(in alphabetical order):

Wolfgang Härdle, Humboldt University of Berlin

Wolfgang Karl Härdle completed his Dr. rer. nat. in Mathematics at Heidelberg University and received his habilitation in Economics at Friedrich Wilhelm Universität Bonn. He is now Ladislaus von Bortkiewicz Professor of Statistics at Humboldt-Universität zu Berlin. He was the director of the Collaborative Research Center CRC 373 “Quantification and Simulation of Economic Processes” (1994 - 2003) and of C.A.S.E. (Center for Applied Statistics and Economics) (2001 - 2014). He founded the CRC 649 “Economic Risk” (2005 - 2016) and has directed the SinoGerman International Research Training Group IRTG1792 “High dimensional non stationary time series analysis” (2013-2023). He has invented the CRIX Crypto Currency IndeX, the FRM Financial Risk Meter and the Quantlet app layer. He heads the BRC blockchain-research-center.com and has created the P2P knowledge platform quantinar.com. Currently he directs IDA https://www.theida.net/ Institute Digital Assets in Bucharest, Romania.

He has influential contributions on machine learning, computational statistics and quantitative finance. He has published 30+ books and 400+ papers in top statistical, econometrics and finance journals. He is a highly cited scientist as reflected by various citation indices across worldwide ranking platforms, like SSRN, REPEC, Scopus and Google scholar, see https://www.theida.net/wkh/. He has supervised more than 80 PhD students. He provides professional consulting service on FinTech, complex data analysis and dynamic decision analytics. He is Yushan fellow in Taiwan, leading researcher on sentiment distillation, crypto currencies and DEDA Digital Economy & Decision Analytics, and has successfully established long-term collaboration with prestigious institutions in the USA, Singapore, UK, China, and Japan.

Lecture Topic: PyTSA Applied Time Series Analysis with Python

Arthur Lewbel, Boston College

Arthur Lewbel is a Professor of Economics at Boston College, in Chestnut Hill, Massachusetts, USA. He is the inaugural holder of the Barbara A. and Patrick E. Roche Chair in Economics at BC. He was a co-editor of Econometric Theory, of The Journal of Business and Economic Statistics, and of Economics Letters. He has also served on the editorial boards of The Journal of Econometrics and The Journal of Applied Econometrics. He is an elected fellow of the Econometric Society, a fellow of the Journal of Econometrics, and has a Multa Scripsit award from Econometric Theory. Prof. Lewbel’s research is mainly in the areas of micro econometrics, consumer demand analysis, and household economics. He has published over two dozen papers in the top five economics journals, including ten publications in Econometrica and five in the American Economic Review. He received a B.S. in Mathematics and Ph.D. in Management Applied Economics both from the Massachusetts Institute of Technology.

Lecture Topic: Identification in Econometrics

Guodong Li, The University of Hong Kong

Guodong Li currently is a professor at the Department of Statistics and Actuarial Science, University of Hong Kong. He received PhD degree in statistics from the University of Hong Kong in 2007 and then joined the Division of Mathematical Sciences, Nanyang Technological University, Singapore, as an Assistant Professor. He returned to the University of Hong Kong in 2009. His current research areas include time series analysis, econometrics, high-dimensional data analysis and machine learning. He has published more than 70 papers so far, and many of them are at top econometric journals, top statistical journals or top computer science conferences on machine learning.

Lecture Topics:

[1] High-Dimensional Time Series Modeling and Forecasting via Tensor Techniques

[2] High-Dimensional Time Series Modeling and Forecasting via Machine Learning Techniques

Zhipeng Liao, University of California, Los Angeles

Zhipeng Liao is a Professor of Economics at the University of California, Los Angeles (UCLA). He received his Ph.D. in Economics from Yale University in 2012. His research lies in econometric theory, with a focus on model comparison and specification testing, semiparametric and nonparametric inference, and econometric methods for time series and macro-financial data. His work develops new statistical tools for evaluating economic models and conducting robust inference in complex econometric settings. His research has appeared in leading journals including Econometrica, Review of Economic Studies, Annals of Statistics, and Journal of Econometrics. He currently serves as Co-Editor of Econometric Theory and Associate Editor of the Journal of Econometrics, Journal of Business & Economic Statistics, and Econometrics Journal, and will serve as Associate Editor of Econometrica starting in July 2026.

