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Johannes ResinView profile

Researcher

• Statistical Methodology
• Probabilistic Forecasting
• Forecast Evaluation
• +3 more

Dr. Johannes Resin is a postdoctoral researcher at the Faculty of Economics and Business, Goethe University Frankfurt, and a visiting scientist at the Computational Statistics group, Heidelberg Institute for Theoretical Studies. He specializes in statistical methodology for probabilistic forecasting and forecast evaluation. Institution: Goethe University Frankfurt Collaboration: Heidelberg Institute for Theoretical Studies Research Focus: Probabilistic forecasting, forecast evaluation, and statistical diagnostics His work emphasizes quantile evaluation , Wasserstein distance analysis , and regression diagnostics . Recent publications explore shift-dispersion decompositions, exact multinomial tests, and proper scoring rules for probabilistic predictions. Selected affiliations and contact details: Private Website GitHub | Google Scholar Email: johannes.resin@awi.uni-heidelberg.de | resin@econ.uni-frankfurt.de

Glen SattenView profile

Professor

• Genetic Epidemiology
• Microbiome Research
• Genomics
• +3 more

Dr. Glen Satten is a Professor in the Department of Biostatistics and Bioinformatics at Emory University, with secondary appointments in the Departments of Human Genetics and Biostatistics and Bioinformatics. He is affiliated with the Division of Research in the Department of Gynecology and Obstetrics within the Emory University School of Medicine. His work focuses on developing statistical methods for analyzing microbiome, genetic, and epidemiologic data. Education: Ph.D. in Biostatistics from Harvard University (1985), M.A. in Biostatistics from Harvard (1981), and B.A. from Oberlin College (1979). Research interests include Genetic Epidemiology, Microbiome Research, Genomics, and the development of novel statistical methodologies for high-dimensional biological data. His contributions span computational tools for microbiome analysis (e.g., LDM and MIDASim), bias correction in compositional data, and improving statistical efficiency in genetic association studies. He has also contributed to studies on maternal health outcomes and HIV in pregnancy. His recent publications highlight advancements in microbiome data analysis, including bias detection in mock communities and integrating multiple sequencing modalities. Collaborative work extends to public health surveillance and methodological improvements for case-control studies. No scientific awards are explicitly listed in the provided materials. His research has been applied to clinical settings, such as optimizing embryo transfer protocols using national surveillance data. He advises on statistical methodologies but no student names are provided in the text. Labs/Teams: While not explicitly named, his work suggests involvement in interdisciplinary teams focused on bioinformatics, genetic epidemiology, and clinical data analysis.

Lyndsey DarrowView profile

Adjunct Associate Professor

• Maternal and Child Health
• Air Pollution
• Biomarkers
• +5 more

Lyndsey Darrow is an Adjunct Associate Professor in the Department of Epidemiology and Gangarosa Department of Environmental Health within the School of Community Health Sciences at the University of Nevada, Reno. She specializes in environmental epidemiology, focusing on maternal and child health, air pollution, biomarkers, chemical exposure, climate change impacts, and reproductive health. Her work integrates interdisciplinary approaches to assess environmental contaminants' effects on respiratory diseases, pregnancy outcomes, and endocrine function in children. Dr. Darrow’s research includes studies on traffic pollutants during pregnancy and early childhood asthma incidence, antibiotic exposure in infancy and childhood allergies, and the effects of heatwaves on birth outcomes. She collaborates with the EPA-funded Clean Air Research Center at Emory and Georgia Tech, and leads projects supported by grants such as R03ES018963, R21ES019697, and R01AI122266. Her studies often involve large-scale datasets like the High-resolution Urban Meteorology for Impacts Dataset (HUMID) and retrospective cohort analyses from Kaiser Permanente Georgia. Key Research Themes: Environmental contaminants and pediatric respiratory health Climate change impacts on maternal and child outcomes Exposure modeling for air pollutants and heat Breastfeeding policy effects and childhood nutrition Antimicrobial resistance and early-life health Her grants and collaborations emphasize methodological rigor, including quantile regression for pollution-birth weight links, classification and regression trees for air pollution-asthma associations, and addressing gestational age uncertainties in exposure studies. She also evaluates state breastfeeding laws and their impact on exclusive breastfeeding practices among low-income populations.

