Pamela Prentice is a Lecturer in Animal and Veterinary Sciences at Scotland's Rural College (SRUC), with a focus on Animal Welfare. She is stationed at the Roslin Institute Building in Midlothian, UK, and her ORCID identifier is 0000-0002-6290-9581. She is currently accepting PhD students. Her research primarily revolves around the behavioral and genetic aspects of animal welfare, with a focus on species such as guppies (Poecilia reticulata), Atlantic salmon (Salmo salar), and captive fish populations. Key themes include stress response predictability, cognitive performance, and the impact of environmental enrichment on welfare. She has contributed to projects like 'Developing further evidence requirements for precision bred animals' (2024), emphasizing animal health and welfare in breeding practices. Her publications span topics such as genetic influences on stress behaviors, cognitive performance in guppies, and environmental interventions in aquaculture. Notable contributions include studies on stress coping mechanisms and the application of animal personality traits to reduce chronic stress in captive fish. Prentice has also authored reports for DEFRA, addressing the welfare implications of precision breeding techniques. Her work frequently intersects genetics, behavior, and applied welfare solutions in both wild and captive animal populations.
Dr. Gabriel Bertolini is a postdoctoral research fellow at the University of British Columbia's Department of Earth, Ocean, and Atmospheric Sciences. He holds a Ph.D. in Geosciences from the University of Aberdeen and Universidade Federal do Rio Grande do Sul (2020), focusing on ancient sedimentary systems. His expertise includes geochemical analysis, geochronology, and petrology of detrital minerals, with applications in multivariate statistical modeling of sedimentary processes. Prior roles include postdoctoral fellowships and lecturing at Universidade Federal do Rio Grande do Sul (2021–2023) and research at Unisinos (2024) applying machine learning to sedimentary basins. Research spans South America, South Africa, China, and Europe, emphasizing sedimentary provenance, aeolian-fluvial systems, and intracratonic basins. Technical skills include R/Python for geostatistical analysis. Research interests include: Sedimentary provenance analysis Volcano-sedimentary interactions Stratigraphy of continental basins Data-driven geology Recent work focuses on Cretaceous aeolian systems across Gondwana, integrating U-Pb zircon dating with spatial statistical methods. His studies address continental margin correlations, grain-size distribution dynamics, and tectono-climatic controls on sediment dispersal systems. Notable contributions include: Atlantic margin sand-provenance correlation (2024) Grain-size controls in ancient ergs (2023) Paraná Basin stratigraphic reconstructions (2023) Collaborative projects involve international teams studying South China's Cretaceous glacial systems and Brazilian mafic-ultramafic complexes. His work bridges field geology, analytical geochemistry, and computational modeling to advance understanding of deep-time sedimentary processes.
Ralf Andreas Wilke is a Professor in the Department of Economics at the University of Copenhagen (Copenhagen Business School). He holds a PhD in Economics from the University of Dortmund (2002). His research focuses on Applied Econometrics, Microeconometrics, and Quantitative Methods. Wilke coordinates the Minor in Quantitative Methods in Economics, Business and Finance and teaches advanced econometrics courses at the MSc and PhD levels. His research interests include duration models, competing risks analysis, and econometric methodologies for industrial and historical data. Recent work addresses issues like dependent censoring in risk modeling and spatial patterns in technological diffusion. Wilke has published extensively in journals such as Computational Statistics & Data Analysis , Quality Engineering , and Cliometrica . No scientific awards are explicitly listed, but his active research projects include contributions to the Pension Research Center. He advises PhD courses in Advanced Econometrics and collaborates on research projects involving panel data structures and statistical methodologies.
