Find Academic Supervisors

• Biostatistics
• Spatio-temporal Modeling
• Public Health Surveillance
• +5 more

David M. Kline is an Associate Professor and Interim Vice Chair in the Department of Biostatistics and Data Science within the Division of Public Health Sciences at Wake Forest University School of Medicine. He holds secondary faculty appointments in the Department of Epidemiology and Prevention and is an Affiliate Faculty member in the Department of Statistical Sciences. Additionally, he is a Full Member of the Center for Addiction Research and the Maya Angelou Research Center for Healthy Communities, and co-leads the Spatial and Environmental Statistics in Health (SESH) Lab. Dr. Kline earned his BA in Mathematics from Messiah College in 2010, followed by an MS in Statistics from The Ohio State University in 2012, and completed his PhD in Biostatistics from The Ohio State University in 2015. Prior to joining Wake Forest in 2021, he served as an Assistant Professor in the Department of Biomedical Informatics and was a member of the Center for Biostatistics at The Ohio State University College of Medicine. His research focuses on developing advanced biostatistical methodologies to address challenges in public health surveillance, with particular emphasis on multivariate Bayesian hierarchical models for spatio-temporal disease mapping. Dr. Kline specializes in leveraging multiple data sources to tackle complex public health problems, most notably the opioid epidemic. His methodological expertise includes small area estimation, spatio-temporal methods, multivariate modeling, and Bayesian statistics. He actively collaborates with subject matter experts across various health disciplines to apply these methods to pressing scientific questions. Dr. Kline's recent publications demonstrate a strong focus on opioid misuse estimation, spatio-temporal modeling of disease patterns, and public health surveillance methods. His work spans multiple domains including infectious disease tracking, substance use disorders, and healthcare access analysis. The publications reveal a consistent application of sophisticated statistical techniques to address real-world public health challenges with spatial and temporal dimensions. 2024 Emerging Leader Award, Wake Forest University School of Medicine 2023 & 2022 Top 10 Research Paper, Wake Forest University School of Medicine Division of Public Health Sciences 2020 Excellence in Teaching Award, The Ohio State University Department of Biomedical Informatics 2015 CSP Best Student Poster Runner-up, American Statistical Association 2014 Lester R. Curtin Award, American Statistical Association Dr. Kline serves as Principal Investigator on multiple NIH-funded research projects, including R21DA045236 and R01DA052214 focused on spatio-temporal methods for surveillance of the opioid syndemic. He mentors several trainees, including F31 and F30 recipients. His collaborative work spans numerous interdisciplinary projects addressing health disparities, infectious disease surveillance, and substance use disorders. Currently, he serves on the Editorial Board for the journal Epidemiology, reflecting his standing in the field. As co-leader of the Spatial and Environmental Statistics in Health Lab, Dr. Kline directs research that bridges biostatistics, spatial analysis, and public health. His lab focuses on developing innovative methods for analyzing health data with spatial and temporal components, particularly for problems involving small area estimation and multi-source data integration. This work has significant implications for public health policy and practice, especially in addressing the opioid crisis and health disparities.

• Wireless Networks
• Mobile Computing
• Smart Grid Systems
• +4 more

Vincent Gauthier is a Professor at Telecom Paris, part of Institut Polytechnique de Paris, with a distinguished research career spanning over two decades in mobile network analysis, urban mobility modeling, and smart infrastructure systems. His research portfolio demonstrates deep expertise in leveraging mobile network metadata for urban population estimation, transportation optimization, and smart grid applications. Dr. Gauthier's research interests center on wireless networks, mobile computing, smart grid systems, urban mobility analysis, and electric vehicle infrastructure. His work bridges theoretical network modeling with practical urban applications, particularly evident in his studies of mobility patterns in Paris and Peru. His research methodology combines mobile network metadata analysis with advanced computational techniques to address urban infrastructure challenges. His recent publication trends show a strategic pivot toward sustainable transportation solutions, with multiple 2024-2025 publications focusing on electric vehicle charging infrastructure optimization. Earlier work established foundational contributions to understanding urban dynamics through mobile network data, with significant publications on population estimation and transport mode detection. His research consistently demonstrates interdisciplinary approaches, connecting telecommunications engineering with urban planning and environmental sustainability. Dr. Gauthier has maintained long-term collaborations with researchers including Monique Becker, Michel Marot, and Hassine Moungla, with whom he has co-authored numerous publications over multiple years. His work shows particular strength in applications to Peru and other LMIC countries, reflecting a commitment to globally relevant research. His research methodology combines theoretical network modeling with practical urban applications, particularly evident in his studies of mobility patterns in Paris and Peru. His work bridges telecommunications engineering with urban planning and environmental sustainability, creating impactful solutions for modern cities.

Chong WangView profile

Professor

• Biostatistics
• Meta-Analysis
• Disease Surveillance
• +4 more

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 DassView profile

Professor

• Statistics
• Bayesian Statistics
• Data Science
• +7 more

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 YooView profile

Assistant Professor

• Uncertainty Quantification
• Statistical Computing
• Deep Learning
• +3 more

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 PetersonView profile

Adjunct Associate Professor

• Environmental Monitoring
• Spatial and Temporal Statistics
• Geographic Information Science (GIS)
• +3 more

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 SteinView profile

Research Fellow

• Human-Computer Interaction
• Ubiquitous Computing
• Machine Learning
• +2 more

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 MahoneyView profile

Professor

• Machine Learning
• Big Data
• Statistical Analysis
• +5 more

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
• +4 more

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
• +7 more

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 KalogirosView profile

Lecturer

• Data Science
• Mathematical Modelling
• Mathematical Biology
• +7 more

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
• +3 more

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 ErgonView profile

Associate Professor

• Ecological statistics
• Life-history theory
• Population dynamics
• +5 more

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 TandonView profile

Research Fellow

• Spatio-temporal Statistics
• Climate Extremes Modeling
• Precipitation Analysis
• +6 more

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 RodriguezView profile

Professor

• Bayesian statistics
• Machine learning
• Nonparametric methods
• +3 more

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.