Joakim Westerlund is a Professor in the Department of Economics at Lund University's School of Economics and Management. With over 134 research outputs and 33 academic activities, his work focuses on econometrics, particularly panel data analysis, structural breaks, and estimation theory. He has contributed to the development of econometric methods for the New Keynesian Phillips Curve and common correlated effects models. Active Wallenberg Academy Fellowship (2019-2028) Supervised 11 doctoral theses and bachelor/master projects Peer-review panel member and journal editor His research aligns with UN Sustainable Development Goals in Economics and Econometrics, with significant contributions to panel unit root testing, interactive effects models, and Stata-based econometric methods. Westerlund received the prestigious Journal of Applied Econometrics Distinguished Author award in 2018. Current PhD supervisees include Christina Maschmann (2023-2028), Tilman Bretschneider (2023-2028), Pelle Almgren (2022-2027), and Shayan Meskinimood (2021-2026).
Steven Berry is the David Swensen Professor of Economics at Yale University and the inaugural Faculty Director of the Tobin Center for Economic Policy at Yale. He specializes in empirical analysis of markets in equilibrium, with a focus on industrial organization, product differentiation, and dynamic market structures. He holds a PhD from the University of Wisconsin-Madison (1989) and a BA from Northwestern University (1980). His research explores competition policy, environmental economics, international trade, and labor market power. He has served as Economics Department Chair at Yale and Director of the Division of Social Sciences. Berry is a Research Associate at the National Bureau of Economic Research (NBER) and has advised governments on antitrust, environmental, and trade policies. He is an elected Fellow of the Econometric Society and a member of the American Academy of Arts and Sciences, having won the Frisch Medal in 2017. His work integrates micro and macro data to analyze markets like automobiles, airlines, and media, emphasizing structural econometric methods. Key Research Themes: Empirical industrial organization, demand estimation, dynamic policy analysis. Awards: Frisch Medal, Distinguished Fellow of the Industrial Organization Society. Consulting: Government and private-sector antitrust policy, environmental regulation.
Martin Huber is Professor of Applied Econometrics and Policy Evaluation at the University of Fribourg, Switzerland, within the Faculty of Management, Economics and Social Sciences, Department of Economics. He leads the Chair of Applied Econometrics and maintains an active research profile with numerous publications in top economics and statistics journals. His work bridges theoretical econometrics with practical policy applications across multiple domains including labor, health, and education economics. Professor Huber earned his Ph.D. in Economics and Finance in 2010 and served as Assistant Professor at the University of St. Gallen until 2014. He has conducted research stays at Harvard University (2011/2012) and the University of Sydney (2014 and 2019), establishing an international research network. His academic affiliations include the Committee for Econometrics of the Verein für Socialpolitik, Global Labor Organization, Soda Labs (Monash Business School), and Centre for European Economic Research (ZEW) Mannheim. Huber's research focuses on data-based causal analysis , machine learning applications in economics , and policy evaluation methods . He specializes in developing and applying statistical and econometric methods for measuring causal effects, with particular emphasis on semi- and nonparametric microeconometrics. His work spans labor economics (gender occupational segregation, maternal labor supply), health economics, education policy, and competition policy (bid-rigging cartels detection). His recent publications (2023-2025) demonstrate a clear trajectory toward integrating machine learning techniques with traditional econometric methods for causal inference. This includes developing frameworks for causal discovery, improving difference-in-differences methods with machine learning, and creating novel approaches for detecting collusion in markets. His 2023 book "Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R" (MIT Press) has become a key reference in the field. As an active researcher, Professor Huber directs several research projects including experimental evaluations of gender occupational segregation in the Swiss apprenticeship market. His work combines theoretical rigor with practical policy relevance, often employing experimental and quasi-experimental methods to address questions of causal mechanisms in social and economic phenomena. Through his Chair of Applied Econometrics, Huber supervises Ph.D. students and maintains an active research group focused on advancing causal inference methodologies. His work has significant implications for evidence-based policymaking across multiple sectors, particularly in evaluating the effectiveness of social programs and economic policies.
