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Haoran ShiView profile

Researcher

• river hydrodynamics
• environmental fluid mechanics
• physical limnology
• +3 more

Dr. Haoran Shi is a Postdoctoral Research Officer at the School of Ocean Sciences, Bangor University. He holds a PhD in Civil and Environmental Engineering from École Polytechnique Fédérale de Lausanne (EPFL), Switzerland (2023). His research focuses on river hydrodynamics, environmental fluid mechanics, and physical limnology, with recent work exploring subsurface heatwaves in lakes, hyperpycnal river plumes, and multivariate extremes in aquatic systems. His methodologies include experimental studies, numerical modeling, and spatio-temporal analysis. Education: PhD in Civil and Environmental Engineering, EPFL (2023); prior degrees not detailed in the text. Research collaborations span international teams addressing coastal sediment dynamics, lake climatology, and vegetation effects on flow patterns. Key contributions include the development of analytical models for vegetated channel hydraulics and improved parameterization of environmental fluid dynamics processes. Dr. Shi’s publications (n=15+) demonstrate expertise in fluid dynamics applications across riverine, lacustrine, and coastal environments. His work bridges fundamental fluid mechanics with real-world challenges like climate impacts on water resources and sediment transport modeling. No awards or grants are explicitly listed in the provided materials. Research activities include laboratory experiments on plume dynamics, field observations of lake thermal regimes, and computational simulations of flow-vegetation interactions. Current projects likely involve integrating remote sensing data with in-situ measurements for environmental monitoring.

Sergey ZelikView profile

Professor

• Partial Differential Equations
• Dynamical Systems
• Mathematical Analysis
• +3 more

Professor Sergey Zelik is a faculty member in the School of Mathematics and Physics at the University of Surrey. His research focuses on partial differential equations (PDEs), dynamical systems, and mathematical analysis, with a particular emphasis on nonlinear dynamics, reaction-diffusion systems, and attractor theory. He has made significant contributions to the study of global attractors, inertial manifolds, and chaos in PDEs. Notably, he was awarded China’s Highest Foreign Research Honour in 2019. His work spans theoretical and applied mathematics, including studies on the Cahn-Hilliard equation, Ginzburg-Landau equations, and wave equations. Recent research includes the dynamics of multi-pulse interactions and the validity of Whitham modulation equations in Sobolev spaces. Zelik’s publications explore topics like Poincaré inequalities, interpolation inequalities, and the long-time behavior of dissipative systems. His findings have been published in top-tier journals such as SIAM Journal on Applied Dynamical Systems , Nonlinearity , and Communications in Partial Differential Equations .

Christian RohrbeckView profile

Senior Lecturer

• Extreme Value Analysis
• Spatial Statistics
• Environmental Statistics
• +3 more

Dr. Christian Rohrbeck is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath. He holds a PhD in Statistics and Operational Research from Lancaster University and leads projects in statistical climatology through the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and Centre for Climate Adaptation & Environment Research. His research combines extreme value theory, spatial statistics, and Bayesian methods to analyze environmental and financial data. Current projects focus on flood risk modeling, climate extremes standardization, and monotonic regression techniques. Dr. Rohrbeck accepts doctoral students in extreme value analysis, flood risk, and climate adaptation statistics.