Charles BosView profile

Associate Professor

• Time Series Modelling
• High-Frequency Data
• Stochastic Volatility
• +3 more

Charles Bos is an Associate Professor at the Department of Finance within the School of Business and Economics at Vrije Universiteit Amsterdam. He holds a PhD from Erasmus University Rotterdam (2001) with a thesis on Time Varying Parameter Models for Inflation and Exchange Rates. Previously, he served as a research officer at Nuffield College, Oxford University and received a VENI grant from NWO. His research focuses on time series modeling, high-frequency data, and stochastic volatility, with applications in finance and macroeconomics. Teaching includes courses on Econometrics for Quantitative Finance and Quantitative Financial Risk Management . He advises ETFbook (since 2020) and SquaredData GmbH (since 2023). His work contributes to UN SDGs related to sustainable economic development. Recent research highlights include analyzing market power in California’s water market and investigating non-standard errors in financial data. His 34+ publications span econometric methods, volatility modeling, and policy analysis. Awards include the VENI grant (NWO), recognizing exceptional research potential.

Long HoàngView profile

Research Fellow

• Industrial Organization
• Microeconomic Theory
• Applied Econometrics
• +5 more

Long Hoàng is a PhD candidate at Vrije Universiteit Amsterdam (VU, Netherlands) and a Research Fellow at Eastern International University (EIU, Vietnam), pursuing a joint research qualification at Tinbergen Institute. He graduated as a Valedictorian from Vietnam National University and previously worked at Axis Research JSC (Sovico Group, Vietnam). Research Interests : Industrial Organization, Microeconomic Theory, Applied Econometrics, Development Economics, with a focus on two-sided markets, innovation, urbanization, and urban sprawl. Teaching : Macroeconomics I (VU), Quantitative Research Method I (EIU), and Microeconomics I (Vietnam National University). Peer Review : Referee for the Journal of Economic Development and the International Journal of Housing Markets and Analysis. SDG Contributions : Research aligns with UN Sustainable Development Goals related to sustainable cities (SDG 11), reduced inequalities (SDG 10), and economic growth (SDG 8).

Julia SchaumburgView profile

Professor

• Time Series Econometrics
• Financial Econometrics
• Machine Learning Applications
• +4 more

Prof. Julia Schaumburg is a Full Professor of Econometric Methods and Applications at Vrije Universiteit Amsterdam's School of Business and Economics, serving as Head of the Department of Econometrics and Data Science. She holds concurrent roles as a research professor at Halle Institute for Economic Research and a research fellow at Tinbergen Institute. Her research focuses on time series econometrics, financial stability, systemic risk, and climate economics, integrating machine learning and panel data methods. She has secured significant grants including NWO-Veni (2015), NWO-Vidi (2020), and the ECB Lamfalussy Fellowship (2018). Educated at Humboldt-Universität zu Berlin (PhD 2013), she teaches advanced econometric courses and supervises PhD students. Her work addresses macro-financial interdependencies, climate policy implications, and financial system resilience. She contributes to peer-reviewed journals like Journal of Econometrics and serves on editorial boards including International Journal of Forecasting and Oxford Open Economics . Recent publications explore dynamic factor models, clustering algorithms for financial data, and policy responses to climate change. Award-winning scholar with the Engle Prize (2019), she advocates for ethical academic-industry collaborations and participates in public debates about fossil fuel ties in academia. Her work aligns with UN Sustainable Development Goals on climate action and sustainable finance.

• Behavioral Economics
• Experimental Economics
• Applied Microeconometrics
• +5 more

Prof. Dr. Thomas Dohmen is a Professor of Applied Microeconomics at the Department of Economics, Universität Bonn, and holds a professorship in Education and the Labour Market at Maastricht University. He also serves as a Research Professor at DIW Berlin and a Research Fellow at IZA. His research focuses on behavioral and experimental economics, applied microeconometrics, and the formation of cognitive/non-cognitive skills. Key affiliations include the Institute for Applied Microeconomics (IAME) and the Research Centre for Education and the Labour Market (ROA). Education: M.A. and Ph.D. from Maastricht University, MSc from the University of Warwick. His work spans topics like risk preferences, labor market dynamics, and organizational economics, with publications in top journals such as the American Economic Review and Science. Research highlights include studies on testosterone's impact on competitiveness, the role of patience in economic development, and cross-country analyses of non-cognitive skill formation. Awards include the Leading House Best Paper Award and the 2013 SOEP Prize. He advises on policy and serves on scientific boards, including the Human Capital and Economic Opportunity Global Working Group (HCEO). His work bridges microeconomic theory with empirical applications, emphasizing experimental and survey-based methodologies.