Pengfei Wang is an Assistant Professor in the Department of Civil & Environmental Engineering at Old Dominion University (ODU). He holds a Ph.D. in Geotechnical Engineering and an M.S. in Statistics from UCLA, alongside a B.S. in Transportation Engineering from Tongji University. Prior to ODU, he conducted postdoctoral research at UCLA. His expertise focuses on Geotechnical Engineering , Engineering Seismology , and Applied Statistics , with emphasis on regional geo-hazard modeling, multi-hazards risk assessment, and statistical learning applications. Key research interests include seismic site response analysis, liquefaction susceptibility, and probabilistic risk frameworks for infrastructure resilience. Dr. Wang’s work integrates geospatial analysis and statistical methodologies to address challenges in earthquake engineering. He has developed frameworks for regional landslide and liquefaction risk assessments, particularly in vulnerable regions like California’s Sacramento-San Joaquin Delta. His contributions include advancing HVSR (Horizontal-to-Vertical Spectral Ratio) methodologies and ergodic site response modeling. He maintains active collaborations with institutions globally and contributes to open-source databases for seismic data, promoting transparency and reproducibility in geotechnical research. His educational background in transportation engineering enriches interdisciplinary approaches to civil infrastructure resilience.
Daniel Wilhelm is a Professor of Statistics and Econometrics at LMU Munich, with a courtesy appointment in the Department of Economics. His research focuses on econometric theory, nonparametric methods, measurement error modeling, and statistical inference. He leads the Statistics and Econometrics Group at LMU and holds affiliations with the Centre for Microdata Methods and Practice (CeMMAP), Institute for Fiscal Studies (IFS), and the Centre for Research and Analysis of Migration (CReAM). Wilhelm’s work includes groundbreaking contributions to NPIV estimation, robust statistical testing, and the development of R and Stata packages for rank inference and econometric analysis. His recent publications address topics like rank-based inference, measurement error detection, and high-dimensional independence testing. He organizes academic events such as the Munich Econometrics Seminar and the LMU-Todai Econometrics Workshop. His research emphasizes methodological rigor and practical applications, with a focus on improving statistical techniques for social science and policy analysis.
Dr. Gaël Kermarrec is a researcher at the Boundary Layer Meteorology Group , part of the Institute of Meteorology and Climatology within the Faculty of Mathematics and Physics at Leibniz University Hannover . His work focuses on atmospheric turbulence, GNSS applications, and remote sensing for environmental monitoring. Boundary layer meteorology Turbulence theory GNSS signal processing Terrestrial laser scanning Climate change impacts Geodetic time series analysis His research integrates advanced mathematical models like LR B-splines and Matérn covariance with large eddy simulations to study: Atmospheric turbulence effects on optical/GNSS signals Hydrospheric mass loading Deformation analysis of terrain/port infrastructure Climatic sea-level changes Machine learning for remote sensing The 15 most recent articles (2025-2023) demonstrate his focus on: GNSS-based turbulence detection AI-enhanced climate mapping Advanced surface approximation techniques Multi-sensor data fusion Stochastic modeling of geodetic observations Environmental impacts on optical measurements He has developed tools like the Klimascanner QGIS plugin for urban climate resilience and contributes to: Understanding atmospheric scale lengths Improving TLS/GNSS deformation monitoring Analyzing hydrospheric changes Wavefront modeling Ionospheric corrections
Miguel R. Rueda is an Associate Professor in the Department of Political Science at Emory University, specializing in electoral manipulation, civil conflict, money in politics, and political methodology. He holds a PhD from the University of Rochester (2014), an M.Sc. in Economics, and a B.Sc. in Economics and Mathematics from La Universidad de los Andes. Before Emory, he was a visiting scholar at Princeton University's Center for the Study of Democratic Politics (2013–2014). In Fall 2024, he will serve as a Visiting Associate Professor at Vanderbilt University. His research has been published in top journals like the American Political Science Review , American Journal of Political Science , and Journal of Conflict Resolution . Key themes include electoral fraud mechanisms, civil war dynamics, and the intersection of political methodology with empirical policy analysis. Rueda's work spans theoretical models of strategic behavior (e.g., foreign aid allocation, partisan poll-watching) and applied analyses of electoral systems, conflict outcomes, and governance challenges. His methodological contributions address econometric issues like post-instrument bias and omitted variable effects. Contact: miguel.rueda@emory.edu , 315 Tarbutton Hall, Emory University, Atlanta, GA 30322.
