Robert Piche is a Professor at the Computing Sciences Mathematics Research Centre, specializing in advanced signal processing, positioning systems, and sensor fusion. He holds a Doctor of Science (Technology) and Master of Science from the University of Waterloo, Canada (1986 and 1982, respectively). His research focuses on Kalman filters, Global Positioning Systems (GPS), particle filters, and indoor positioning technologies. He has contributed extensively to fields like satellite orbit prediction, non-line-of-sight (NLoS) positioning, and machine learning applications in biomechanics and robotics. Dr. Piche has authored over 230 publications and received recognition through an invitation/ranking in a 2014 competition. He actively participates in academic activities, including conference presentations and peer-review roles. His work bridges theoretical advancements and practical applications, with contributions to autonomous systems, sensor data analysis, and wearable technology. Collaborations span international institutions, reflecting his global impact in engineering and computer science disciplines.
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.
Min Seong Kim is an Associate Professor in the Department of Economics at the University of Connecticut, affiliated with the College of Liberal Arts and Sciences. His research focuses on econometrics, particularly panel data analysis, bootstrap methods, and cross-sectional dependence. He earned his Ph.D. in Economics from UC San Diego in 2011. His contact information includes email: min_seong.kim@uconn.edu , and office location Oak Hall 330. Education: Ph.D., Economics, UC San Diego, 2011 Research Interests: Econometric theory and applications Bootstrap methods and robust inference Panel data models with cross-sectional dependence Time series analysis and spatial econometrics Publications highlight his contributions to econometric methodology, including robust inference techniques for panel data models, bootstrap methods, and policy analysis. Recent work addresses cross-sectional dependence in large panel models and diffusion index forecasts. No scientific awards are explicitly listed. Advising and grant details are not provided in the text. His research is supported through standard academic channels, and he maintains a professional website at http://minseongkim.weebly.com .
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.
Mark J. Roberts is a Professor of Economics at Pennsylvania State University and a Research Associate at the National Bureau of Economic Research. His work focuses on productivity analysis, industrial organization, and international trade, particularly examining how export market exposure influences firm R&D investment and structural modeling of firm dynamics in oligopolistic markets. Key affiliations: Penn State University, NBER, Penn State Census Bureau Research Data Center His research investigates the causal relationships between exporting activities and R&D investment, showing that exporters experience higher returns on innovation and productivity growth. He has developed structural models to quantify how trade barriers like tariffs reduce long-term R&D payoff for firms in high-tech German manufacturing sectors. Recent methodological contributions include counterfactual simulations using dynamic panel data to analyze institutional determinants of R&D investment in emerging economies. The work highlights corruption, regulatory quality, and political stability as critical factors affecting innovation incentives.
Associate Professor Seojeong Lee is a faculty member at the University of New South Wales (UNSW) Business School, School of Economics, specializing in advanced econometric theory. She joined UNSW in 2012 after completing her PhD at the University of Wisconsin-Madison and has established herself as a leading researcher in robust inference methods under complex data conditions. Her educational background includes: Ph.D. in Economics, University of Wisconsin-Madison (2008-2012) M.A. in Economics, Seoul National University (2006-2008) B.A. in Economics and Political Science (dual major), Seoul National University, summa cum laude (2000-2006, with military service 2002-2004) Professor Lee's research centers on developing theoretically rigorous methods for econometric inference, with primary focus on generalized method of moments (GMM), instrumental variables (IV), and two-stage least squares (2SLS) under model misspecification. Her work addresses critical challenges including invalid/many/weak instruments, heterogeneous treatment effects, and clustered sampling, contributing foundational advances to statistical inference in economics. Analysis of her recent publications reveals a strong trajectory in refining methods for many-instrument settings and misspecified models, with increasing emphasis on computational implementations (e.g., Stata packages) and applications to causal inference. Her work bridges theoretical econometrics with practical policy-relevant analysis. Her scientific achievements include: Australian Research Council DECRA Fellowship (2017-2019) UNSW Dean's Research Fellowship (2020-2022) Zellner Thesis Award Honorable Mention from American Statistical Association (2014) Multiple competitive UNSW research awards Professor Lee actively supervises PhD candidates Wei Tian and Fangzhou Yu, and has secured over AUD 700,000 in research funding including ARC Discovery Projects. She teaches undergraduate and postgraduate econometrics courses, integrating her research into pedagogy. Her ongoing work continues to push boundaries in robust econometric methodology for modern data challenges.
