جست‌وجوی استاد و پژوهشگر

• Spatial epidemiology
• Spatial Statistics

Dr. Deborah Costain is a Senior Lecturer in Statistics at the School Of Mathematical Sciences, Lancaster University. Her research focuses on spatial epidemiology and spatial statistics, integrating statistical methods into health technology assessment and clinical trial design. Current research projects include: Statistical modelling of post-stroke disability and survival (2024-2026) Statistical Methods for Evaluating Quality and Length of Life in Post-stroke Patients (2021-2023) Design and Analysis of Clinical Trials for Complex Interventions (2022-2024) Statistical Methods for Health Technology Assessment (2022-2024) She advises PhD student Joseph Price and contributes to research groups in Bayesian and Computational Statistics, Biostatistics, and the STOR-i Centre for Doctoral Training. Her recent publication in the Scandinavian Journal of Statistics (2025) explores multidimensional monotonic regression techniques.

Jakob Frankeمشاهده پروفایل

استاد

• Plant Biosynthetic Pathways
• Metabolic Engineering
• Ecosystem Services
• +۴ مورد دیگر

Prof. Dr. Jakob Franke is a W2 Tenure Track Professor for the Biochemistry of Plant Specialised Metabolites at the Institute of Botany, Leibniz University Hannover , Germany. He serves as an Emmy Noether Group Leader and Junior Research Group Leader under Prof. Russell Cox, focusing on plant biosynthetic pathways and metabolic engineering. Education: PhD in Chemistry (2011-2015) and MSc in Chemistry (2009-2011) from Ludwig Maximilians University Munich; BSc in Biochemistry (2006-2009) from Technical University Munich. His research bridges plant biochemistry with ecosystem service assessment, particularly in agricultural and coastal environments. Recent work includes standardizing ecosystem condition indicators, modeling pollinator habitats, and evaluating digital agriculture impacts using Bayesian networks. Key article trends (2025-2018) span Environmental Science , Ecology , and Computational Modeling , with subfields like ecosystem accounting, soil biodiversity, marine service mapping, and socio-ecological frameworks. Collaborative projects often involve Prof. Burkhard and teams across Europe, China, and Southeast Asia. Scientific Awards: Emmy Noether Group Leader He has advised on national ecosystem service indicators and contributed to Horizon Europe projects. His lab integrates biochemical research with large-scale environmental modeling, emphasizing sustainability and policy-relevant outcomes.

Song Qianمشاهده پروفایل

استاد

• Environmental and Ecological Statistics
• Bayesian Hierarchical Modeling
• Water Quality and Watershed Modeling
• +۳ مورد دیگر

Dr. Song Qian is a Professor in the Department of Environmental Sciences at the University of Toledo, College of Natural Sciences and Mathematics. His research focuses on quantitative methods for environmental and ecological data analysis, particularly using Bayesian hierarchical models to study cross-scale ecological processes. Ph.D. from Duke University (1995) M.S. from Nanjing University B.S. from Tsinghua University Research interests include: water quality modeling, stream ecosystem responses to urbanization, harmful algal blooms in Lake Erie, statistical methods for chemical measurements, and ecological applications of Bayesian networks. Recent work addresses grass carp spawning dynamics, climate change effects on phenology, and nutrient management challenges. Contact: song.qian@utoledo.edu | Phone: 419.530.4230 Article trends show strong focus on invasive species management (12/15), water quality assessment (10/15), and climate change impacts (7/15) using advanced Bayesian techniques across aquatic ecosystems in North America, China, and Antarctica.

