جست‌وجوی استاد و پژوهشگر

Chong Wangمشاهده پروفایل

استاد

• Biostatistics
• Meta-Analysis
• Disease Surveillance
• +۴ مورد دیگر

Dr. Chong Wang is a Professor at Iowa State University, holding dual appointments in the Department of Veterinary Diagnostic and Production Animal Medicine and the Department of Statistics. He specializes in Biostatistics, Meta-Analysis, and Disease Surveillance, with a focus on veterinary applications such as livestock disease control and diagnostic test evaluation. Education: PhD in Statistics (Cornell University, 2006), MS in Statistics (Cornell University, 2004), BS in Statistics and Probability (Peking University, 2001). Research interests include systematic reviews, machine learning applications in veterinary epidemiology, and statistical methods for animal disease surveillance. His work emphasizes optimizing trial designs and analyzing antimicrobial resistance patterns. He has advised numerous PhD and Master’s students in biostatistics and veterinary science. Publications span over 150 articles, focusing on network meta-analysis, PRRS virus dynamics, and diagnostic assay validation. Collaborations include studies on porcine epidemic diarrhea virus and swine health management. Affiliations include leadership roles in statistical methodologies for veterinary research and contributions to grants on disease surveillance and antimicrobial stewardship.

Sarat Dassمشاهده پروفایل

استاد

• Statistics
• Bayesian Statistics
• Data Science
• +۷ مورد دیگر

Sarat Dass is a Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. He is actively engaged in research, teaching, and supervising PhD students. His primary research interests span Statistics, Bayesian Statistics, Data Science, and their applications in Epidemiology and Spatio-temporal processes. He has published extensively, with 136+ research outputs since 2006, focusing on topics like ionospheric TEC forecasting, disease modeling (e.g., chikungunya, COVID-19), and statistical computing methods. Education & Teaching: Teaches courses such as F79BI: Bayesian Inference and Computational Methods, emphasizing advanced statistical methodologies and computational techniques. Research Interests: His work integrates machine learning (e.g., LSTM, neural networks) with statistical modeling for environmental and health applications. Key areas include predicting ionospheric disturbances during solar flares/earthquakes, analyzing disease spread dynamics, and optimizing hydrocarbon exploration using Gaussian processes. Collaborations: Collaborates internationally, particularly in Malaysia, Indonesia, and Pakistan, on projects involving epidemiology, space weather, and geophysical modeling. His research contributes to Sustainable Development Goals related to health, climate action, and innovation. Advising & Grants: Accepts PhD students and has secured grants for projects on disease modeling and environmental data analysis. His work bridges theoretical statistics with practical applications in public health and geophysics.

William Weimin Yooمشاهده پروفایل

استادیار

• Uncertainty Quantification
• Statistical Computing
• Deep Learning
• +۳ مورد دیگر

William Weimin Yoo is an Assistant Professor at the School of Mathematical & Computer Sciences, Heriot-Watt University. His research focuses on Bayesian nonparametrics, uncertainty quantification, statistical computing, deep learning, and dark matter physics. He has worked internationally across the UK, Netherlands, France, US, and Canada. He teaches courses including F21BD: Big Data Management, F70TS: Time Series and Machine Learning, and F90AM: Advanced Machine Learning. Yoo is actively supervising PhD students in topics such as uncertainty quantification for clustering and accelerated posterior contraction rates. He emphasizes interdisciplinary research, combining statistical theory with applications in physics and sports analytics. His work bridges computational methods and theoretical guarantees in Bayesian frameworks. His academic contributions span methodological advancements in posterior contraction, credible sets, and spatio-temporal modeling. He frequently engages in collaborative research and peer review across top journals and conferences in statistics and machine learning.