Semhar Michael is an Associate Professor of Statistics at the Department of Mathematics and Statistics, South Dakota State University (SDSU). She holds a B.S. in Applied Mathematics from the University of Asmara (2006), an M.S. in Mathematics from the University of North Dakota (2011), and a Ph.D. in Applied Statistics from the University of Alabama (2015). Her research focuses on computational statistics, including mixture modeling, unsupervised learning, and clustering, with applications to forensic science, health informatics, renewable energy, and precision agriculture. Dr. Michael’s academic responsibilities include teaching advanced statistics courses such as Statistical Inference I/II, Multivariate Analysis, and Statistical Computation. She has served as an associate editor for the Journal of Classification and organized conferences like the SDSU Annual Data Science Symposium. Her professional memberships include the American Statistical Association (ASA), Institute of Mathematical Statistics (IMS), and International Institute for Analytics (IIA). Her research has led to awards such as the 2017 Certificate of Appreciation and the 2016 ASA Travel Award. Key projects include grants from the National Science Foundation (NSF) and NIH, focusing on statistical methods for latent subpopulations, health disparities in kidney disease, and precision agriculture innovation. She collaborates with interdisciplinary teams on topics like AI-driven healthcare bias detection and climate-smart agriculture strategies. Dr. Michael’s work bridges statistical theory and real-world applications, emphasizing methodological innovation and societal impact. Her lab’s research extends to forensic source identification, solar irradiance forecasting, and text mining of large datasets.
Robert Krueger is an Assistant Professor in the Department of Computer Science & Engineering at NYU Tandon School of Engineering, and a member of the Visualization Imaging and Data Analysis Center (VIDA) at NYU. Previously, he held a joint postdoctoral appointment as a Senior Research Scientist and Subgroup Lead at Harvard University's Visual Computing Group (VCG) and Laboratory of Systems Pharmacology (LSP). His research focuses on scalable visualization and visual analytics for spatial and multivariate data, particularly in biomedical and geographical applications like smart cities and cancer tissue analysis. Education: Ph.D. in Computer Science (Dr. rer. nat.), University of Stuttgart, 2017 M.S. in Computer Science and Media, Stuttgart Media University, 2012 B.S. in Media and Communication Informatics, Reutlingen University, 2008 Research Interests: Krueger specializes in integrating machine learning with interactive visual interfaces to enable human-in-the-loop analysis of large biomedical and spatial datasets. He designs tools for multiplexed imaging data exploration, spatial biology, and geospatial dynamics. His work emphasizes interdisciplinary collaboration to address challenges in smart cities, immuno-oncology, and data standardization (e.g., MITI guidelines). Organization Committees: He actively contributes to the VIM (Visualization and Image Data Management) monthly meetings and the Spatial Biology Association (SBA), advancing data interoperability and spatial biology applications. His teaching accolades include multiple Harvard Excellence in Teaching Awards. Grants and Advising: As a subgroup leader since 2021, he manages research collaborations between VCG and LSP. He has supervised 14 student trainees/researchers on projects like CIMPLEX and HTAN. His team develops open-source tools like Minerva and Scope2Screen. Labs/Teams: Affiliated with VIDA at NYU and VCG/Harvard Medical School. He leads visualization efforts in the Human Tumor Atlas Network (HTAN), funded under the NCI Cancer Moonshot Initiative.
Hsin Hsu is a Research Fellow affiliated with the Geosciences and Atmospheric and Oceanic Sciences (GEO/AOS) departments at Princeton University. They are a core member of the Fueglistaler Research Group , dedicated to advancing climate science through interdisciplinary investigations. Dr. Hsu’s research focuses on understanding complex land-atmosphere interactions, particularly how soil moisture regimes influence extreme weather events like droughts and heatwaves. Their work integrates climate modeling frameworks to evaluate projections of precipitation patterns, nonlinear terrestrial processes, and paradoxical hydrological phenomena such as soil drying despite increased rainfall. Key themes include exploring critical soil moisture thresholds, hypersensitive coupling mechanisms, and the implications of CO2-driven evaporation shifts on environmental systems. No scientific awards are explicitly listed in the provided information. Advising roles and grant details are not mentioned, though their contributions to the Fueglistaler Research Group highlight active participation in collaborative projects. They are part of a team focused on resolving climate model uncertainties and advancing methodologies in climate change impact assessments.
