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Fuyu YangView profile

Researcher

• Econometrics
• Bayesian Inference
• Macroeconomic Forecasting
• +6 more

Fuyu Yang is an Early Stage Researcher at Humboldt-Universität zu Berlin, affiliated with the Wirtschaftswissenschaftliche Fakultät (Faculty of Humanities and Social Sciences) under the COMISEF project. She holds a PhD in Economics from the University of Leicester, UK, awarded in January 2009. Her academic work bridges econometric modeling and macroeconomic forecasting, with a strong emphasis on Bayesian methods and high-dimensional latent structures. Research Interests: Her research focuses on advanced econometric techniques, particularly Bayesian inference in models with high-dimensional latent variables and model selection using information flow. She actively contributes to the fields of nowcasting and forecasting macroeconomic variables, leveraging mixed-frequency data and the impact of economic announcements. Her interdisciplinary approach combines statistical rigor with practical macroeconomic applications. Publication Trends: Her recent work demonstrates a consistent focus on Bayesian econometrics and dynamic modeling. The articles highlight her expertise in stochastic volatility, unit root models, and yield curve modeling, with applications in forecasting and model evaluation. She frequently collaborates with leading researchers in the field, indicating strong integration into the international econometrics community. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: There is no information available about students she may have advised or grants she has received. However, her role as a Graduate Teaching Assistant during her postgraduate studies at Leicester indicates prior experience in academic instruction and mentorship. Labs and Teams: Fuyu Yang is part of the COMISEF (Computational and Methodological Statistics for Economics and Finance) project at Humboldt-Universität zu Berlin, which fosters training, knowledge transfer, and collaborative research in computational statistics and econometrics. This affiliation places her within a dynamic, interdisciplinary research network focused on methodological innovation in economics and finance.

Marcus MichelenView profile

Assistant Professor

• Probability
• Combinatorics
• Statistical Physics
• +7 more

Marcus Michelen is an Assistant Professor in the Department of Mathematics, Statistics, and Computer Science at the University of Illinois, Chicago (UIC), within the College of Liberal Arts and Sciences. He earned his Ph.D. in Mathematics from the University of Pennsylvania in 2019 under Robin Pemantle and completed his undergraduate studies at The Cooper Union with a B.S.E. in General Engineering and a minor in Mathematics. He began at UIC as a postdoc in 2019 and transitioned to Assistant Professor in 2021 through the Bridge-to-Faculty program. His research lies at the intersection of probability, combinatorics, statistical physics, and theoretical computer science , with recent work focusing on random matrices, random polynomials, sphere packing, and Gibbs point processes. He frequently uses complex- and Fourier-analytic techniques in discrete settings. His research is supported by an NSF CAREER grant (DMS-2336788) and other NSF awards. His most recent publications explore topics such as lower bounds in Ramsey theory, root separation in random polynomials, gossip propagation via percolation, and limits in sphere packing. These works appear in top journals like the Journal of the American Mathematical Society , Probability Theory and Related Fields , and Combinatorics, Probability and Computing , reflecting a strong trend toward probabilistic methods in combinatorial and geometric problems. NSF CAREER Grant DMS-2336788 Penn Prize for Excellence in Teaching by Graduate Students (2016) Herbert S. Wilf Memorial Award University of Pennsylvania Math Department Good Teaching Awards (2015, 2016) Marcus advises one master’s student and three Ph.D. students and has mentored undergraduate research in mathematics and origami. He has taught courses ranging from Calculus III to graduate-level combinatorics and probability. He was featured in the 2024 Lathisms Calendar and has contributed to public understanding of mathematics through Quanta Magazine and La Recherche .

• Biophysics
• Bioimaging
• Optical Microscopy
• +6 more

Andreas E Vasdekis serves as an Associate Professor in the Department of Physics at the College of Science, University of Idaho, with cross-affiliated roles as Research Faculty at the Institute for Health in the Human Ecosystem, Member of the Initiative for Bioinformatics and Evolutionary Studies, and Participating Faculty at the Institute for Modeling Collaboration and Innovation. His academic foundation includes a PhD in Physics (2008) and MSc in Optics and Photonics (2003) from the University of St Andrews, United Kingdom, and a BSc in Physics (2002) from the University of Athens, Greece. Dr. Vasdekis pioneers biophysical imaging methodologies, specializing in label-free microscopy and single-cell quantitative analysis . His research integrates optofluidics , deep learning , and Raman spectroscopy to decode cellular heterogeneity, metabolic noise, and nongenetic plasticity. Key innovations include photon-sparse bioimaging, Airy light-sheet microscopy, and gradient retardance optical techniques for tissue and microbial systems. Recent publications reveal a dominant trend toward computational bioimaging , where deep learning reconstructs high-fidelity images from near-zero photon data, and multimodal light-sheet platforms enabling 3D analysis of plant-microbe symbiosis, malaria parasite dynamics, and multicellular gradient responses—all unified by a physics-driven approach to biological complexity. He actively collaborates through the Initiative for Bioinformatics and Evolutionary Studies and the Institute for Modeling Collaboration and Innovation, driving interdisciplinary convergence in biophysics, data science, and biological discovery.