Erin Petersonمشاهده پروفایل

دانشیار مدعو

• Environmental Monitoring
• Spatial and Temporal Statistics
• Geographic Information Science (GIS)
• +۳ مورد دیگر

Dr. Erin Peterson is an Adjunct Associate Professor at Queensland University of Technology (QUT), affiliated with the Faculty of Science, Tier 1 Research Centre - Science (U91), and the Centre for Data Science. She is also an Associate Investigator at the ARC Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS) and the owner of EP Consulting in Brisbane. With over 20 years of experience, her work bridges natural resource management, GIS, and environmental statistics, focusing on innovative environmental monitoring using new technologies like in situ sensors and citizen science. Education: B.S. in Conservation Forestry (Michigan State University), M.S. and Ph.D. in Earth Resources (Colorado State University). Prior roles include Senior Research Scientist at CSIRO and leader of the Environmental & Agricultural Informatics team. Research interests include spatio-temporal modeling, adaptive monitoring frameworks, and integrating diverse datasets for ecological assessments. Key projects involve virtual reality for conservation modeling, jaguar population studies, and Great Barrier Reef monitoring. She teaches spatial statistical modeling courses and develops tools to make complex methodologies accessible to non-statisticians. Grants include leadership in Agri-Intelligence for Cotton Production and sustainability reporting projects. Collaborations span government, industry, and international institutions.

Sebastian Steinمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Ubiquitous Computing
• Machine Learning
• +۲ مورد دیگر

Dr. Sebastian Stein is a Research Fellow in the School of Computing Science at the University of Glasgow, working within the Inference, Dynamics and Interaction Research Group. His current research focuses on the EPSRC-funded project 'Closed-Loop Data Science for Complex, Computationally- and Data-Intensive Analytics' under Professor Roderick Murray-Smith. With a background spanning human-computer interaction, ubiquitous computing, and machine learning, his research integrates computer vision, action recognition, and intelligent interactive systems. He holds a PhD in Computing from the University of Dundee and a Diplom in Computer Science and Electrical Engineering from TU Dortmund. Stein's publications demonstrate expertise in multimodal sensing systems, rehabilitation engineering, probabilistic modeling interfaces, and intelligent transportation solutions. Recent work includes EEG-based pain biomarkers, interactive Bayesian models, and COVID-19 containment strategies through control theory. His technical contributions span healthcare applications like spinal cord injury rehabilitation, neuroprosthetics development, and urban computing platforms for built environment analysis.

Michael Mahoneyمشاهده پروفایل

استاد

• Machine Learning
• Big Data
• Statistical Analysis
• +۵ مورد دیگر

Michael Mahoney is a Professor in the Department of Statistics at the University of California, Berkeley. He holds roles as Vice President and Director of the Big Data Group at the International Computer Science Institute (ICSI), Group Lead for the Machine Learning and Analytics Group at Lawrence Berkeley National Laboratory (LBNL), and a core member of the RISELab within the Department of Electrical Engineering and Computer Sciences (EECS). He is also an Amazon Scholar. His research focuses on the applied mathematics of data, including randomized numerical linear algebra (RandNLA), optimization, and their applications in machine learning, climate science, genetics, and other domains. He teaches courses such as Linear Algebra for Data Science and has led initiatives like the FODA (Foundations of Data Analysis) Institute under the NSF TRIPODS program. His work emphasizes scalable algorithms, including contributions to RandBLAS/RandLAPACK frameworks and tools like SuperBench and SqueezeLLM. Mahoney collaborates with industry and academia, advancing methods for scientific machine learning and efficient AI infrastructure. Roles: Professor (UC Berkeley), Vice President (ICSI), Group Lead (LBNL), RISELab Member Education: Ph.D. in Computer Science (Yahoo! Research and Stanford University background) Research interests include algorithmic and statistical foundations of big data, with applications in internet analysis, climate modeling, and genomics. His work bridges theory and practice, developing scalable tools for high-dimensional data analysis. He advises numerous students and postdocs, contributing to projects like RandNLA, neural scaling laws, and physics-informed learning. His labs and teams focus on foundational methods for scientific machine learning and efficient AI systems.