Mario Palendeng is an Adjunct Professor at Charles Darwin University's Faculty of Science and Technology, affiliated with the North Australian Centre for Oil and Gas (NACOG) and Energy and Resources Institute. He holds a PhD in Electrical and Electronics Engineering from the University of Queensland (2016). His research focuses on big data analytics, machine learning applications in energy sectors, and signal processing for healthcare monitoring. He leads projects like the 'Residential Inverter Testing Services at CDU Microgrid' (2022), addressing microgrid stability and renewable energy integration. Education: PhD, Electrical and Electronics Engineering, University of Queensland (2016) Research interests span cybersecurity in IoT smart cities, distributed microgrid control systems, and livestock age estimation via spectroscopy. His work contributes to UN Sustainable Development Goals related to affordable energy and sustainable agriculture. Recent publications explore smart city cyber attack prevention, inverter testing methodologies, and cattle age determination using reflectance measurements. Collaborations include interdisciplinary projects with industry partners.
Brendan Kelly is an Associate Professor of Cotton Fiber Phenomics at Texas Tech University's Department of Plant and Soil Science, with a joint appointment at Texas A&M AgriLife Research. He leads the Cotton Phenomics Laboratory at the Fiber and Biopolymer Research Institute, focusing on improving cotton fiber quality through advanced data analysis and industry collaboration. His work integrates modern instrumentation and multivariate approaches to understand fiber variability and its impact on textile processing. Dr. Kelly holds a Ph.D. and bachelor’s degree in mathematics from Texas Tech University. His teaching includes foundational courses like PSS 100 (Freshman Seminar), PSS 1321 (Agronomic Plant Science), and specialized courses such as PSS 5370 (Cotton Fiber-Textile Industries). His research emphasizes cotton fiber elongation, genetic improvement, and the development of protocols for fiber quality evaluation. Key research areas include cotton fiber morphology, climate impacts on yield, and the application of artificial intelligence in phenotyping. His work bridges agricultural science with textile industry needs, aiming to enhance cotton’s utility as an industrial raw material. Collaborations with industry partners ensure practical applications of his findings. Labs and teams include the Cotton Phenomics Lab and affiliations with Texas Tech’s Fiber and Biopolymer Research Institute. Future work focuses on advancing sustainable cotton production and improving yarn quality through genetic and agronomic strategies.
Brian Hugh Neelon is a Professor and Graduate Training Director for Biostatistics in the Department of Public Health Sciences at the Medical University of South Carolina (MUSC). He holds joint appointments at the Ralph H. Johnson VA Medical Center and the Biostatistics Shared Resource at Hollings Cancer Center. His research focuses on developing Bayesian hierarchical models for longitudinal and spatial data, with applications spanning infectious/chronic diseases, maternal/child health, environmental health, and health services research. Research Interests: Bayesian inference, longitudinal/spatial data analysis, zero-inflated models, latent growth modeling, and health disparities. His work integrates statistical innovation with real-world public health challenges, such as COVID-19 surveillance and social vulnerability assessments. Publications: Recent articles emphasize Bayesian methods in spatiotemporal modeling of pandemics (e.g., COVID-19), zero-inflated data frameworks, and health equity. Key themes include geospatial analysis of disease spread, policy impacts on public health, and computational advances in biostatistics. Awards: Recipient of the AJPM 2021 Paper of the Year Award for research on political affiliations and COVID-19 outcomes. Education & Advising: As Graduate Training Director, he oversees biostatistics students and collaborates with interdisciplinary teams. No specific grant or lab details are provided in the source text.
