جست‌وجوی استاد و پژوهشگر

• Environmental statistics
• Bayesian statistics
• Spatial statistics
• +۳ مورد دیگر

Andrew O. Finley is a Professor at Michigan State University with joint appointments in Forestry and Statistics & Probability. He leads the Geospatial Lab, focusing on environmental statistics, Bayesian modeling, and spatial analysis. His research integrates hierarchical models to enhance ecological and forestry data analysis, with applications in forest inventory, carbon monitoring, and biodiversity assessment. Education includes a PhD from the University of Minnesota (2007), MS in Statistics (2007) and Forestry (2003), and a BS from Penn State (2000). He has held NSF, NASA, and USDA grants totaling over $10M, advancing carbon monitoring and statistical methodologies. Research interests span Bayesian statistics, spatial-temporal modeling, and computational tools for large datasets. Notable contributions include the spBayes and spNNGP R packages, widely used for spatial analysis. His work has been recognized with ASA awards and fellowships. Recent projects include assessing western US tree species decline, developing scalable carbon monitoring frameworks, and integrating remote sensing with field data. He advises over 20 graduate students and collaborates globally on ecological and environmental challenges.

Shanshan Zhaoمشاهده پروفایل

دانشیار مدعو

• Biostatistics
• Environmental Health
• Cancer Research
• +۷ مورد دیگر

Dr. Shanshan Zhao is an Adjunct Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill and a Principal Investigator at the National Institute of Environmental Health Sciences (NIEHS). Her research focuses on developing statistical methods to understand environmental and social influences on health, particularly in cancer epidemiology and risk prediction. She holds a secondary appointment in the NIEHS Epidemiology Branch and leads a group integrating statistical methodology with population-based studies. Her work emphasizes disease risk assessment using survival analysis, longitudinal data, and biomarker evaluation. Key research areas include multivariate failure time data, environmental mixture analysis, and epigenetic biomarkers. She has contributed to studies on breast, prostate, and colorectal cancers, emphasizing spatial-temporal modeling and public health applications. Zhao’s awards include the 2014 ASA David P. Byar Travel Award and recognition for her 2023 NIEHS publication on environmental breast cancer risk factors. Her team develops software tools like mhazard for multivariate survival analysis and STAFT for spatio-temporal modeling. Her lab collaborates across disciplines to advance environmental health sciences, with a focus on translating statistical innovations into actionable insights for clinicians and policymakers.

Elisabeth Addinkمشاهده پروفایل

دانشیار

• Remote Sensing
• Hydrology
• Geo-computation
• +۳ مورد دیگر

Dr. Elisabeth Addink is an Associate Professor in Physical Geography at Utrecht University's Faculty of Geosciences. Her research focuses on remote sensing applications for vegetation and ecosystem monitoring, particularly in dynamic environments like tidal flats, rivers, and agricultural landscapes. She integrates techniques such as object-based image analysis (GEOBIA), deep learning, and UAV-based sensing to study landscape functioning, geo-computation, and hydrological processes. Dr. Addink's work addresses challenges in land degradation, environmental sustainability, and climate resilience, with projects spanning Europe, Africa, and Asia. Her research interests include the use of geospatial technologies to map ecological variables, assess irrigation water consumption, and model channel networks in estuaries and braided rivers. She has contributed to initiatives like the 'Future Delta' program and collaborates on projects analyzing subsidence in the Mekong Delta and plague host distribution in Kazakhstan. Dr. Addink is actively involved in academic activities, including editorial roles for remote sensing journals and organizing international conferences like GEOBIA. Dr. Addink's publications reflect a focus on advancing methodologies for environmental monitoring, with recent work emphasizing deep learning-enhanced remote sensing and the application of UAV technology for vegetation and floodplain analysis. She teaches courses in earth sciences and geospatial techniques, and her lab is part of Utrecht's Pathways to Sustainability research theme.