• Hydrology
• Climate Dynamics
• Water Resource Systems Analysis
• +۴ مورد دیگر

Dr. Upmanu Lall is the Director of the Columbia Water Center and the Alan and Carol Silberstein Professor of Engineering at Columbia University. He chairs the Department of Earth & Environmental Engineering in the Fu Foundation School of Engineering and Applied Science. His research focuses on hydrology, climate dynamics, water resource systems, risk management, and sustainability, with a particular emphasis on societal outcomes and innovative applications. His major initiatives include the Global Water Sustainability Initiative addressing water scarcity, the Global Flood Initiative for flood prediction and mitigation, and 'America's Water' for sustainable urban infrastructure. These programs leverage systems-level modeling of hydrology, climate, agronomy, and economics. He pioneered techniques in nonlinear dynamical systems, Bayesian models, and climate variability studies. Dr. Lall's work spans over 30 projects, including disaster planning in New York, climate-informed flood risk assessments, and groundwater depletion studies in India. His research integrates interdisciplinary approaches to address water-energy-food nexus challenges, climate adaptation, and community-driven solutions. He leads the Columbia Water Center's educational initiatives, such as the Aquanauts program promoting citizen science and sustainable engineering education. His publications emphasize probabilistic climate projections, flood hazard analysis, and participatory water development strategies. Collaborations with institutions like The Nature Conservancy highlight his focus on practical, scalable solutions to global water crises.

• Network Analysis
• Graph Modeling
• Local Content Development
• +۷ مورد دیگر

Ebenezer Afrifa-Yamoah is a researcher at Edith Cowan University's School of Science with a diverse research portfolio spanning environmental science, public health, statistics, and education. His work demonstrates a strong focus on Ghanaian contexts while addressing global challenges in resource management, health, and education. Dr. Afrifa-Yamoah's research interests include: Network analysis and graph modeling of complex systems Local content development in resource extraction sectors Missing data imputation techniques for environmental monitoring Diabetes research and management in Ghanaian populations Entrepreneurship education and student outcomes Climate change impacts and CO2 emissions analysis His methodological expertise spans path analysis, mediation modeling, Bayesian statistics, and network analysis to address complex problems across multiple sectors. Recent publications (2024-2025) show increasing focus on sustainable resource management in African contexts and application of data science to agricultural, public health, and environmental challenges. His work on Ghana's oil and gas sector revealed the centrality of institutional characteristics in driving local content development, while finding economic growth was the least integrated domain in the ecosystem. His scientific contributions include: Methodological advances in missing data imputation for environmental monitoring Analysis of diabetes management biomarkers in Ghanaian populations Studies on occupational health and safety in Ghanaian mining Research on postharvest technology for agricultural products like jackfruit and persimmon Investigations of educational assessment design during the pandemic Dr. Afrifa-Yamoah maintains active collaborations between Australian and Ghanaian institutions, with his interdisciplinary work demonstrating strong methodological rigor across diverse application areas.

Dimitris Kalogirosمشاهده پروفایل

مدرس

• Data Science
• Mathematical Modelling
• Mathematical Biology
• +۷ مورد دیگر

Dr Dimitris Kalogiros is a Lecturer in Data Science at Queen Mary University of London and the London City Institute of Technology. He holds a Fellowship from the Higher Education Academy (Advance HE) and is a certified Mental Health First Aider (MHFA England). His academic journey includes an MEng in Applied Mathematics and Physics from the National Technical University of Athens, an MSc in Applied Mathematical Sciences with Biological and Ecological Modelling (Distinction) from Heriot-Watt University, and a PhD in Mathematical Modelling of Plant Root Systems from the University of Dundee. His research focuses on interdisciplinary applications of mathematical modelling in biology, ecology, epidemiology, and data science. Key interests include root system architecture, mathematical epidemiology, and the development of computational pipelines for integrating data and models. He is actively involved in teaching, particularly in empowering students through data science education, and has contributed to modules such as “Data Analysis and Data Solutions” and “Applied Data Science.” Dr Kalogiros has held roles at the University of Nottingham’s Centre for Mathematical Medicine and Biology, the Bristol Medical School, and has supervised students across multiple disciplines. His awards reflect his commitment to education and mental health advocacy. He organizes initiatives like the Practical Machine Learning Summer School and serves on committees promoting early career researchers’ well-being and professional development.

• Environmental Statistics
• Spatio-Temporal Modeling
• Bayesian Hierarchical Models
• +۳ مورد دیگر

Michael Bertolacci is a Senior Research Fellow at the School of Mathematics and Applied Statistics, University of Wollongong. He holds a PhD from the University of Western Australia (2016–2020). His research focuses on large-scale spatio-temporal problems in environmental statistics, including hierarchical Bayesian models for rainfall analysis, nonstationary time series methods, and flux inversion for trace gases. Current work emphasizes offshore engineering applications. He has secured funding from the University of Wollongong and NASA, collaborating on projects like WOMBAT v2.0, a Bayesian flux-inversion framework. His GitHub repositories reflect computational contributions to statistical methods and geospatial modeling.

