, though no student advising details appear in the available records.

• Hydrology
• Hydraulic Engineering
• Water Resources Management
• +3 more

Alberto Bellin is Full Professor at the University of Trento's Department of Civil, Environmental and Mechanical Engineering. His research focuses on hydrological processes in heterogeneous environments, including transport phenomena, water resources management under climate change, and subsurface-surface water interactions. He holds a Ph.D. in Hydrodynamics from the University of Padua (1992) and a Civil Engineering degree magna cum laude from the same institution (1987). His academic career includes visiting positions at UC Berkeley and leadership roles including Head of the PhD School in Environmental Engineering and Head of the Department of Civil and Environmental Engineering. Bellin's research investigates flow and transport in complex hydrosystems, climate change impacts on water resources, and integrated modeling approaches. His work combines stochastic methods with field observations to address water security challenges in Alpine regions. Recent studies examine climate change impacts on the Water-Energy-Food-Ecosystem nexus and develop decision-support tools for managed aquifer recharge. His publications demonstrate consistent focus on improving hydrological prediction capabilities through advanced modeling techniques, with recent emphasis on climate-hydrology interactions and sustainable water management solutions. Bellin leads multiple EU-funded research consortia including GLOBAQUA and CLIMB projects. He established and leads the Laboratory of Hydraulic Engineering at the University of Trento, supervising doctoral candidates and postdoctoral researchers. Current projects investigate climate adaptation strategies for Alpine watersheds and develop early-warning systems for hydrological extremes.

Jesse CrawfordView profile

Associate Professor

• Statistics
• Probability Theory
• Mathematics Education
• +3 more

Dr. Jesse Crawford is an Associate Professor in the Department of Mathematics at Tarleton State University. His roles include teaching courses such as Probability & Statistics, Calculus, Foundations of Mathematics, and Statistical Models. He actively engages in research on climate modeling, environmental statistics, and hydrology, with publications in journals like the Journal of Hydrology . His work also addresses issues like nitrate contamination from CAFOs and amphibian population dynamics. Dr. Crawford's academic contributions span both teaching and interdisciplinary research, with a focus on applying statistical methodologies to real-world problems. Teaching Focus: Probability theory, statistical modeling, calculus, and mathematical foundations. Research Interests: Climate variability modeling, environmental risk assessment, and statistical applications in ecological systems. He has presented at conferences including the Mathematical Association of America Texas Section meetings and published collaborative studies on ensemble climate models and agricultural impacts on groundwater systems. Office hours are held MWF 9:00-9:50 AM, and he can be reached at jcrawford@tarleton.edu for academic inquiries.

Alessio FarcomeniView profile

Professor

• Statistics
• Statistical Methodology
• Data Science
• +5 more

Alessio Farcomeni is a Professor of Statistics at the University of Rome Tor Vergata, specializing in statistical methodology development. His work bridges academia and applied research across disciplines including economics, medicine, ecology, and engineering. He has authored/co-authored over 250 peer-reviewed papers and two books, with notable contributions in hidden Markov models, Bayesian analysis, and quantitative social science measurement. His research emphasizes interdisciplinary collaboration, reflected in studies on population health, economic policy evaluation, and surgical outcome optimization. Farcomeni’s research interests span statistical theory, with a focus on methodological innovations for complex data structures (e.g., longitudinal, spatial, and high-dimensional datasets). He has pioneered approaches in quantile regression, semi-Markov processes, and latent variable modeling. His applied work addresses real-world challenges such as estimating material deprivation scales, modeling cardiovascular disease risk factors, and analyzing pandemic dynamics (e.g., COVID-19 forecasting in Italy). His recent articles highlight advancements in statistical techniques for healthcare (e.g., AI-driven dermatology diagnostics, atrial fibrillation prediction models) and socio-economic analysis (e.g., macroprudential policy impacts, cross-country material deprivation comparisons). He is recognized for developing open-source statistical software packages, contributing to reproducible research practices. Farcomeni holds the distinction of being ranked among Italy’s top scientists by VIA-Academy. His work frequently integrates Bayesian methods, machine learning, and big data analytics to address pressing questions in public health, environmental science, and economic policy.

• Geology
• Paleontology
• Sedimentology
• +5 more

Dr. Mo Snyder is an Assistant Professor in Earth and Environmental Science at Acadia University. Their research focuses on salt tectonics, soft-sediment deformation, geoscience education, and accessibility in Earth Science. They teach courses such as Field Methods, Sedimentary Geology, and Structural Geology & Tectonics. Dr. Snyder holds a PhD (2019) and BSc (2010) in Geology/Paleontology from the University of Alberta. Education: PhD in Geology, University of Alberta, 2019 BSc (Specialization in Paleontology), University of Alberta, 2010 Research Interests: Exploring deformation mechanisms in sedimentary basins (e.g., Bay St. George sub-basin) Advocating for inclusive fieldwork practices through initiatives like bathroom culture reform Integrating field-based teaching with accessibility considerations Characterizing salt tectonics and evaporite dynamics in Atlantic Canada Recent publications emphasize both technical geological studies (e.g., salt tectonics in strike-slip basins) and educational outreach, including open-access textbooks and fieldwork equity initiatives. Dr. Snyder actively participates in professional service roles including the Atlantic Geoscience Society Education Committee and Joggins Fossil Cliffs Science Advisory Committee. Advising & Grants: Currently accepts Honours students focusing on soft rock geology, salt tectonics, EDIAAR (equity/diversity/inclusion), or paleontology. Collaborates on projects combining field observations with quantitative methods like Markov chain analysis. Labs/Teams: Involved with Acadia Petrographic Laboratory and contributes to regional geoscience field schools.