Paul A Hohenloheمشاهده پروفایل

استاد

• Genomics
• Conservation Biology
• Evolutionary Genetics
• +۵ مورد دیگر

Paul A. Hohenlohe is a Professor in the Department of Biological Sciences within the College of Science at the University of Idaho, where he serves as Director of the Bioinformatics and Computational Biology Graduate Program. He holds additional affiliations as a Member of the Initiative for Bioinformatics and Evolutionary Studies and as Participating Faculty in the Institute for Modeling Collaboration and Innovation, both through the Office of Research and Economic Development. Dr. Hohenlohe earned his PhD in Zoology from the University of Washington in 2000 and his BA in Biology from Williams College in 1994. His research integrates genomic approaches with conservation biology to address critical wildlife management challenges. His work bridges theoretical evolutionary genetics with practical conservation applications, ensuring scientific findings inform management decisions for threatened species. Dr. Hohenlohe's research program focuses on applying genomic tools to conservation challenges. His lab utilizes techniques including Restriction site-Associated DNA sequencing (RADseq) and whole-genome sequencing, combined with bioinformatics and mathematical modeling rooted in evolutionary genetics theory. Current projects address adaptation to changing environments, population structure and phylogeography, disease resistance, inbreeding, the genetic basis of complex phenotypes, hybridization, and population history in threatened species. Specific research includes bulltrout adaptation, predator-prey systems responding to climate change, pygmy rabbit populations, trout hybridization, and burrowing owl adaptation. Analysis of Dr. Hohenlohe's recent publications reveals a strong emphasis on wildlife genomics applications to conservation, with particular focus on threatened species monitoring and methodological advances in population genomics. A significant portion of his work addresses the impact of transmissible cancers in Tasmanian devils, demonstrating the application of genomic approaches to wildlife disease ecology. His research also highlights innovative methods in genetic monitoring, including non-invasive sampling techniques and GT-seq panels for threatened species, often incorporating both microsatellite and SNP-based approaches. Dr. Hohenlohe has mentored numerous graduate students to completion of their degrees across Bioinformatics and Computational Biology, Natural Resources, and Biology programs. His lab maintains active collaborations with conservation practitioners, including the National Genomics Center for Wildlife and Fish Conservation and US Fish & Wildlife Service, ensuring scientific findings translate to actionable conservation strategies. Current research projects span multiple threatened species across diverse taxa, reflecting the broad applicability of genomic approaches to conservation challenges. The Hohenlohe lab operates within the Department of Biological Sciences at the University of Idaho, collaborating with researchers across multiple institutions including the US Forest Service and international partners. The lab maintains a strong focus on developing and applying bioinformatics tools for population genomic analysis, contributing to software such as Stacks while advancing methodological approaches for conservation genomics.

Anders Nielsenمشاهده پروفایل

استاد

• Biostatistics
• Stock Assessment
• Fish Population Dynamics
• +۵ مورد دیگر

Anders Nielsen is a Professor at the National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark, working within the Section for Marine Living Resources. His research focuses on the intersection of biostatistics, fisheries science, and marine ecology, with particular emphasis on developing statistically sound approaches for fish stock assessment and management. His research interests span biostatistics with a focus on modeling dynamical systems and state-space models, fish stock assessment methodologies, fish movement patterns, and statistical software development. Nielsen specializes in improving stock assessment models through better statistical frameworks, optimized sampling designs, and incorporation of spatial structure. His work on automatic differentiation in statistical tools has contributed significantly to computational approaches in fisheries science. Analysis of Nielsen's recent publications reveals strong trends in environmental DNA (eDNA) applications for fisheries monitoring, advanced statistical modeling for stock assessments, and integration of climate variables into fish population dynamics. His work bridges theoretical statistical development with practical fisheries management applications, particularly in North Sea and Baltic Sea ecosystems. The recurring themes include uncertainty quantification, retrospective analysis of assessment models, and development of robust frameworks for sustainable fisheries management. Nielsen actively participates in numerous research projects including MAKE-IT (sustainable mackerel management), FISHMAP (fish distribution and fisheries management), REVIVAL (herring stock assessment), CanMac (genetic approaches to mackerel assessment), and LenSAM (length-based assessment models). He serves as project coordinator for CanMac and LenSAM, demonstrating leadership in developing innovative assessment methodologies. His collaborative network spans multiple institutions and countries, reflecting the international nature of fisheries science. As a member of the International Council for the Exploration of the Sea (ICES), Nielsen contributes to international fisheries science and advice. His work directly supports UN Sustainable Development Goals related to sustainable marine resource management. While specific details about laboratory facilities aren't provided in the available information, his research clearly involves advanced computational modeling and analysis of fisheries data, likely utilizing high-performance computing resources at DTU.