José António Ferreira Machado is a Full Professor at the Nova School of Business and Economics, Universidade Nova de Lisboa. He currently serves as Vice-Rector of the university and previously held director roles at the Nova School of Business and Economics (2005-2015) and Angola Business School (2010-2015). His academic career includes consultancy at the Bank of Portugal (1992-2015) and teaching Econometrics, Statistics, and Macroeconomics. Research Interests: Machado's work focuses on Econometrics, Quantile Regression, Wage Distributions, Firm Size Analysis, and Macroeconomic Modeling. His most cited paper (2005) introduced counterfactual decomposition methods for wage distribution analysis. Recent publications examine quantile regression extensions, trade margins, and moment-based statistical inference. His research spans both theoretical and applied economics, with collaborations including J. M.C. Santos Silva and Roger Koenker.
Jungbin Hwang is an Associate Professor in the Department of Economics at the University of Connecticut. He specializes in econometrics theory, with a focus on improving the accuracy and robustness of Generalized Method of Moments (GMM) methods in handling time series and panel data with dependence and heterogeneity. His research also extends to financial econometrics, Bayesian methods, and cointegration analysis. Education: Ph.D., Economics, University of California, San Diego (2016) M.A., Economics, Seoul National University (2010) B.A., Economics, Seoul National University (2008) Research Interests: Efficiency and approximation in GMM estimation Cluster-robust inference and bootstrap methods Cointegration in non-stationary systems Applications to financial markets and policy analysis Teaching: Courses include Empirical Methods in Economics, Econometrics I, and advanced topics in panel data analysis. Key Contributions: His work addresses challenges in GMM inference for time series and panel data, including finite-sample corrections and robust variance estimation. Recent studies explore low-frequency cointegration and quantile regression in dynamic settings. Grants & Collaborations: Collaborations with scholars like Yixiao Sun and Gonzalo Valdés have produced influential methods for accurate econometric testing and inference. Contact: Located in 333 Herbst Hall, Storrs, CT. Office hours: Wednesdays 3:00-4:00 PM or by appointment.
Manuel Arellano is Professor of Economics at the Center for Monetary and Financial Studies (CEMFI) in Madrid since 1991, with prior appointments at the University of Oxford (1985-89) and London School of Economics (1989-91). A leading econometrician specializing in panel data analysis, his work bridges theoretical econometrics and labor economics applications. He earned his undergraduate degree from the University of Barcelona and Ph.D. from the London School of Economics. Arellano's research focuses on econometric methodology for panel data, particularly dynamic models with heterogeneity. His seminal book Panel Data Econometrics (2003) established foundational frameworks for nonlinear and dynamic panel estimation. Current work extends to distributional analysis of random coefficients and robust inference under uncertainty, maintaining consistent emphasis on labor market applications like unemployment duration and policy evaluation. His publication history reveals a 30-year trajectory advancing panel data econometrics, evolving from specification testing (1987-1995) to sophisticated dynamic and nonlinear models (2003-2014), with persistent focus on practical implementation and labor economics applications. Major honors include: President of the Econometric Society (2014) Foreign Honorary Member of the American Academy of Arts and Sciences (2014) Rey Jaime I Prize in Economics (2012) ISI Highly Cited Researcher status (2010) Fellow of the Econometric Society (2002) No information on student advising or research grants appears in the source materials. Similarly, details about research laboratories or collaborative teams are not documented in the provided texts.