• Mathematical Modelling
• Dynamical Systems
• Pattern Formation
• +3 more

Pranali Roy Chowdhury is a Research Fellow at the University of Alberta's Department of Mathematical and Statistical Sciences, where she leads toxic-based ecosystem modelling research in the Interdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE) under Prof. Hao Wang. Previously, she held a postdoctoral position at the Indian Institute of Technology (IIT) Bombay in the Department of Mathematics, contributing to the National Disease Modelling Consortium's work on Respiratory Syncytial Virus (RSV) burden estimation in India. She earned her PhD in Mathematics from IIT Kanpur in 2023. Pranali completed her PhD in Mathematics at the Indian Institute of Technology Kanpur (IITK), India, in 2023. Her doctoral research focused on canards, relaxation oscillations, and transient spatiotemporal patterns in ecological systems. Prior to this, she briefly held a postdoctoral position at IIT Bombay. Her research interests revolve around mathematical modelling in ecological and epidemiological contexts, with a focus on dynamical systems and pattern formation. She explores these topics through interdisciplinary approaches, particularly examining the impact of toxins on ecosystems, slow-fast systems in predator-prey interactions, and spatio-temporal dynamics in fragmented habitats. Her work integrates both theoretical analysis and applied mathematical techniques to address real-world challenges in environmental and public health domains. Her publications reflect a strong emphasis on slow-fast dynamical systems and their applications in ecology and epidemiology. Recent work includes examining methane's ecological impact, oxygen minimum zones in plankton dynamics, and bifurcation analysis in predator-prey models. She also investigates transient dynamics and pattern formation in spatio-temporal systems, highlighting the role of time scales in ecological stability and disease spread modeling. In terms of advising and grants, Pranali Roy Chowdhury is currently leading research projects within ILMEE and previously contributed to the National Disease Modelling Consortium. While she has not listed formal advisees, her work involves collaborations and supervision within these interdisciplinary research environments. Details about specific grants or funding are not explicitly provided in the text. She is affiliated with the Interdisciplinary Lab for Mathematical Ecology and Epidemiology (ILMEE) at the University of Alberta and was part of the National Disease Modelling Consortium during her time at IIT Bombay.

Prathyush SambaturuView profile

Research Fellow

• Computational and Spatial Epidemiology
• Network Science
• Combinatorial Optimization
• +1 more

Dr. Prathyush Sambaturu is a Research Fellow in the Department of Biology at the University of Oxford, affiliated with the Kraemer research group. His work focuses on computational and spatial epidemiology, network science, combinatorial optimization, and complex systems. He designs agent-based network models to study infectious disease dynamics and develops surveillance strategies like genomic or wastewater monitoring to detect outbreaks and infer epidemiological parameters such as the reproduction number. His research interests include understanding how human mobility and population structure influence disease spread, as well as creating scalable algorithms for epidemic control. He also explores the impacts of climate change on migration patterns and associated disease risks. His articles span advanced computational methods applied to public health challenges, including graph-based active learning, fair disaster containment policies, and GPU-optimized intervention strategies. No scientific awards are explicitly mentioned. His research advisory role and grants information are not detailed in the provided texts. He is part of the Kraemer group, contributing to interdisciplinary projects at the intersection of epidemiology, network science, and algorithm design.

Paolo FavaroView profile

Professor

• Computer Vision
• Computational Photography
• Machine Learning
• +4 more

Prof. Dr. Paolo Favaro is a full Professor and Head of the Computer Vision Group (CVG) at the University of Bern's Institute of Computer Science. He holds a Laurea (BSc+MSc) from the University of Padova, Italy, and M.Sc. and Ph.D. in Electrical Engineering from Washington University in St. Louis. His career includes postdoctoral roles at UCLA and Cambridge University, followed by work in medical imaging at Siemens Corporate Research. From 2006–2011, he was Lecturer and Reader at Heriot-Watt University, and Honorary Fellow at the University of Edinburgh before joining Bern in 2012. His research focuses on computer vision , computational photography , machine learning , and signal/image processing , with contributions to inverse problems and variational techniques. His work spans theoretical advancements and practical applications, such as video diffusion models, unsupervised segmentation, and image restoration. He is an active member of the IEEE Society. Recent research trends emphasize generative models (e.g., diffusion models, video generation) and self-supervised learning , addressing challenges in real-world image/video denoising and sparsity-driven methods. His articles explore topics like Kalman-based optimization, multimodal world models, and spatio-temporal representation learning. Prof. Favaro leads the CVG, fostering interdisciplinary research at the intersection of computer vision and AI. His lab develops cutting-edge solutions for 3D reconstruction, medical imaging, and autonomous systems.