Torbjørn Håkan Ergonمشاهده پروفایل

دانشیار

• Ecological statistics
• Life-history theory
• Population dynamics
• +۵ مورد دیگر

Torbjørn Håkan Ergon is an Associate Professor at the University of Oslo's Department of Biosciences, working at the Centre for Ecological and Evolutionary Synthesis (CEES). He also serves as the director of the Finse Alpine Research Center and is an associate editor of Methods in Ecology and Evolution. His educational background includes: BSc and MSc from the University of Bergen PhD from the University of Oslo Additional PhD work at the University of Aberdeen, Scotland He completed a post-doctoral fellowship at Patuxent Wildlife Research Centre (Maryland) and University of Tromsø, and has conducted research at University of Otago (New Zealand), University of Wellington (New Zealand), University of California, Berkeley, and Cornell University (NY). Ergon's research focuses on understanding variation in life-history traits and demographic rates within populations and linking this to population level patterns and processes such as delayed density dependence. His work combines theoretical and empirical approaches, with particular emphasis on developing statistical models to estimate (correlated) distributions of latent life-history traits within populations. This research has significant implications for evolutionary processes, especially the evolution of phenotypic plasticity in life-history traits. His broader research interests include long term bird population studies, bioacoustics, and statistical modeling in ecology. His research spans multiple areas including small rodent population dynamics, seabird ecology, and the development of innovative statistical methods for ecological data analysis. Ergon has led and participated in numerous research projects including BIODIVTRENDS, studies on Fennoscandian bird populations, fishing-induced eco-evolutionary changes, and spatio-temporal dynamics of small rodents. Ergon teaches several courses at the University of Oslo: Design and analysis of biological studies (BIOS3000/4000) Alpine Ecology (BIOS5312/9312) Terrestrial Zoology (BIOS2210) Life history strategies and climate effects (BIOS 5112/BIOS 9112) His scientific work has been widely cited, with 78 publications accumulating over 19,000 reads and 2,600+ citations according to ResearchGate. His research has been published in leading journals including Nature, Proceedings of the National Academy of Sciences, Journal of Animal Ecology, Ecology, and Methods in Ecology and Evolution. As an associate editor for Methods in Ecology and Evolution, Ergon plays an important role in advancing methodological approaches in ecological research. His leadership of the Finse Alpine Research Center facilitates important alpine ecosystem studies in Norway.

Kushagri Tandonمشاهده پروفایل

پژوهشگر ارشد

• Spatio-temporal Statistics
• Climate Extremes Modeling
• Precipitation Analysis
• +۶ مورد دیگر

Kushagri Tandon is a Doctoral Research Fellow at the Department of Statistics and Data Science, University of Oslo, affiliated with the Faculty of Mathematics and Natural Sciences. Her research develops advanced statistical models for spatio-temporal processes to improve prediction of climate extremes, particularly precipitation patterns in Norway's Oslofjord region. She investigates how temperature-precipitation interactions and local weather memory influence rainfall simulations. Research Focus: Kushagri's work integrates Bayesian methods (Neural Networks, Inference, PAC Bayes) with spatio-temporal modeling to enhance climate resilience planning. Key interests include: Capturing space-time dependencies in high-resolution climate data Developing flexible models for extreme weather events Improving forecasting accuracy through historical pattern analysis Applications in Norwegian climate adaptation strategies Education: M.Sc. Mathematics (Ranked 1 st , CGPA 9.944) - Indian Institute of Technology Delhi B.Sc. Honors Mathematics (CGPA 9.405) - Lady Shri Ram College, University of Delhi Teaching Experience: Has taught undergraduate and graduate courses including: Statistical Inference Advanced Probability Theory Nonparametric Statistical Inference Multivariate Statistical Methods Supervision: Advised by Prof. Thordis Linda Thorarinsdottir (Principal) and Prof. Geir Olve Storvik (Co-supervisor).