Dr. Matthew Volovski is an Assistant Professor of Transportation Engineering at Manhattan University's Department of Civil & Environmental Engineering. He holds a Ph.D. in Civil Engineering from Purdue University, alongside M.S. and B.S. degrees from Purdue and Northeastern University respectively. His research focuses on transportation data visualization, econometric modeling, infrastructure finance, and asset management. Dr. Volovski is actively involved in organizations like the Institute of Transportation Engineers (ITE) and the American Society of Civil Engineers (ASCE), contributing as a journal reviewer and committee member. His academic work emphasizes spatial analysis, predictive modeling for infrastructure, and sustainable financing strategies. Notable projects include studies on highway maintenance cost estimation, bridge condition forecasting, and the impact of autonomous vehicles on transportation systems. Dr. Volovski has received prestigious awards such as the Indiana ITE Edward J. Cox Memorial Scholarship and the International Road Federation Essay Competition award. Education: Ph.D. in Civil Engineering, Purdue University M.S. in Civil Engineering, Purdue University B.S. in Civil Engineering, Northeastern University Professional Memberships: ASCE Planning, Economics, and Financing Committee Member ITE Manhattan College Chapter Faculty Advisor Board Member, University Transportation Research Center Region 2 His recent research trends highlight advanced statistical methods (e.g., neural networks, random-parameter models) applied to transportation challenges, alongside policy analyses of infrastructure funding and socio-economic impacts. Dr. Volovski’s grants include studies on transit ridership in NYC and user equity in transportation finance.
Paolo Bocchini is a Professor and Director of the Center for Catastrophe Modeling and Resilience at Lehigh University, also serving as Director of Graduate Programs in Civil and Environmental Engineering. He specializes in probabilistic methods for civil infrastructure resilience, catastrophe modeling, and computational mechanics. His research bridges engineering and policy, emphasizing interdependent infrastructure systems' recovery under natural disasters. Education: Ph.D. in Structural Mechanics from the University of Bologna (2008), Laurea (B.Sc. + M.Sc.) in Civil Engineering (2004), and Degree from Collegio Superiore (2004), all at the University of Bologna. Research focuses on: Catastrophe modeling for hurricanes, earthquakes, and wildfires Resilience metrics for transportation and energy systems AI applications in disaster management Climate change impacts on infrastructure 3D concrete printing for thermal energy storage Key projects include the NSF-funded PRAISys initiative (2017-2022), involving 58 researchers to model infrastructure resilience, and leadership roles in ASCE's Objective Resilience Manual. His work informs U.S. Congress guidelines for transportation resilience funding and contributes to global climate adaptation strategies. Awards: Fellow of the Structural Engineering Institute of ASCE Grants and collaborations include a $12M DOE grant for marine energy resilience (2024) and a $1M DOE Climate Resilience Center (2024). He advises on policy through National Academies committees and co-leads ASCE's climate impact publications. His lab's website: www.paolobocchini.com .
Garry Russ is an Adjunct Professor at James Cook University, specializing in coral reef ecology and fisheries management. His research focuses on the ecological and human-driven factors influencing marine ecosystems, particularly in the context of marine protected areas (MPAs) and coral reef resilience. He has extensively studied the impacts of environmental disturbances, fishing pressures, and habitat dynamics on coral reef fish assemblages. Key areas of interest include larval connectivity, reproductive biology of reef fish species, and the effectiveness of no-take marine reserves in promoting biodiversity and fishery recovery. His work spans both the Great Barrier Reef and the Philippines, emphasizing large-scale ecological patterns and conservation strategies. Russ has contributed to major initiatives like the Great Barrier Reef Marine Park rezoning and has collaborated with local communities in developing sustainable management practices. His research highlights the critical role of MPAs in mitigating coral degradation and enhancing ecosystem resilience against climate change and overfishing.