Orhun Aydinمشاهده پروفایل

استادیار

• Computational Sustainability
• Spatial and Spatiotemporal Artificial Intelligence (GeoAI)
• Urban Sensing
• +۶ مورد دیگر

Orhun Aydin is an Assistant Professor at Saint Louis University's School of Science and Engineering , affiliated with the Department of Earth and Atmospheric Sciences and Department of Computer Science (by courtesy) . Ph.D. in Energy Resources Engineering (Geostatistics), Stanford University M.Sc. in Computer Science, Georgia Institute of Technology M.Sc. in Energy Resources Engineering, Stanford University B.Sc. in Petroleum Engineering and Electrical/Electronic Engineering, Middle East Technical University His research spans computational sustainability, spatial artificial intelligence (GeoAI), urban sensing, and integrated human-earth system modeling, with applications in disaster response optimization and open-source geospatial software development. Recent publications focus on spatial clustering algorithms, seagrass habitat conservation modeling, and stochastic regionalization frameworks, reflecting interdisciplinary work at the intersection of geoscience, data science, and environmental management. He teaches courses on Machine Learning in GIS and Remote Sensing and Advanced Programming in GIS and Remote Sensing , leveraging his dual expertise in geospatial analysis and computational methods.

• International Macroeconomics
• Monetary Economics
• Chinese Economy

Liu Qing is a Tenured Associate Professor in the Department of Economics at Tsinghua University's School of Economics and Management. He has been affiliated with Tsinghua University since 2008 and holds a Ph.D. in Economics from the University of Toronto. Education: Ph.D. in Economics, University of Toronto (2008) M.A. in Economics, University of British Columbia (2002) M.A. in Economics, Zhejiang University (1999) His research focuses on international macroeconomics, monetary economics, and China's economy, particularly examining real exchange rates, current account imbalances, monetary policy, industrial policy, and structural transformation. Recent studies analyze labor market flexibility, CO2 emissions decomposition, and the impact of preferential lending policies on earnings inequality. His publications span journals like International Economic Review , Journal of Development Economics , and Journal of International Money and Finance . Research methods emphasize Bayesian DSGE modeling and empirical analysis of macroeconomic structures. Scientific Awards: Tsinghua SEM Teaching Excellence Award (First Prize) Tsinghua University Outstanding Class Teacher (Second Prize) Tsinghua SEM Outstanding Class Teacher Award Liu Qing teaches undergraduate courses in Money and Banking, master's courses in International Finance, and doctoral courses in Advanced Macroeconomics and Open Macroeconomics. His academic contributions include analyzing China's monetary policy frameworks and global welfare implications of oil price stabilization.

Zhaoxia Yuمشاهده پروفایل

استاد

• Statistical Methods
• Neuroscience
• Genetic Epidemiology
• +۵ مورد دیگر

Zhaoxia Yu is a Professor in the Department of Statistics at the University of California, Irvine. Her research focuses on statistical methodology and applications in neuroscience, genetic epidemiology, and high-dimensional data analysis. University: University of California, Irvine Department: Department of Statistics Her scientific work spans mixed-effects models for neuroscience, Bayesian nonparametric analysis of calcium imaging data, applications in connectome genetics, and rigorous statistical inference for multi-subject single-cell omics. She has also contributed to methodological advancements in clustered data analysis, ridge penalization, and spike inference. Recent publications demonstrate expertise in applying statistical models to neuroscience (e.g., cortical plasticity, ketamine effects), genetic interactions (e.g., GWAS, gene-environment), and medical imaging. Her teaching includes advanced statistics courses like Statistics 200A and mixed-effects model tutorials.

Massimiliano Di Lucaمشاهده پروفایل

دانشیار

• Human Perception
• Multisensory Integration
• Temporal Perception
• +۷ مورد دیگر

Massimiliano Di Luca is an Associate Professor at the University of Birmingham, affiliated with both the School of Psychology and the School of Computer Science. His research focuses on understanding the mechanisms of human perception through psychophysical methods, computational modeling, and immersive technologies, particularly in the dynamic and temporal aspects of multisensory integration. He has made significant contributions to the study of virtual reality, haptics, and 3D shape perception. PhD in Cognitive Science (Brown University, 2006) Laurea in Psychology (Università di Trieste, 2000) Di Luca’s research spans human perception, haptics, and computational modeling, emphasizing the temporal dynamics of multisensory processing. His recent work includes the ARME project , Virtual Reality Lab , and collaborations on wearable haptic devices. He has developed models for perceptual recalibration, synchronization in musical ensembles, and tactile feedback optimization in XR systems. His publications highlight trends in virtual reality applications, haptic technology, and multisensory perception, with keywords like 3D shape , augmented reality , Bayesian modeling , and wearable haptics . Notable projects include TEXT: Text Entry for XR Trove and ARME . Scientific Awards: Turing Fellow (2021-2023), Fellow of the InterContinental Academia UBIAS (2021-2023). Di Luca mentors PhD, MSc, and BSc students in psychology and computer science. His grants include funding from EPSRC, BBSRC, Google, and UKRI for projects like ARME , Perception of Materials , and iCURE . He leads the Virtual Reality Lab and has contributed to the InterContinental Academia and Alan Turing Institute .