Luke Miratrix serves as Assistant Professor at Harvard Graduate School of Education and affiliate faculty in Harvard Department of Statistics. His methodological expertise centers on causal inference applications in educational research, particularly treatment effect heterogeneity and cluster-randomized trial evaluation. His academic background includes a Doctorate in Statistics from University of California, Berkeley (2012), Master of Science in Computer Science from M.I.T., Bachelor of Science in Computer Science from California Institute of Technology, and Bachelor of Arts in Mathematics from Reed College. Prior to academia, he spent seven years as a high school teacher and tutor. Miratrix's research prioritizes minimal-assumption statistical approaches to validate data-driven arguments. Key interests include developing methods for characterizing variation in treatment impacts, analyzing post-treatment subgroups, and applying high-dimensional techniques to text summarization in legal, journalistic, and educational contexts. His work consistently bridges theoretical statistics with practical implementation challenges in real-world settings. Analysis of his recent publications (2023-2025) reveals three dominant trends: advancement of matching methodologies (e.g., synthetic controls, caliper matching), refinement of heterogeneous treatment effect estimation across multisite trials, and integration of machine learning with human coding for efficient text-based inference in educational assessments. These efforts demonstrate increasing focus on scalable, accessible tools for applied researchers. He contributes to methodological infrastructure through the CARES Lab and software packages like 'matchMulti' and 'textreg', providing practical implementation guides for complex statistical techniques. His work emphasizes translating advanced causal inference methods into usable frameworks for education researchers and policymakers.
Wayne Springer is a Professor in the Department of Physics & Astronomy at the University of Utah, with a career spanning over 25 years. He has been actively involved in experimental particle astrophysics, ultra-high-energy cosmic ray (UHECR) physics, and gamma-ray astronomy. Ph.D. in Physics from University of Maryland (1991) B.S. in Physics from University of Maryland (1985) Postdoctoral training at University of Maryland and University of Alberta His research focuses on particle astrophysics, cosmic ray detection, and gamma-ray astronomy. He has made significant contributions to the development of the HiRes and Telescope Array cosmic ray observatories, as well as the HAWC and SWGO gamma-ray observatories. His recent work includes deployment of the Trinity neutrino detector prototype and serving as SWGO project manager for Chile site infrastructure. Article trends show strong emphasis on TeV gamma-ray observations (HAWC, SWGO), cosmic ray diffusion mechanisms, dark matter searches, and high-energy astrophysical source characterization (pulsars, microquasars, supernova remnants). He has secured multiple NSF grants for particle astrophysics research and leads detector working groups in international collaborations. Professor Springer actively participates in astronomy outreach, co-developing observatories and implementing computational physics teaching tools with Gradescope auto-graders for enhanced pedagogy. His work bridges experimental high-energy physics, detector development, and multiwavelength astrophysical studies.
Dr. Michael Stevens is a Senior Lecturer at University of New South Wales (UNSW) Canberra , where he focuses on advanced manufacturing and biomedical device control systems . His work bridges digital manufacturing for SMEs with smart artificial heart technologies , emphasizing industry collaboration and translational research. Specializes in physiological control systems for rotary blood pumps Develops unobtrusive fall detection systems for dementia patients Leads international projects on total artificial heart development Education : B.Eng (Medical - First Class Honours), Queensland University of Technology (2010) PhD in Physiological Control for Biventricular Assist Devices, University of Queensland (2014) Research Trends show consistent focus on: Machine learning for biomedical diagnostics (2018–2025) mmWave radar and thermal sensors in patient monitoring (2021–2024) Computational fluid dynamics in artificial heart modeling (2016–2024) Physiological control algorithms for rotary blood pumps (2011–2025) Scientific Awards : UNSW Scientia Education Award (2021) for contextual teaching Heart Foundation Runner-up for "Smart Artificial Hearts" pitch (2021) ARC PGC Supervisor Award (2017) for mentoring Grants & Supervision : Holds over $6 million in competitive funding including MRFF and ARC grants. Currently supervises 4 PhD students while maintaining industry partnerships with VitalCare and BiVACOR. Labs & Facilities : Works across UNSW Engineering labs and Graduate School of Biomedical Engineering platforms, including mock circulation loops and high-performance computing clusters for CFD simulations.