Associate Professor Mahyar Shirvanimoghaddam is a distinguished academic at The University of Sydney's School of Electrical & Computer Engineering, specializing in IoT, Telecommunications, and Coding Theory. His research focuses on 6G communication strategies, ultra-reliable low-latency systems, and machine learning integration in wireless networks. He holds a PhD from The University of Sydney and has received multiple accolades, including the World Economic Forum's Young Scientist award (2018) and the Australian Award for University Teaching (2020). Education: B.Sc. (1st Class Honors) in Electrical Engineering, University of Tehran (2008) M.Sc. (1st Class Honors) in Electrical Engineering, Sharif University of Technology (2010) PhD in Electrical Engineering (Telecommunications), The University of Sydney (2015) Research Interests: IoT Communication Protocols, Rateless Coding, Non-Orthogonal Multiple Access (NOMA), 5G/6G Technologies, and Federated Learning in Wireless Networks. His work on channel coding for massive IoT and URLLC has been funded by ARC Discovery Projects and European Research Council grants. He pioneered the 'Idea Factory' interdisciplinary teaching project, blending engineering and business education. Key projects include designing 6G communication strategies (ARC 2022-2024) and robust coding for mission-critical communications (ARC 2019-2021). Awards: Over 20 awards, including teaching excellence (Vice-Chancellor's Awards 2019, 2022), research recognition (IEEE Best Paper Awards), and leadership roles in IEEE and the Higher Education Academy. He supervises 1 PhD student (Tyseer BASHIR) and actively engages in editorial roles for IEEE Transactions and other journals. His team's innovations aim to bridge technological and societal challenges in IoT and smart infrastructure.
Prosper Dovonon is Full Professor of Economics at Concordia University, Montréal, Canada, where he holds the Tier 1 Concordia University Research Chair in Econometrics of Large Datasets . He is concurrently Adjunct Professor at the University of Adelaide, Australia, and has previously served as Associate and Assistant Professor at Concordia, Visiting Professor at HEC Montréal, and Assistant Vice-President at Barclays Wealth in London. Education Ph.D. in Economics, Université de Montréal (2007) M.Sc. in Statistics and Economics, ENSEA, Abidjan, Côte d’Ivoire (2000) M.Sc. in Mathematics, Université Nationale du Bénin, Abomey-Calavi, Benin (1996) Research Interests Professor Dovonon’s research lies at the intersection of theoretical econometrics and financial data applications . He focuses on developing robust inferential procedures for moment-condition models, bootstrap techniques for high-frequency data, identification issues in GMM, and volatility modeling with factor structures that accommodate skewness and leverage effects. His work on large-dimensional datasets emphasizes scalable methods for estimation and testing in big-data environments. Scientific Awards & Recognition Concordia University Research Chair, Tier 1, in Econometrics of Large Datasets (2022–present) Collaborations & Affiliations Beyond Concordia and the University of Adelaide, he is affiliated with the Centre Interuniversitaire de Recherche en Économie Quantitative (CIREQ) in Montréal and has collaborated with leading scholars across North America, Europe, and Australia. His research is frequently cited in top econometrics and statistics journals, attesting to its broad impact.
Ji Hyung Lee is a Professor of Economics at the University of Illinois Urbana Champaign, with a courtesy appointment in the Department of Finance at Gies College of Business. His research focuses on econometric theory, time series analysis, financial econometrics, and machine learning applications in economics. He holds a Ph.D. in Economics from Yale University (2013) and a B.A. in Economics from Seoul National University (2005). His research interests include developing robust econometric methods for high-dimensional data, quantile regression techniques, and applications to macroeconomic policy and financial risk analysis. Notable contributions include work on predictive quantile regression, nonparametric density estimation, and modeling household inflation expectations. His recent articles explore topics such as machine-learning approaches to growth risk, quantile impulse responses for value-at-risk dynamics, and parameter-free methods for density estimation. Lee’s work emphasizes methodological innovation and practical relevance in policy contexts. He has held positions at multiple institutions and maintains affiliations with the Midwest Econometrics Group. His research has been published in top journals like Journal of Econometrics and Econometric Theory .