• Machine Learning
• Computational Biology
• BioImage Informatics
• +۳ مورد دیگر

Gisele Barboni Miranda is an Assistant Professor in Machine Learning for Computational Biology at KTH Royal Institute of Technology and a SciLifeLab Fellow. She holds a joint Ph.D. in Computer Science and Applied Mathematics from the University of São Paulo (Brazil) and Applied Biological Sciences from Ghent University (Belgium). Her research focuses on integrating multi-modal data (e.g., microscopy images, omics data) to model cellular behavior and improve disease diagnosis/treatment. She leads projects in the BioImage Informatics Facility and the Imaging Program of the Chan-Zuckerberg Initiative. Research interests include generative AI, deep learning, graph machine learning, and multi-modal profiling for biological systems. She teaches courses such as Statistical Methods in Applied Computer Science and supervises degree projects in Computer Science and Engineering. Scientific contributions span epidemiological modeling (e.g., influenza, SARS-CoV-2), environmental engineering applications of ML, and computational linguistics. Awards include the SciLifeLab Fellowship. Active in collaborative initiatives like the Chan-Zuckerberg Imaging Program.

Alan Woodleyمشاهده پروفایل

مدرس ارشد

• Data Science
• Environmental Management
• Computational Modeling
• +۳ مورد دیگر

Dr. Alan Woodley is a Senior Lecturer in the School of Computer Science at Queensland University of Technology (QUT) and leads the Spatial Informatics Laboratory. He holds a PhD in Computer Science from QUT and a Certificate in New Ventures Leadership from MIT. His roles include Associate Investigator at the Centre for Agriculture and the Bioeconomy, Australian Data Science Centre, and ARC Centre of Excellence for Mathematical & Statistical Frontiers. Education: PhD (2008), B.IT (Hons) (2003), B.IT (2003) from QUT; MIT Innovation Bootcamp (2018). Research focuses on applying big data solutions to environmental and mining domains, emphasizing data analytics, computational modeling, and visualization. Key areas include industrial sustainability, social-technical integration, and flood monitoring using remote sensing and social media. His work has attracted over $4M in funding and produced over 70 publications. Recent articles highlight innovations like flood mapping via spectral indices, social media-based disaster detection, and parallel clustering algorithms. Notable grants include projects with the US Department of Energy, Food Agility CRC, and CRC for Spatial Information. Awards: Vice Chancellor's Research Fellowship (2018), Bonneville Power Administration grants for salmon monitoring and fish density tools, and Food Agility CRC funding for circular economy frameworks. Teaching: Courses include Programming Principles, Web Development, and Large Scale Data Mining (encompassing 100–600 students annually). Collaborations: Partnerships with Lendlease, Bonneville Power Association, Queensland Government, and mining industry representatives to develop tools like community compost apps and land change systems. His Spatial Informatics Lab drives interdisciplinary projects in environmental informatics and geospatial data science.

• Evolution of Cognition
• Comparative Cognition
• Causal Reasoning
• +۵ مورد دیگر

Prof Amanda Seed is a Professor at the School of Psychology and Neuroscience, University of St Andrews. Her research focuses on the evolution of cognitive flexibility and abstract reasoning across species, particularly in non-human primates and children. She investigates causal reasoning, object concepts, and executive functions like inhibition and working memory. Key projects include cross-species comparisons of physical reasoning, causal understanding, and the evolutionary pressures shaping intelligence in large-brained animals like corvids and parrots. Her work spans developmental psychology and comparative cognition, with a focus on understanding how cognitive processes evolved to enable uniquely human thinking. Prof Seed has supervised PhD students including Kaustabh Baruah, Janie Fink, and Rasmus Overmark. Current research explores trap problems in primates and children, causal structure inference in non-human animals, and short-term memory in capuchin monkeys using virtual environments. Publications highlight investigations into sequence memory, tool-use evolution, and causal knowledge in chimpanzees. While no specific awards are listed, her contributions to comparative and evolutionary cognition are widely recognized in academic circles. Her lab focuses on integrating experimental psychology with evolutionary theory to explain cognitive adaptations.