Nicolás de Roux is an Associate Professor in the Department of Economics at Universidad de los Andes in Bogotá, Colombia. He holds a PhD in Economics from Columbia University and an MA from Universidad de los Andes. His academic work focuses on applied microeconomics with a specialization in development economics, particularly examining firm behavior, financial inclusion, agricultural economics, and labor markets in developing countries. He actively contributes to the academic community as a co-organizer of the biweekly Virtual Development Seminar (VDEV/CEPR/BREAD). De Roux's research interests center on understanding economic development challenges in the Global South, with particular emphasis on how weather shocks affect agricultural productivity, labor market power in developing economies, and the impacts of trade disruptions on firm performance. His interdisciplinary approach combines rigorous econometric methods with theoretical insights to address pressing development issues. His work demonstrates how microeconomic analysis can inform policy decisions that improve economic outcomes for vulnerable populations in developing countries. His publications span multiple high-impact journals including the Journal of Labor Economics, The Review of Financial Studies, and Journal of Public Economics. De Roux's research demonstrates consistent focus on labor markets, agricultural productivity, and financial inclusion, with recent work examining how weather risk affects small farms, the extent of employer power in developing country labor markets, and how firms adapt to trade partner collapse. His research methodology typically combines detailed administrative data with innovative econometric approaches to establish causal relationships. Featured Economist Interview, International Economic Association (IEA), July 2025 De Roux has mentored numerous students through his teaching at Universidad de los Andes, where he teaches courses including Introduction to Mediation, Econometrics 1, and Haciendo Economía 1. His research collaborations extend across multiple institutions, working with economists from around the world including Giacomo De Giorgi, Garance Genicot, Gianmarco León-Ciliotta, and Eric Verhoogen. His work on academic tracking revealed the counterintuitive finding that students perform better when they're at the top of a lower-ability group rather than at the bottom of a higher-ability group, challenging conventional wisdom about educational tracking. His research team includes collaborators working on projects examining internet access and banking competition in rural credit markets, as well as quality upgrading in the Colombian coffee sector. De Roux maintains an active presence in policy discussions through media outlets like VoxDev, World Bank blogs, and Colombian publications including Foco Económico and La República.
Guanghao Qi is an Assistant Professor in the Department of Biostatistics at the University of Washington. His research focuses on developing statistical and machine learning methods for multi-omics approaches in genetic studies, particularly integrating single-cell RNA-seq, GWAS, and functional genomic data. Key areas include single-cell eQTL analysis, Mendelian randomization, and multi-trait genetic association analyses. Education: PhD in Biostatistics from Johns Hopkins Bloomberg School of Public Health (2020), BS in Mathematics from Fudan University (2015). Research interests emphasize high-dimensional data analysis, allele-specific expression in single cells, and causal inference using genetic variants. Notable achievements include a 2025 NIH K01 award for developing methods to integrate single-cell eQTL and GWAS data, and the development of the TWiST method for single-cell transcriptome-wide association studies. Recent work highlights advancements in computational tools like SURGE for context-specific genetic regulation analysis, and evaluations of Mendelian randomization methods in studies of type 2 diabetes and cardiovascular disease. His work often bridges computational biology and statistical theory to address challenges in interpreting large-scale genomic datasets. Awards: NIH K01 Award (2025) Key Contributions: TWiST method (2025), SURGE framework (2024), HIPO power optimization (2018) Labs/Teams: Active collaborations in genomic epidemiology and statistical genetics, with a focus on single-cell multi-omics integration and causal inference methodologies.