Alfonso Diz-Lois Palomaresمشاهده پروفایل

پژوهشگر ارشد

• Bayesian Inference
• Spatio-Temporal Modeling
• Environmental Health
• +۳ مورد دیگر

Alfonso Diz-Lois Palomares is a Doctoral Research Fellow at the University of Oslo , affiliated with the Faculty of Mathematics and Natural Sciences and the Statistics and Data Science research group. His work focuses on Bayesian inference, spatio-temporal modeling, environmental health, and epidemiology. Role: Doctoral Research Fellow Institution: University of Oslo Research Group: Statistics and Data Science His research explores the interplay between environmental factors (temperature, air pollution) and public health outcomes, particularly mortality patterns. He employs computational biology and epidemiological modeling to analyze climate-health relationships in Norway and globally. Recent publications highlight collaborations on temperature-related mortality risks , air pollution interactions , COVID-19 transmission dynamics , and vaccination strategy modeling . His methodological work includes sequential Monte Carlo approaches for disease modeling and global climate-health analyses. Current affiliations: University of Oslo , Faculty of Mathematics and Natural Sciences , and Statistics and Data Science research group.

Chadwick Rittenhouseمشاهده پروفایل

دانشیار

• Conservation Planning
• Wildlife Ecology
• Landscape Ecology
• +۶ مورد دیگر

Dr. Chadwick Rittenhouse is an Associate Professor in Residence at the University of Connecticut , serving as Associate Department Head in the Department of Natural Resources & the Environment. He also directs the Master of Energy and Environmental Management program and coordinates the Sustainable Environmental Planning and Management initiative. PhD (2008) , Wildlife Science, University of Missouri MS (2003) , Wildlife Science, University of Missouri BS (2000) , Wildlife Ecology, University of Wisconsin-Madison His research focuses on the intersection of science and environmental management , with emphasis on conservation planning and landscape ecology . Key areas include: Remote sensing and GIS applications in forest disturbance analysis Wildlife resource selection , movement patterns, and population dynamics (elk, mule deer, box turtles, birds) Environmental planning for habitat protection and restoration Climate change impacts on forest ecosystems Decision support systems for conservation stakeholders Recent publications highlight trends in invasive species management , geospatial conservation tools , and adaptive forest management . His work spans ecosystem restoration in South Africa, bat habitat responses to silviculture, and private land stewardship challenges in the Northeast US. Scientific awards include: Leadership Institute Fellow , The Wildlife Society (2011) Dr. Rittenhouse teaches advanced courses in natural resources management, decision methods, environmental communications, and conservation planning. He has active collaborations in forest ecosystem vulnerability assessments and habitat suitability modeling .

• Epidemiology
• Public Health
• Network Science
• +۴ مورد دیگر

Jessica Davis serves as a Research Assistant Professor in the Department of Public Health and Health Sciences at Northeastern University's Bouvé College of Health Sciences and is an active member of the MOBS Lab within the Network Science Institute. Her interdisciplinary work integrates network science methodologies with epidemic modeling to develop practical tools for public health policy and pandemic preparedness, with significant contributions during the COVID-19 pandemic. Dr. Davis earned her PhD in Network Science from Northeastern University, where her dissertation research examined cryptic transmission patterns and the efficacy of travel restrictions during the initial phases of the COVID-19 outbreak. This foundational work established her expertise in bridging theoretical network analysis with real-world public health applications. Her research program centers on two interconnected domains: Biological and Health Systems , which investigates biological interdependencies through network medicine, systems epidemiology, and public health infrastructure; and Complex Systems Forecasting , which develops network-based computational models for predicting epidemic trajectories and supporting evidence-based decision-making. These areas converge in her development of open-source tools like Epydemix and innovative surveillance frameworks using aircraft wastewater networks. Analysis of her 15 most recent publications (2021-2025) reveals consistent methodological evolution toward real-time modeling with explicit uncertainty quantification, increasing focus on multi-scale heterogeneity in disease transmission, and growing emphasis on practical implementation of forecasting systems. Her work demonstrates strong continuity in applying network science to epidemic dynamics while expanding into influenza surveillance, variant tracking, and vaccine impact assessment. No scientific awards or major honors are documented in the available materials, though her research has generated significant policy impact through presentations at venues like the Rhodes Policy Summit and collaborations with governmental agencies. Dr. Davis has secured substantial research funding including a Gates Foundation partnership investigating equitable global vaccine distribution strategies. While specific advisee relationships aren't publicly listed, she actively contributes to academic training through the MOBS Lab's hiring of computational statistics researchers and participation in the Network Science Student Research Symposium. The MOBS Lab, directed by Alessandro Vespignani and jointly affiliated with Physics, Health Sciences, and Computer Sciences departments, operates as a hub for computational epidemiology with physical locations in Boston, London, and Portland. Current lab initiatives focus on developing mathematical models for complex network systems with immediate applications to pandemic response, including the aircraft-based wastewater surveillance network featured in her 2025 Nature Medicine publication.