Xiaowei Jiaمشاهده پروفایل

استادیار

• Artificial Intelligence
• Data Mining
• Machine Learning
• +۳ مورد دیگر

Xiaowei Jia is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh, affiliated with the School of Computing and Information. He holds a Ph.D. from the University of Minnesota (supervised by Prof. Vipin Kumar) and B.S./M.S. degrees from the University of Science and Technology of China and SUNY Buffalo. His research focuses on integrating scientific theory with machine learning to address societal challenges in environmental science, hydrology, and climate modeling. Key research interests include physics-guided machine learning, spatio-temporal data mining, and fairness-aware models. He has developed frameworks like Physics-Guided Neural Networks (PGNN) and Spatial-Net for heterogeneous data, addressing issues in stream temperature prediction, crop yield modeling, and climate change projections. His work has been recognized with awards including the Best Dissertation Award (Minnesota) and multiple Best Paper awards in top conferences. Dr. Jia teaches advanced machine learning courses, emphasizing knowledge integration into AI systems. His lab collaborates extensively with domain scientists to advance applications in agriculture, hydrology, and environmental monitoring. Ongoing projects include high-resolution methane emission datasets (X-MethaneWet) and fairness-aware learning for resource distribution.

• Temporary Migration
• Creative and Cultural Industries
• Popular Music Studies
• +۳ مورد دیگر

Rosie Roberts is a Senior Lecturer at the University of South Australia (UniSA) within the School of Creative Industries under UniSA Creative. Her research focuses on spatio-temporal intersections of gender, ethnicity, class, and ageing, with key programs in temporary migration and creative/cultural industries (particularly popular music). She authored Ongoing Mobility Trajectories: Lived Experiences of Global Migration (2019) and co-edited Rethinking Privilege and Social Mobility in Middle-Class Migration (2022). Her work includes collaborations with the South Australian Music Development Office to assess live music venues' pandemic impacts and mid-career transformations in the music industry. Roberts is affiliated with the Creative People, Products and Places Research Centre at UniSA. Her recent publications analyze migration policy, transnational identities, and cultural ecosystem resilience. She has contributed to policy initiatives such as the $3M South Australian music venue support package. Her research bridges academic and applied contexts, emphasizing the social and economic roles of cultural industries and migration dynamics. Current projects explore gendered career trajectories in music and the evolving impacts of global mobility on identity formation.

Kevin Nowkaمشاهده پروفایل

عضو هیئت علمی

• Semiconductor Design
• Computer Architecture
• Memory Systems
• +۲ مورد دیگر

Kevin Nowka is Professor of Practice in Electrical and Computer Engineering at Texas A&M University's College of Engineering. His research bridges semiconductor design and agricultural informatics, developing machine learning tools for precision farming and wildfire prediction. Education and industry background include extensive experience in high-frequency processor design and VLSI systems. Previous roles include IBM Research leadership in memory systems and low-power architectures. Research applies deep learning to environmental challenges, notably through UAS-based crop monitoring frameworks and neural network optimization for embedded agricultural systems. Recent work focuses on cotton yield forecasting using Bayesian networks and LSTM models. Publications show convergence of semiconductor expertise and agri-informatics, with innovations in model compression for edge computing in field applications. Key themes include phenotyping automation and privacy-preserving data analytics. Teaches courses on computer architecture and embedded systems. Supervises student projects on AI applications in sustainable agriculture.