Thomas Gschwend, Ph.D. is a Professor for Quantitative Methods in the Social Sciences at the School of Social Sciences, University of Mannheim, and serves as a project director at the Mannheimer Zentrum für Europäische Sozialforschung (MZES). His academic position places him at the intersection of political science methodology and substantive political research, where he leads the Chair of Political Science, Quantitative Methods in the Social Sciences (QMSS). Professor Gschwend's research interests span electoral behavior, public opinion, comparative politics, political psychology, and constitutional politics. His methodological expertise includes ecological inference models, item-response theory, Bayesian statistics, and experimental design. He is particularly interested in how electoral institutions shape individual decision-making processes and their consequences for voters, party strategies, and election outcomes. His current work develops micro-theories of actor behavior under institutional constraints, with a focus on expectation formation in electoral contexts. Gschwend's recent publications reveal a strong emphasis on election forecasting (particularly through the Zweitstimme.org project), coalition politics, strategic voting, judicial politics, and innovative methodological approaches including AI applications in social science research. His work consistently bridges theoretical political science with advanced quantitative methods, demonstrating how methodological innovation can address substantive political questions. As an educator, Gschwend has taught numerous graduate and undergraduate courses at the University of Mannheim since 2001, including Multivariate Analysis, Research Design, Advanced Quantitative Methods, and Crafting Social Science Research. His teaching portfolio shows consistent commitment to methodological training across multiple program levels. Professor Gschwend leads a research team that includes postdocs, doctoral students, and research assistants working on projects related to electoral systems, coalition politics, judicial behavior, and political methodology. His research has been supported by major funding bodies including the Deutsche Forschungsgemeinschaft (DFG), Social Sciences and Humanities Research Council of Canada, National Science Foundation (NSF), and Fritz Thyssen Foundation. The Chair he leads is actively involved in multiple research projects including Zweitstimme.org (election forecasting), coalition politics before elections, measuring common policy spaces, and political attention in legislative reform. The team has produced numerous publications and received recognition including the Franz-Urban-Pappi-Prize and Lorenz-von-Stein Prize for student work supervised under Gschwend's direction.
Dr. Theo Niyonsenga is an Associate Professor of Biostatistics at the University of Canberra's Faculty of Health, affiliated with the Health Research Institute (HRI) and the Centre for Research and Action in Population Health (CeRAPH). He holds adjunct roles at the University of South Australia and has prior experience at institutions like Florida International University and the University of Saskatchewan. His expertise spans biostatistics, spatial epidemiology, and multivariate data analysis, with a focus on cardiovascular disease, HIV/AIDS, and substance abuse research. Education: PhD in Mathematical Statistics, University of Montreal (1991) MSc in Mathematical Statistics, University of Montreal (1985) BSc in Mathematics, Physics & Engineering, National University of Rwanda (1982) Research Interests: Dr. Niyonsenga specializes in statistical methodologies including spatial statistics, hierarchical models, and structural equation modeling. His work addresses cardiovascular risk factors, geographic disparities in healthcare access, HIV/AIDS survival disparities, and the intergenerational transmission of substance use. Recent projects include evaluating interventions for veterans' mental health and optimizing physical activity thresholds for heart disease prevention. Collaborations & Projects: Leading studies on knee osteoarthritis triage and cardiovascular rehabilitation strategies (2022–2025) Piloting clinical implementation strategies for cardiac rehabilitation (2025–2027) Assessing physical activity metrics in heart disease populations (2023) Labs/Teams: Active in multidisciplinary teams at the HRI, focusing on translational research linking statistical methods to public health outcomes. Collaborates globally on projects addressing mental health inequities and chronic disease management.
Ester D. Navarro, Ph.D., is an Assistant Professor of Psychology at St. John's College of Liberal Arts and Sciences, St. John's University. Her research focuses on social cognition, bilingualism, and stress physiology, with a particular interest in how language experience, multiculturalism, and cognitive abilities influence perspective-taking and theory of mind. She leads the Schema Lab, exploring individual differences in cognitive, emotional, and physiological responses to stress and uncertainty, especially in military personnel. Her work integrates methods from psychology, linguistics, and psychometrics, including item response theory and network science. Navarro’s research bridges theoretical and applied domains, addressing questions such as the impact of bilingualism on empathy and the efficacy of wearable technologies for predicting human performance. She has published widely on topics like acute stress induction, cognitive mediation in metalinguistic awareness, and the interplay between theory of mind and intelligence. Her lab’s findings have implications for education, clinical practice, and military resilience programs. Navarro’s academic profiles include a Google Scholar page (https://scholar.google.com/citations?user=bE7oQeEAAAAJ&hl=en) and a ResearchGate profile (https://www.researchgate.net/profile/Ester-Navarro). Her lab’s activities are detailed at https://schemalabsj.wixsite.com/schema-lab.