Yanan Fanمشاهده پروفایل

استاد مدعو

• Bayesian analysis
• Markov chain Monte Carlo methods
• Approximate Bayesian computation
• +۳ مورد دیگر

Professor Yanan Fan is an adjunct full Professor of Statistics at the School of Mathematics and Statistics , University of New South Wales, and a senior Principal research scientist at CSIRO's Data61 in Australia. Their expertise lies in Bayesian models and computational methodology for real-world problem-solving. Current research includes Bayesian semiparametric models, computational methods for large spatial data from medical images, and approximate Bayesian inference. Supervision areas: Bayesian inference, computational statistics, quantile regression, medical image analysis, and bias detection in language. Based in The Red Centre, Room 2055, Sydney NSW 2052, they bridge statistical theory with applications in ecology, climate models, and medical imaging.

• Biogeochemistry
• Watershed Ecology
• Carbon Cycling
• +۶ مورد دیگر

Alice M. Carter is an Assistant Professor at the Department of Mathematics and Statistics, Utah State University. As an ecosystem statistician and river biogeochemist, she combines empirical field research with advanced statistical modeling to study carbon cycling in aquatic systems. Research Focus Bayesian inference for watershed-scale analysis Process-guided machine learning in river energetics Time series modeling of biogeochemical fluxes Continental-scale aquatic ecosystem dynamics Key Contributions Her work addresses how local ecological controls interact with watershed-scale geomorphic constraints, particularly focusing on carbon cycling responses to environmental changes. Publications highlight innovations in process-guided deep learning approaches and hypoxia dynamics in river systems. Academic Engagement Actively mentors ecology graduate students in statistical methods and contributes to open-source research code through her GitHub repositories.

Tse-min Linمشاهده پروفایل

دانشیار

• Political Methodology
• Formal Political Theory
• American Politics
• +۴ مورد دیگر

Tse-min Lin serves as Associate Professor of Government in the College of Liberal Arts at the University of Texas at Austin, concurrently holding the position of Associate Director of the Center for Taiwan Studies. He earned his Ph.D. in Political Science from the University of Minnesota and maintains active teaching responsibilities including advanced statistical analysis and political behavior courses scheduled through Fall 2025. His academic journey includes teaching appointments at the University of Minnesota, State University of New York at Stony Brook, Duke University, Michigan State University, Academia Sinica, National Taiwan University, University of Nottingham, and National Tsing Hua University, demonstrating extensive regional expertise in East Asia. Dr. Lin's research program integrates political methodology, formal theory, and comparative political behavior with specific focus on Taiwan's democratization, cross-strait relations, and protest dynamics in Hong Kong. His methodological rigor spans Bayesian statistics, conjoint experiments, and longitudinal analysis, consistently applied to questions of political participation and institutional trust in emerging democracies. Analysis of his recent publications reveals a cohesive research trajectory examining how symbolic politics interacts with pragmatic realities in Taiwan, internet-mediated participation in China, and grievance-driven protests in Hong Kong. These works demonstrate sophisticated methodological approaches including survey experiments and time-series forecasting while addressing critical questions about democratic resilience in East Asia. Through the Center for Taiwan Studies, Lin contributes to interdisciplinary research initiatives and has conducted methodology workshops for Academia Sinica and the Inter-University Consortium of Political and Social Research (ICPSR), extending his methodological expertise to broader academic communities. No public information regarding doctoral advisees or external research grants is available in the provided materials, though his course listings indicate active mentorship of undergraduate and graduate students in statistical methods and political theory.