Abel Rodriguezمشاهده پروفایل

استاد

• Bayesian statistics
• Machine learning
• Nonparametric methods
• +۳ مورد دیگر

Abel Rodriguez is Professor of Statistics and Department Chair at the University of Washington, with affiliate appointments at the eScience Institute and Center for Statistics in the Social Sciences. His research centers on Bayesian statistics and machine learning, specializing in nonparametric methods, spatio-temporal modeling, network analysis, and extreme value theory for applications in biology, social sciences, and engineering. He develops methodological frameworks addressing complex real-world data challenges through innovative statistical modeling. Dr. Rodriguez has received significant recognition including: Elected Member, International Statistical Institute (ISI) (2024) Fellow, American Statistical Association (ASA) (2022) He maintains active scholarly engagement through conference presentations at venues like OBayes and ISBA World Meetings, and collaborates extensively across disciplinary boundaries through his institute affiliations.

Stuart Clarkمشاهده پروفایل

استاد

• Sedimentary basin dynamics
• Machine learning applications in geology
• Numerical simulations of Earth processes
• +۵ مورد دیگر

Professor Stuart Clark is a distinguished academic at the University of New South Wales (UNSW), currently serving as Professor in the Civil and Environmental Engineering department within the Faculty of Engineering. He was appointed Director of Governance for the Faculty of Engineering in 2024 and promoted to Professor in 2025. Previously, he was an Associate Professor (2021-2025) and Senior Lecturer (2017-2021) in the Minerals and Energy Resources School at UNSW. His educational background includes a PhD in Geophysics from the University of Sydney (2007), Master of Arts from the University of Melbourne (2004), and BSc.(Hons)/B. Arts from the University of Sydney (2002). He also earned a Graduate Certificate in University Learning and Teaching from UNSW in 2022. Professor Clark's research focuses on understanding the influence of deep Earth processes on sedimentary basin development and applying machine learning to geological modeling. His work spans quantitative sedimentary basin dynamics, numerical simulations of Earth processes like subduction and sediment transport, and innovative applications at the intersection of geology and machine learning. His research has significant implications for sustainable resource use and exploration. His recent publications reveal a strong emphasis on basin analysis, particularly in the Northern Carnarvon Basin, with applications spanning petroleum geology, resource distribution, and geological modeling using advanced statistical methods like Bayesian inference. His work increasingly bridges traditional geology with computational approaches, including machine learning applications for fracture detection, fault identification, and image analysis of geological structures. Arc Postgraduate Council's Supervisor Award (2021) UNSW Vice Chancellor's Teaching Excellence Award - Rising Star (2019) UNSW Engineering Hero Award (2020) Professor Clark has successfully secured multiple significant research grants, including the Geodynamical Assessments of Subsidence in the North West Shelf in Australia (2024-2025), the ARC Industry Transformation Research Hub for Resilient and Intelligent Infrastructure Systems (2021-2027), and several Australian Research Council Linkage projects. He currently supervises numerous PhD and research master's students working on diverse projects related to basin evolution, dynamic topography, fracture mechanics, and resource exploration. His teaching portfolio includes undergraduate and postgraduate courses in geology, sedimentary and energy resources, and seismic imaging.

Dipak Deyمشاهده پروفایل

استاد

• Bayesian analysis
• Bioinformatics
• Biostatistics
• +۸ مورد دیگر

Dipak Dey is a Professor in the Department of Statistics at the University of Connecticut . His work bridges theoretical and applied statistics, with a focus on Bayesian methodologies and computational statistics. Affiliations : University of Connecticut, Department of Statistics Contact : dipak.dey@uconn.edu , Office: AUST 327, Phone: (860) 486-4755 His research interests span Bayesian analysis , Biostatistics , Computational statistics , Statistical genetics , and Spatio-temporal modeling . He has pioneered techniques in spatial curvature processes and scalable Bayesian algorithms for large datasets. Recent publications highlight his contributions to Bayesian spatial modeling (blockNNGP, curvature processes), survival analysis (skew-t distributions, cure rate models), and computational statistics (variable selection in Gaussian processes, fast inference algorithms). Applications include insurance data , epidemiology , and environmental statistics . Scientific Awards : Board of Trustees Distinguished Professor (University of Connecticut) He actively collaborates on interdisciplinary projects and mentors researchers in advanced statistical methodologies for complex data structures.