Fredrik LindstenView profile

Associate Professor

• Machine Learning
• Statistical Machine Learning
• Approximate Bayesian Inference
• +7 more

Fredrik Lindsten is a Senior Associate Professor in Machine Learning and Head of the Division of Statistics and Machine Learning at Linköping University's Department of Computer and Information Science (IDA). His research focuses on statistical machine learning, emphasizing probabilistic modeling and uncertainty quantification in methods such as approximate Bayesian inference, representation learning, and graph-based approaches. Applications span weather forecasting, materials science, biochemistry, and automotive industry challenges. Education: MSc (2008), PhD (2013) in Automatic Control from Linköping University; Postdoctoral roles at University of Cambridge, UC Berkeley, and University of Oxford. Affiliations: WASP (Wallenberg AI, Autonomous Systems and Software Program), ELLIIT (Lab for Information and Communication Technology). Research Interests: Lindsten’s work bridges statistical methodology and machine learning, particularly in quantifying uncertainty in predictions. His team explores method development across diverse applications, including spatio-temporal models and graph-based techniques. Recent projects include probabilistic weather forecasting with graph neural networks and cryo-EM reconstruction techniques. Publications: Over 25+ peer-reviewed articles, with recent highlights in Nature Methods , NeurIPS , and Physical Review Materials , focusing on Bayesian methods, generative models, and computational statistics. Awards: Ingvar Carlsson Award (Swedish Foundation for Strategic Research), Benzelius Award (Royal Society of Sciences in Uppsala). Grants & Teams: Supervises 7+ PhD students; active in collaborative projects funded by WASP, ELLIIT, and VR (Swedish Research Council). Labs/Teams: Leads the Division of Statistics and Machine Learning (STIMA) at IDA, fostering interdisciplinary research in probabilistic machine learning.

Arul EarnestView profile

Professor

• Biostatistics
• Health Economics
• Clinical Registries
• +4 more

Professor Arul Earnest is a leading academic research biostatistician at Monash University's School of Public Health & Preventive Medicine, where he serves as Deputy Head of the Clinical Outcomes Data Reporting and Research Program (CORRP). He holds a Professor (Research) appointment in Biostatistics, Data Analytics/Modelling, and Health Economics. His expertise spans Bayesian spatio-temporal modelling, machine learning applications in healthcare, and clinical registries analysis. He coordinates the MPH5270 unit on Advanced Statistical Methods for Clinical Research and developed the 'Clinical Registries Data Analysis using Stata' course. His research collaborations include government bodies, patient advocacy groups, and international organizations, with a focus on improving healthcare data quality and patient outcomes. Professor Earnest has authored over 245 peer-reviewed publications, a textbook on biostatistical collaboration, and earned 19 scientific awards. He manages a statistical team supporting ~30 clinical registries and leads national projects on ovarian cancer management, real-time patient outcome measures, and chronic disease priority-setting strategies. His work contributes to UN Sustainable Development Goals addressing health and well-being (SDG 3), and he actively participates in grant review panels, ethics committees, and statistical conferences.

Christina DavyView profile

Associate Professor

• Biodiversity Conservation
• Wildlife Pathogens
• Aeroconservation
• +3 more

Christina Davy is an Associate Professor in the Department of Biology at Carleton University (located on unceded Algonquin Anishnaabeg territory). Her research focuses on biodiversity conservation under environmental threats like habitat loss, climate change, and wildlife pathogens. She leads the Davy Lab, emphasizing collaboration among researchers, governments, and Indigenous communities. Key areas include aeroconservation and functional connectivity of migratory species. Educations: B.Sc. from University of Guelph M.Sc. from Western University Ph.D. from University of Toronto (affiliated with Royal Ontario Museum) Research Themes: Integrates ecological theory with applied conservation science to address species persistence. Current projects investigate synergistic threats (habitat modification, invasive species, pathogens) on amphibians, reptiles, and bats. Strong emphasis on Indigenous-led conservation stewardship and public science communication. Publications: Recent work highlights urbanization impacts on migratory bats, reptile-roads interactions, and white-nose syndrome in bats. Research spans ecological networks, functional connectivity, and pathogen-driven declines. Lab & Fieldwork: Collaborates across Turtle Island territories (Michi Saagiig, Omàmìwininìwag, Haudenosaunee lands), acknowledging Indigenous stewardship. Focuses on improving conservation partnerships and science outreach.