Lecture Topic: Model Comparison and Forecast Evaluation in Econometrics and Machine Learning

Oliver Linton, University of Cambridge

Oliver Linton is a Fellow of Trinity College and is Professor of Political Economy at Cambridge University, and is currently the Chair of the Faculty of Economics. Formerly, he was Professor of Econometrics at the London School of Economics and Professor of Economics at Yale University. Professor Linton obtained his PhD in Economics from the University of California at Berkeley in 1991. He has published three books and nearly two hundred articles on econometrics, statistics, and empirical finance. In 2015 he was a recipient of the Humboldt Research Award of the Alexander von Humboldt Foundation. He was Co-editor at the Journal of Econometrics between 2014 and 2019. He is a Fellow of the Econometric Society, the Institute of Mathematical Statistics, and the British Academy. He was President of the Society for Financial Econometrics from 2021-2023. He was a lead expert in the U.K. Government Office for Science Foresight project: “The future of Computer Trading in Financial Markets”, which published in 2012. He has appeared as an expert witness in several cases involving market manipulation.

Lecture Topic: Nonparametric Methods in Economics and Finance

Whitney K. Newey, Massachusetts Institute of Technology

Whitney K. Newey is the Ford Professor of Economics at Massachusetts Institute of Technology. He is a Distinguished Fellow of the American Economic Association, Member of the American Academy of Arts and Sciences, and a Fellow of the Econometric Society. He is the recipient of the 2026 Erwin Plein Nemmers Prize in Economics. He also served as Co-editor of Econometrica, as Program Co-chair for the 2005 World Congress of the Econometric Society, and on the Executive Committee of the Econometric Society. Professor Newey is best known for his contribution to the development of the Newey-West estimator of the variance of estimators in the presence of autocorrelation and heteroskedasticity. He has also contributed to the development of other important econometric techniques, such as nonparametric instrumental variable identification and estimation, dynamic or nonlinear panel data models, and semiparametric estimation depending on unknown functions. He has published extensively on these and other topics in top academic journals such as Econometrica, Journal of Political Economy, The Review of Economic Studies, Journal of the American Statistical Association, and the Journal of Econometrics. His current research interests include debiased machine learning, linear estimation of nonseparable panel models, and economic demand estimation in panel data.

Lecture Topics:

[1] An Introduction to Machine Learning Using Lasso, Neural Nets, and Random Forests

[2] Double Lasso Estimation of Regression Coefficients

[3] Automatic Debiased Machine Learning

[4] Construction of Neyman Orthogonal Moment Functions

Qiwei Yao, London School of Economics and Political Science

Qiwei Yao is a Professor of Statistics at the London School of Economics and Political Science. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute. An internationally renowned statistician, he has long been engaged in teaching and research in statistics. His main research areas include time series analysis, spatio-temporal processes, and financial econometrics. Professor Yao is widely recognized for his pioneering work on nonlinear and high-dimensional time series. He has published more than 110 papers in leading journals, including top statistics journals such as Journal of the American Statistical Association, Annals of Statistics, and Journal of the Royal Statistical Society: Series B, as well as leading econometrics journals such as Econometrica and Journal of Econometrics. His research has been supported by multiple grants from UK national funding agencies, including EPSRC and BBSRC. His monograph Nonlinear Time Series: Nonparametric and Parametric Methods (co-authored with Jianqing Fan) was published by Springer in 2003, and The Elements of Financial Econometrics (co-authored with Jianqing Fan) was published by Cambridge University Press in 2017. He has served as Joint Editor of the Journal of the Royal Statistical Society: Series B and as an Associate Editor for several leading journals, including Annals of Statistics and the Journal of the American Statistical Association. He has also provided consultancy services to companies such as Barclays, EDF Energy, and Winton Group.

Lecture Topics:

[1] Autoregressive Networks with Dependent Edges and Goodness-of-Fit

[2] Cointegration Between Two Intrinsically Stationary Spatial Processes