Rajarshi Guhaniyogiمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Machine Learning
• Big Data Analytics
• +۹ مورد دیگر

Rajarshi Guhaniyogi is an Associate Professor in the Department of Statistics at Texas A&M University, College of Arts & Sciences. His research focuses on Bayesian methodology for complex biomedical and machine learning applications, including scalable Bayesian methods, spatial/spatio-temporal statistics, and tensor analysis. He has been recognized with prestigious awards such as the 2023 Early Investigator Award from ASA ENVR and the 2023 ECASDS Award from IISA. Key collaborations include work with neuroscientists on multi-modal neuroimaging data and environmental scientists on remote sensing data. His research is supported by grants from NIH, NSF, Department of Energy, and Office of Naval Research. Rajarshi will transition to a Full Professor role in August 2025. Recent work emphasizes distributed Bayesian inference for large datasets, Bayesian tensor regression, and robust computation strategies for high-dimensional data. He has contributed to journals like Biostatistics , Technometrics , and Journal of Machine Learning Research , and serves on editorial boards for Journal of Machine Learning Research and Journal of Computational and Graphical Statistics . His grants include an NIH R01 grant (1 percentile score) for Bayesian multi-object data modeling and a DOE grant for spatial modeling of categorical data. He actively organizes conferences like the NSF-sponsored CBMS conference on causal graphical models.

Toryn Schaferمشاهده پروفایل

استادیار

• Spatio-temporal statistics
• Reinforcement learning
• Bayesian statistics
• +۵ مورد دیگر

Toryn Schafer is an Assistant Professor in the Department of Statistics at Texas A&M University, holding the 2024 ConocoPhillips Data Science Faculty Fellowship. Previously, he was a postdoctoral associate at Cornell University's Department of Statistics and Data Science, contributing to the NSF-funded PRISM Institute for Trans-domain Systemic Risk. His academic journey includes a PhD in Statistics (2020) from the University of Missouri, an MA in Statistics (2018), and a BSc in Statistics & Wildlife Biology (2014) from Colorado State University. His research interests focus on spatio-temporal statistics, reinforcement learning, Bayesian methods, and machine learning, applied to ecological and environmental systems. He emphasizes interdisciplinary approaches to address challenges in energy, sports analytics, and biodiversity conservation. His recent work explores advanced statistical methods for trend analysis, changepoint detection in time series, and the integration of community science data for ecological modeling. Notable contributions include Bayesian inverse reinforcement learning frameworks for animal behavior analysis and studies on renewable energy impacts on electricity markets. Education: PhD in Statistics, University of Missouri (2020) MA in Statistics, University of Missouri (2018) BSc in Statistics & Wildlife Biology, Colorado State University (2014) Research Themes: His publications reflect a trajectory toward methodological advancements in spatio-temporal modeling, with applications ranging from animal movement patterns to energy grid resilience. Recent articles highlight critical risk indicators for power systems and biodiversity status metrics inspired by financial portfolio theory. Grants & Fellowships: 2024 ConocoPhillips Data Science Faculty Fellowship (Texas A&M University). Labs/Teams: While specific lab affiliations are not detailed, his work intersects with interdisciplinary teams focused on ecological data science and systemic risk analysis.

Thomas Marlowمشاهده پروفایل

استادیار

• Environmental Sociology
• Urban Sociology
• Computational Social Science
• +۳ مورد دیگر

Thomas Marlow is an Assistant Professor of Sociology at Texas A&M University, specializing in environmental and urban sociology with a focus on computational methods. His research addresses environmental inequalities, climate policy barriers, and sustainable urban transitions. He holds a Ph.D. from Brown University and conducted postdoctoral research at NYU Abu Dhabi’s Center for Interacting Urban Networks (CITIES). Education Ph.D. in Sociology, Brown University (2020) Postdoctoral Fellowship, NYU Abu Dhabi (2016–2020) Research Interests Environmental Justice and Climate Policy Urban Mobility Networks and Spatial Inequality Computational Social Science and Big Data Analysis Legacy Industrial Pollution and Public Health His work has been featured in Socius , Environmental Research Letters , and media outlets like The Guardian and BBC . Courses taught include SOCI 420: Advanced Methods of Social Research. Media Coverage Highlights Ars Technica (2022): Sea-level rise and industrial toxins Boston Globe (2021): Mobility segregation analysis The Guardian (2020): Climate discourse bots Marlow’s research bridges social theory and empirical data science, with a focus on actionable policy insights for equitable urban futures.