Silvia LiveraniView profile

Associate Professor

• Bayesian Statistics
• Spatial Modelling
• Computational Statistics
• +3 more

Silvia Liverani is a Reader in Statistics and Head of the Centre in Data Science, Statistics and Probability at Queen Mary University of London (School of Mathematical Sciences). Her research focuses on Bayesian methods, spatial-temporal modeling, and computational statistics, with applications in epidemiology, environmental health, and genetics. She leads projects on biodiversity analysis and spatial health data, collaborating with institutions like the Royal Botanical Gardens, Kew and the Alan Turing Institute. Education: PhD in Bayesian Clustering from the University of Warwick (2009). Grants & Initiatives: Awarded £1,000 (2025) for an LGBTQ+ Mathematics Open Day; led a Data Study Group at the Alan Turing Institute (2023). Labs/Teams: Director of the Centre in Data Science, developer of R packages PReMiuM and CARME for Bayesian clustering and spatial modeling. Key Research Themes: Dirichlet process mixture models, spatial misalignment correction, and interdisciplinary collaborations in health and environment. Current work includes predicting biodiversity loss under climate change and analyzing socio-economic health disparities through spatio-temporal frameworks.

Nicholas MeyerView profile

Lecturer

• Geomatic Engineering
• Civil Engineering
• Environmental Monitoring
• +3 more

Nicholas Meyer is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zürich. His research focuses on advanced geodetic measuring technologies, including terrestrial laser scanning (TLS) and 3D displacement estimation for structural and environmental monitoring. Key applications include deformation analysis of hydroelectric dams, long-range TLS in alpine environments, and vibration monitoring of bridges. His work emphasizes high-fidelity numerical simulations and multi-scale analysis, targeting infrastructure resilience and natural hazard assessment. He leads projects integrating TLS with RGB data for enhanced geomonitoring and employs self-supervised machine learning for debris flow analysis. Contact details: nicholas.meyer@geod.baug.ethz.ch. No scientific awards are explicitly listed in the provided materials. Research interests span geomatic engineering, environmental monitoring, and civil infrastructure diagnostics. Advising and grants sections remain unspecified, though his research group actively publishes in structural health and geospatial technologies.

• Bayesian Statistics
• Environmental Science
• Climate Change
• +5 more

Jonathan Rougier is a Visiting Professor at Imperial College London's Faculty of Natural Sciences (Department of Mathematics) with an honorary appointment at the University of Bristol. He specializes in statistics, focusing on applications in environmental science, climate change, and geohazards. His work bridges academia and industry, emphasizing probabilistic modeling, uncertainty quantification, and decision-support systems for natural hazards. Education & Affiliations: Honorary staff at Imperial's Mathematics and Statistics departments. Collaborates with institutions on global geophysical and climate projects. Research Interests: Bayesian methods, spatio-temporal modeling, volcanic eruption prediction, climate change impacts, and risk assessment. His research addresses challenges in environmental data analysis, including unreliable historical records and complex simulation outputs. Articles Trends: Recent work focuses on volcanic hazard modeling (e.g., Aso volcano likelihoods), Antarctic ice mass evolution, and Kalman filter applications in environmental monitoring. Themes include uncertainty quantification, multi-scale computational methods, and interdisciplinary data integration. Advising & Grants: No formal advisees listed. Contributions include methodological advancements for climate projections and hazard analysis, supported by collaborative research networks. Labs/Teams: Engages in global initiatives like the VOLCORE database and Antarctic mass balance studies, emphasizing interdisciplinary teams for geophysical and environmental challenges.