Anthony W. Lynch is a Professor of Finance at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since 1994. He is also a Research Associate at the National Bureau of Economic Research since 2002 and has held visiting positions at Columbia University and the Wharton School of the University of Pennsylvania. Education: PhD in Finance and Economics, University of Chicago, 1994 Bachelor of Commerce (Honours), University of Queensland, 1989 Bachelor of Laws (Honours), University of Queensland, 1989 Master of Financial Management, University of Queensland, 1988 Bachelor of Commerce, University of Queensland, 1986 Professor Lynch's research centers on asset pricing , portfolio choice , and mutual funds , with a strong emphasis on modeling investor behavior under uncertainty, transaction costs, and habit formation. His work integrates macroeconomic factors with financial decision-making, particularly in life-cycle investing and retirement planning. He has made significant contributions to understanding how frictions such as taxes, liquidity, and borrowing constraints impact optimal investment strategies. His recent publications demonstrate a consistent focus on dynamic portfolio optimization, stochastic volatility, and the implications of behavioral and structural frictions in financial markets. Themes across his work include time-consistency in decision-making, risk premia estimation, and the interaction between human capital and financial wealth. Scientific Awards and Honors: Glucksman Prize (twice) for best finance research paper by an NYU professor Research Associate, National Bureau of Economic Research (since 2002) Associate Editor, Review of Finance Session Chair, American Finance Association (AFA) and Western Finance Association (WFA) Meetings Professor Lynch advises doctoral students and contributes to academic leadership through editorial and conference roles. He has not received mention of external research grants in the provided text, but his NBER affiliation suggests involvement in collaborative, funded research initiatives. He teaches foundational courses including Foundations of Finance (MBA), Finance Theory I , and Seminar in Asset Pricing Theory (PhD level), shaping the next generation of finance scholars and practitioners. He is affiliated with the Department of Finance at NYU Stern and conducts research that bridges theoretical models with empirical validation, often using advanced econometric and computational methods. His academic network includes leading institutions such as Chicago, Columbia, and Wharton, reflecting his prominence in the finance community.
Patrick Gagliardini is a Full Professor of Econometrics at the University of Lugano (USI) within the Faculty of Economics and the Institute of Finance. He also serves as Pro-Rector at USI. His academic journey includes a PhD in Econometrics from USI (2003) and studies in Physics at ETH Zurich (1998). He has held roles such as Visiting Fellow at CREST Paris (2003) and Assistant Professor at the University of St. Gallen (2004–2006). His research focuses on econometric methods (nonparametric techniques, GMM, latent factor models) and financial applications such as credit risk, asset pricing, and risk management. Competence areas include Big Data, investment decisions, and systematic risk analysis. He teaches courses in econometrics, financial econometrics, and time series at the undergraduate, graduate, and PhD levels. Recent publications explore latent factor models, econometric testing (e.g., eigenvalue tests for factor detection), and financial decision-making in small data regimes. His work bridges theoretical econometrics with practical applications in finance and risk modeling. Notably, his research addresses challenges in dynamic latent factor models, hedge fund performance evaluation, and granularity theory in financial systems. He maintains an active academic profile with contributions to both theoretical and applied econometrics.
Professor Guido Kuersteiner is a leading academic in the Department of Economics at the University of Maryland . Holding a PhD from Yale University (1997), he has previously taught at prestigious institutions including MIT, Boston University, UC Davis, and Georgetown University. His research spans theoretical and applied econometrics , with focus areas in GMM estimation, spatial models, causal inference, and bias correction techniques.