• Energy Economics
• International Trade
• Applied Microeconomics
• +3 more

Mita Bhattacharya is a Senior Lecturer in the Department of Economics at Monash University, affiliated with Impact Labs. She specializes in Energy Economics, International Trade, and Applied Microeconomics, with a focus on sustainability and policy-relevant research. Prior to Monash, she taught at the University of Tasmania and the University of Newcastle. Her work contributes to UN Sustainable Development Goals, particularly addressing climate change and sustainable economic practices. Research Interests: Her key areas include energy productivity, renewable energy deployment, trade dynamics, and the environmental impacts of economic policies. Recent projects include analyzing renewable energy clusters and the role of institutions in sustainable development. She has collaborated with institutions like the OECD and engineering faculties to bridge interdisciplinary gaps. Publications & Awards: Dr. Bhattacharya has over 85 peer-reviewed publications in top journals like Energy Economics and Applied Energy. Notable work includes studies on CO2 emissions, renewable energy policies, and trade liberalization effects. She received the Applied Energy Awards (2019) for her influential research on renewable energy sources. Grants & Collaborations: She has secured grants from the Australian Research Council, Tyre Australia, and Taiwan’s National Science Council. Current projects include the Decision Engine for Net Zero initiatives (2022) and analyzing technical change in Australian manufacturing. Her work often emphasizes policy recommendations for both developed and developing nations. Teaching & Leadership: She serves as Course Director for the Monash Masters in Economics program, demonstrating her commitment to academic leadership and education.

Jessica LeungView profile

Lecturer

• Business Analytics
• Operations Research
• Optimization
• +3 more

Jessica Leung is a Lecturer in the Department of Econometrics & Business Statistics at Monash University. She holds a PhD in Business Analytics from the University of Sydney (2021) and has additional qualifications including a Senior Fellowship from Advance HE and a Certified Analytics Professional - Expert (CAP-X) certification from INFORMS. Her research focuses on Business Analytics, Operations Research, Optimization, Machine Learning, and Data Visualization. Leung has held leadership roles such as Lead Editor of ORMS Tomorrow (INFORMS, 2020–2022) and serves on editorial and peer review boards for journals like International Review of Financial Analysis and OMEGA International Journal of Management Science . She received the Dean's Award for Teaching Excellence - Early Career in 2024. Her scholarly work spans financial risk modeling, optimization algorithms, and visualization practices in operations research. Recent contributions include studies on tail risk forecasting, machine learning applications in finance, and algorithm performance evaluation. Key collaborations include projects with researchers from institutions globally, emphasizing interdisciplinary approaches to analytics and decision-making challenges.

Julia BrettschneiderView profile

Professor

• Statistics
• Biostatistics
• Data Science
• +5 more

Julia Brettschneider is a Reader in Statistics at the University of Warwick, where she previously held roles as Associate Professor (2010-2021) and Assistant Professor (2007-2010). She serves as Deputy Director of the Centre for Applications of Mathematical & Computing Sciences (CAMaCS) and leads the Health & Technology team under the Health Spotlight interdisciplinary initiative. Her academic career includes a postdoctoral fellowship in computational biology at TU Eindhoven, and visiting positions at UC Berkeley and Queen’s University, Canada. Her research focuses on interdisciplinary applications of statistics, including spatial statistics, high-dimensional data quality, and decision algorithms, with collaborations in genomics, neuroscience, and public health. She leads the WIHEA-funded WUST project, developing tools to enhance student academic success and skill development. She has supervised 8 completed PhD students and currently co-supervises 4, with extensive experience in Master’s and undergraduate supervision. Brettschneider holds a PhD in Mathematics from Humboldt University Berlin and Master’s degrees in Mathematics (University of Bonn) and minor qualifications in Computer Science and Psychology. Her awards include Senior Fellow of the Higher Education Academy and Fellow of the Warwick International Higher Education Academy. Her work bridges theoretical statistics with practical applications, emphasizing real-world impact in healthcare, infrastructure, and education.

Songthip OunpraseuthView profile

Professor

• Computational Statistics
• Prediction Error Estimation
• Dimension Reduction & Classification
• +1 more

Songthip Ounpraseuth is a Professor in the Department of Biostatistics at the Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences (UAMS). Their research focuses on computational statistics, prediction error estimation, dimension reduction, and selection bias. Dr. Ounpraseuth is a member of the Graduate Faculty and affiliated with professional societies including the American Statistical Association and Society for Clinical Trials. Education: Ph.D. (Statistics), Baylor University, 2006 MS (Statistics), University of Arkansas, 2000 BS (Mathematics), University of Arkansas, 1998 Research Interests Dr. Ounpraseuth’s work emphasizes statistical methodologies applied to clinical and healthcare challenges. Current projects include evaluating the ANGELS High Risk Pregnancy Program and identifying quality indicators for substance use disorders. Their computational approaches address complex data structures in healthcare outcomes and clinical trials. Recent publications highlight telemedicine applications in chronic kidney disease, prenatal ultrasound accuracy, and heart failure patient outcomes. These studies reflect a focus on bridging statistical innovation with practical clinical solutions. Notable collaborations include leadership on NIH-funded projects and contributions to interdisciplinary teams in maternal-fetal medicine and cardiology. Dr. Ounpraseuth teaches advanced biostatistics courses, including BIOS 5223/6223 Biostatistics III and BIOS 5233 Statistical Methods for Clinical Trials.