Gyanendra PokharelView profile

Associate Professor

• Biostatistics
• Cancer epidemiology
• Bayesian & computational statistics
• +4 more

Dr. Gyanendra Pokharel serves as an Associate Professor in the Department of Mathematics and Statistics at the University of Winnipeg. He concurrently holds an adjunct appointment as Assistant Professor in the Department of Community Health Sciences at the University of Manitoba. His educational qualifications include: Ph.D. in Applied Statistics from the University of Guelph, Canada M.Sc. in Applied Mathematics from Wilfrid Laurier University, Canada M.Sc. in Pure Mathematics from Tribhuvan University, Nepal B.Sc. in Mathematics from Tribhuvan University, Nepal Dr. Pokharel's research spans Biostatistics , Cancer Epidemiology , Bayesian & Computational Statistics , and Infectious Disease Modeling . He develops advanced methodologies for spatial infectious disease surveillance, including emulation-based inference and ensemble learning techniques. His cancer epidemiology work focuses on breast cancer stage-shifting through simulation studies, while his rheumatology research employs network meta-analysis to evaluate treatment effectiveness. His expertise in Statistical Learning enables innovative classification and clustering approaches for complex health data. Analysis of his 15 most recent publications (2014-2024) reveals a dominant focus on infectious disease modeling (60% of articles), particularly spatial-temporal methods and machine learning applications. Biostatistics contributions (40%) center on cancer epidemiology and rheumatology, featuring network meta-analyses and discrete-choice experiments for patient preference studies. His work consistently integrates computational statistics with real-world health challenges. No specific scientific awards are documented in the provided materials, though his research is competitively funded. Dr. Pokharel actively mentors students through his NSERC Discovery Grant-funded projects. He teaches advanced statistical computing courses (STAT 2903/3904) and core statistics courses (STAT 1301/1302), with past instruction in statistical learning (STAT 4103). Prospective graduate students in biostatistics, spatial statistics, or machine learning applications are encouraged to contact him for research opportunities.

Joe WatsonView profile

Researcher

• Statistics
• Environmental Statistics
• Spatio-Temporal Modeling
• +1 more

Joe Watson is a researcher affiliated with the Department of Statistics at the University of British Columbia (UBC), Faculty of Science. His work focuses on statistical methodology for environmental and spatio-temporal data analysis. Research Interests: Joe's research addresses challenges in environmental statistics, particularly preferential sampling and spatio-temporal modeling. His studies provide theoretical frameworks and computational tools for analyzing complex ecological and environmental datasets. Publications: His notable contributions include advancements in Monte Carlo testing for sampling bias, general theories for environmental network sampling, and multi-data-type species distribution models.

Jun ZhuView profile

Professor

• Spatial statistics
• Spatio-temporal statistics
• Markov random fields
• +7 more

Jun Zhu is a Professor of Statistics at the University of Wisconsin-Madison, affiliated with the Department of Statistics and cross-listed in the Department of Entomology, Department of Biostatistics and Medical Informatics, and the Center for Demography and Ecology. His research focuses on spatial and spatio-temporal statistical methodologies, applied to ecological, environmental, agricultural, and health systems. He holds prestigious awards including the Distinguished Achievement Medal from the American Statistical Association and election to the International Statistical Institute. Education: PhD in Statistics (Iowa State University, 2000), MSE in Mathematical Sciences (Johns Hopkins University, 1995), BA in Mathematics and Computer Science (Knox College, 1994). Research emphasizes methodological development for spatial data analysis, with applications in agriculture, ecology, and public health. Key contributions include cluster detection algorithms, spatial regression models, and spatio-temporal analyses of environmental and demographic phenomena. His work bridges theoretical statistics with interdisciplinary collaborations, addressing real-world challenges such as disease mapping and climate change impacts. Scientific Awards: Distinguished Achievement Medal (ASA) Elected Member (International Statistical Institute) Fellow (American Statistical Association) Presidential Award (American Society of Agronomy) Teaching and advising: Leads courses on spatial statistics, time series analysis, and statistical consulting. Advises on methodological and collaborative projects at the intersection of statistics and environmental science. Labs/Teams: Active in interdisciplinary research teams focused on spatial demography, ecological modeling, and statistical ecology.