Alain PIROTTE is a Professor of Economic Sciences at University Paris-Panthéon-Assas, affiliated with the Center for Research in Economics and Law (CRED). His research and teaching focus on econometrics, particularly panel data and spatial econometrics, with applications in labor, transportation, and urban economics. His research interests include: Panel data econometrics and forecasting Spatial econometrics and spatial dependence modeling Transportation and urban economics Labor market dynamics Environmental and agricultural econometrics The recent articles highlight a strong focus on spatial panel data models, prediction techniques, and applications to real-world economic issues such as housing prices, traffic demand, and urban sprawl. His work frequently employs advanced econometric methods, including hierarchical Bayesian models and instrumental variable approaches, often in collaboration with leading scholars like B.H. Baltagi. He has held significant academic responsibilities, including: Head of Master 1 in Managerial and Industrial Economics Member of the Scientific Council at Panthéon-Assas University Member of Doctoral Schools at both Panthéon-Assas and University of Paris-Est Member of AERES expert evaluation committee He is actively involved in research leadership and academic governance, contributing to the development of econometric theory and its application across economic domains.
Ruli Xiao serves as Associate Professor and Director of Graduate Studies in the Department of Economics at Indiana University Bloomington's College of Arts and Sciences. Her academic office is located in Wylie Hall (Room 349), with contact details including email rulixiao@iu.edu and phone (812) 855-3213. Her academic credentials include: B.S. in Statistics from Tongji University M.A. in Economics from Shanghai University of Finance and Economics Ph.D. in Economics from Johns Hopkins University (2014) Dr. Xiao's research program emphasizes Empirical Industrial Organization and Micro-econometrics , specializing in methodological solutions for complex economic modeling scenarios. Her work develops identification frameworks for finite action games where multiple equilibria coexist with unobserved market heterogeneity, advancing estimation techniques for real-world industrial applications. Her publication profile demonstrates consistent focus on econometric theory development, particularly in dynamic modeling with unobservables as evidenced by her 2017 Journal of Econometric Methods paper. Current research trajectories indicate continued innovation in nonparametric methods for structural industrial organization models. As Director of Graduate Studies, Dr. Xiao oversees all graduate programs including M.A., M.S., and Ph.D. tracks, guiding curriculum development and student progression through rigorous economics training.
Prosper Dovonon serves as a Full Professor in the Department of Economics at Concordia University in Montreal, Canada, where he holds a prestigious Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets. He previously held positions as Associate Professor (2015-2023) and Assistant Professor (2010-2015) at the same institution. Additionally, he maintains an adjunct professorship at the University of Adelaide's School of Economics since 2021 and previously served as a Visiting Professor at HEC Montreal's Department of Finance (2017-2018). His educational background includes a PhD in Economics from Universite de Montreal (2007), an MSc in Statistics and Economics from ENSEA, Abidjan, Cote d'Ivoire (2000), and an MSc in Mathematics from Universite Nationale du Benin, Abomey-Calavi, Benin (1996). Dovonon's research focuses on advanced econometric methodologies, particularly in time series analysis and financial econometrics. His work addresses complex identification issues, develops robust estimation techniques, and creates innovative testing procedures for economic models. He specializes in moment condition models, GMM estimation, volatility modeling, and handling identification failures in econometric frameworks. His publication record shows a consistent focus on theoretical econometrics with practical applications in finance. Recent work emphasizes mixed identification strength scenarios, instrument exogeneity testing, and specification testing under challenging identification conditions. His research demonstrates increasing sophistication in handling complex econometric problems with real-world financial data applications. His notable recognition includes the Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets, highlighting his significant contributions to the field. Dovonon has supervised numerous graduate students and collaborated extensively with leading econometricians worldwide. His research has been supported by institutional funding through his Research Chair position, enabling significant contributions to econometric theory and methodology. He maintains active research collaborations across international institutions and continues to push the boundaries of econometric theory with applications to financial markets and economic modeling.