• Econometric Theory
• Quantile Methods
• Causal Inference
• +2 more

Kaspar Wuthrich is an Associate Professor of Economics at the University of Michigan specializing in econometric theory. His research develops novel methods for quantile and IV estimation, causal inference with panel data, and machine learning applications in econometrics. Research areas include: Quantile regression methods Instrumental variable techniques Causal panel data analysis Machine learning for prediction and inference Detection of publication bias Wuthrich examines methodological challenges in econometric identification and develops approaches to address issues such as p-hacking and publication bias in empirical economics research.

• Finance
• Corporate Finance
• Financial Literacy
• +5 more

Professor Panayiotis Andreou is a Professor in the Business School at Durham University, specializing in Finance. He holds a PhD from the University of Cyprus and previously served as a Research Associate there. His teaching focuses on Derivatives Markets for postgraduate programs and Financial Theory and Corporate Policy at the undergraduate level. His research interests span corporate finance, financial literacy, stock price crashes, and the application of artificial neural networks in financial modeling. He has published extensively in journals like the Journal of Corporate Finance , Cambridge Journal of Economics , and British Journal of Management , with a focus on topics such as market orientation, environmental proactivity, and institutional ownership dynamics. Key themes in his work include analyzing how corporate strategies and governance structures influence financial resilience during crises, evaluating the impact of managerial behavior on investment decisions, and exploring the role of financial literacy in consumer banking behavior. His recent studies also investigate the relationship between technological rhetoric and institutional ownership, as well as the resilience of firms during adversity periods. No scientific awards are explicitly listed in the provided text. His advising includes supervision of PhD student Tao Chen. His work bridges theoretical finance with practical applications, leveraging textual analysis and quantitative methods to study corporate behavior and market dynamics. Professor Andreou’s research often intersects with broader economic challenges, such as environmental policy compliance (e.g., EU Emissions Trading System) and the implications of pandemic-driven financial behaviors. He contributes to understanding how firms adapt to regulatory shifts and economic disruptions through strategic and financial mechanisms.

Devin CaugheyView profile

Professor

• American Political Development
• Political Representation
• Electoral Politics
• +3 more

Devin Caughey is the Class of 1949 Professor of Political Science and Director of Graduate Studies at MIT, with affiliations in the Institute for Data, Systems, and Society. His research examines American political development, representation, electoral systems, and advanced methodological approaches to political analysis. Professor Caughey holds a Ph.D. from UC Berkeley (2012) and has received numerous awards including the Virginia Gray Book Award and Leon Epstein Outstanding Book Award. His work spans historical analyses of political realignments, dynamic modeling of public opinion, and methodological innovations in causal inference and survey analysis. His empirical research focuses on the relationship between public opinion and policy outcomes across U.S. states, employing advanced statistical techniques to analyze long-term political trends. Recent work includes studies on multidimensional scaling of survey data, randomization inference methods, and causal inference in historical contexts.

Jery Russell StedingerView profile

Professor

• Hydrology
• Water Resources Engineering
• Risk Analysis
• +6 more

Jery Russell Stedinger is a Professor in the Department of Civil and Environmental Engineering at Cornell University, affiliated with the College of Engineering. He holds a B.A. in Applied Mathematics from the University of California, Berkeley (1972), and a Ph.D. in Environmental Systems Engineering from Harvard University (1977). His career includes sabbaticals at institutions such as the U.S. Geological Survey, U.S. Army Corps of Engineers, and Technische Universität Wien. Stedinger’s research focuses on statistical methodologies in hydrology, flood frequency analysis, and water resource systems optimization. He has pioneered approaches to incorporate historical and paleoflood data into risk assessments and has contributed to national guidelines like Bulletin 17C for flood flow frequency. His work extends to geothermal resource assessment and energy systems engineering. Awarded the ASCE Ven Te Chow Award (2014) and elected to the National Academy of Engineering (2014), Stedinger has authored over 140 professional papers and textbooks, including co-authorship of the foundational Water Resource Systems Planning and Analysis . He teaches courses on uncertainty analysis (CEE 3040) and risk management (CEE 5970), mentoring students in systems engineering and environmental management. Stedinger serves on committees for dam safety, flood risk, and water resources policy, advising federal agencies like the USACE and Bureau of Reclamation. His contributions bridge academic research, policy, and practical applications in hydrology and sustainable engineering.