Kostas Kalogeropoulosمشاهده پروفایل

دانشیار

• Bayesian Inference
• Stochastic Differential Equations
• Markov Chain Monte Carlo
• +۶ مورد دیگر

Kostas Kalogeropoulos is an Associate Professor and MSc Statistics Programme Director in the Department of Statistics at the London School of Economics and Political Science (LSE). He previously served as a post-doctoral researcher at the University of Cambridge's Signal Processing Laboratory and holds a PhD from the Athens University of Economics and Business. His research focuses on Bayesian inference methodologies applied to stochastic differential equations, with applications in financial econometrics, biomedical modeling (e.g., epidemic dynamics), and growth curve analysis. Dr. Kalogeropoulos teaches advanced courses including ST451: Bayesian Machine Learning and ST308: Bayesian Inference at LSE, as well as the summer school course ME315: Machine Learning in Practice. He is actively involved in academic administration as Programme Director of LSE’s MSc in Statistics and organizes the Theory of Machine Learning Reading Group. His professional affiliations include the Data Science Group and Social Statistics Group within the LSE Department of Statistics, as well as leadership roles in the Greek Stochastics Team. His research outputs span computational statistics and applied Bayesian methods, with notable contributions to spatio-temporal epidemic modeling, stochastic volatility analysis, and dynamic term structure modeling. He has collaborated with international institutions on projects addressing public health challenges and financial econometric applications.

Chengchun Shiمشاهده پروفایل

دانشیار

• Statistical Learning
• Reinforcement Learning
• Precision Medicine
• +۳ مورد دیگر

Dr. Chengchun Shi is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical learning methods for individualized decision-making and analyzing complex data, with applications in precision medicine, finance, and ride-sharing. He joined LSE in 2019 after completing his PhD in Statistics at North Carolina State University, supervised by Dr. Wenbin Lu and Dr. Rui Song. His work emphasizes developing computationally efficient algorithms with statistical guarantees for real-world challenges such as personalized treatment regimes, optimal investment strategies, and dynamic resource allocation. He has contributed to reinforcement learning, causal inference, and off-policy evaluation methodologies, addressing issues like interference in spatial experiments and handling high-dimensional, heterogeneous data. Key research areas include reinforcement learning algorithms, causal mediation analysis, and robust policy evaluation. His publications span topics like off-policy evaluation in confounded environments, spatial experimental designs, and deep learning approaches for distributional modeling. Dr. Shi’s research bridges theoretical statistics and practical applications, aiming to improve decision-making across diverse domains. He holds a strong academic background with a focus on high-dimensional statistical models and their applications in dynamic treatment regimes. His work has been recognized for its innovative approaches to tackling complex data challenges through interdisciplinary methods combining machine learning and rigorous statistical theory.

• Statistics
• Biostatistics
• Spatial Epidemiology
• +۵ مورد دیگر

Theresa Smith is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath, affiliated with centers including the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics (SAMBa), the Centre for Mathematics and Algorithms for Data (MAD), and the Centre for Therapeutic Innovation. Her research focuses on statistical methods for spatial and longitudinal data, with applications in health and social sciences, particularly clinical decision support tools. She holds a PhD in Statistics from the University of Washington (2014) and previously served as a Senior Research Associate at Lancaster University (2014–2016). Her research interests include spatial epidemiology, Bayesian inference, and biostatistical modeling. She leads projects such as the TULiPS study on psoriasis burden and the AI4CI Hub for collective intelligence research. Notable collaborations involve developing AI-driven solutions for public health challenges and advancing wastewater-based epidemiology for disease monitoring. Dr. Smith has secured grants from the NIHR, EPSRC, and the Royal Society, including a Short Industry Fellowship for predictive analytics in electronic health records. She advises students like N. Khaleel, whose thesis she supervised. Her work contributes to Sustainable Development Goals related to health and well-being. She is also part of interdisciplinary teams addressing environmental health risks and urban transport policy impacts.