Lealand MorinView profile

Assistant Professor

• econometrics
• time series analysis
• statistics
• +4 more

Dr. Lealand Morin is an Assistant Professor of Economics at the University of Central Florida, College of Business, Department of Economics. He holds a Ph.D. in Economics from Queen's University (Canada) and brings industry experience as a former statistician and data scientist at Capital One Bank. His teaching portfolio includes courses in time series analysis, econometrics, statistics, machine learning, and data mining. Dr. Morin's research focuses on empirical and theoretical econometrics, with applications in financial modeling, consumer credit behavior during crises, and monetary policy. His work emphasizes analytical techniques for business and finance contexts. Recent publications explore interest rate dynamics, credit market interventions during the COVID-19 pandemic, and regulatory impacts on driver behavior. He maintains active media engagement, providing expert commentary for outlets like MoneyGeek on topics including credit cards, auto insurance, and financial decision-making. Laboratories/Teams: Dr. Morin integrates professional data science expertise into academic training, preparing students for analytical roles in business environments.

Loukia MeligkotsidouView profile

Associate Professor

• Bayesian Inference
• Computational Statistics
• Hidden Markov Models
• +4 more

Loukia Meligkotsidou is an Associate Professor of Statistics at the Department of Mathematics of the National and Kapodistrian University of Athens. She holds a BSc in Statistics from Athens University of Economics and Business (2002) and a PhD in Statistics from Lancaster University (2005), focusing on filtering methods for population genetics, phylogenetics, and mixture models. Her research interests span Bayesian inference, computational statistics, hidden Markov models, biostatistics, and econometrics. She teaches undergraduate courses on Bayesian Inference and postgraduate courses on Time Series analysis. Her work emphasizes methodological advancements in statistical modeling, particularly in longitudinal data analysis, competing risks, and model uncertainty. Recent publications address issues like shared parameter models for longitudinal data, Bayesian unit root testing, and quantile regression approaches in financial forecasting. She is a Fellow of the Royal Statistical Society and active in professional societies like ISBA and IMS. Her research integrates theoretical contributions with applied problems in economics, finance, and biology. Notable contributions include frameworks for handling informative dropout mechanisms and developing efficient Bayesian computational methods for complex models. Her work bridges statistical methodology and real-world applications, particularly in econometrics and biostatistics.

• Nonstationary time series econometrics
• Nonparametric statistics
• Econometric Theory
• +2 more

Professor Qiying Wang is a faculty member in the School of Mathematics and Statistics at the University of Sydney, holding the rank of Professor. His research focuses on advanced statistical theory and econometrics, particularly in nonstationary time series analysis, cointegrating regression, and asymptotic methods. He has published extensively in top journals such as Econometric Theory and Journal of Econometrics . Research Interests: Nonstationary time series econometrics, nonparametric statistics, econometric theory, local time theory, and self-normalized limit theory. He is a leading expert in nonlinear cointegrating regression, developing methodologies for handling endogeneity and nonstationarity in economic and financial data. Selected Grants: 2016: Nonlinear cointegrating regression with endogeneity (Australian Research Council DP) 2012: Asymptotics in nonlinear cointegrating regression (Australian Research Council DP) Key Contributions: Authored the monograph Limit Theorems for Nonlinear Cointegrating Regression (2015) and pioneered techniques for optimal bandwidth selection in cointegrating models. His work bridges theoretical advancements with practical econometric applications.

Thorsten RaaschView profile

Professor

• Numerical Mathematics
• Adaptive Discretization Methods
• Partial Differential Equations
• +7 more

Thorsten Raasch is a Professor of Numerical Mathematics at the Institute of Mathematics, Johannes Gutenberg University Mainz, Germany. He is affiliated with the Department of Physics, Mathematics, and Computer Science, and leads research in numerical analysis, particularly adaptive methods for PDEs and inverse problems. Research Interests: His work focuses on adaptive discretization methods for partial differential equations and inverse problems, non-smooth numerical optimization , wavelet systems , multilevel frames , and GPGPU computing . These interests are deeply rooted in applied and computational mathematics, with applications in scientific computing and engineering. The most recent articles show a strong trend in developing adaptive wavelet-based numerical schemes for solving inverse and stochastic PDEs, with a particular emphasis on sparsity, convergence analysis, and preconditioning. His publications span high-impact journals in numerical analysis and inverse problems, indicating sustained scholarly activity. Teaching: He has taught a variety of courses including Fundamentals of Numerics, Convex Optimization, Numerical PDEs, Spectral Methods, and advanced seminars on topics like semi-smooth Newton methods and reduced basis methods. Thorsten Raasch earned his doctorate in 2007 from Philipps University of Marburg and has held academic positions at JGU Mainz, the University of Siegen, and the University of Rostock. He was appointed W2 Professor at JGU Mainz in 2015 and previously held a junior professorship there. No scientific awards are mentioned in the provided texts. Advising and Grants: While no students or grants are explicitly listed, his role as a professor and active researcher suggests involvement in supervising graduate students and participating in research projects, possibly within the 'Mathematics of Computation' research network. Labs and Teams: He is part of the Numerical Mathematics Working Group at JGU Mainz and contributes to the 'Mathematics of Computation' research network, indicating collaboration within a structured research environment.

Alexey OnatskiyView profile

Professor

• Econometrics
• Statistics
• Factor Models
• +3 more

Alexey Onatskiy is a Professor of Econometrics at the Faculty of Economics, University of Cambridge , and serves as Theme Coordinator at the Janeway Institute. He also directs the MPhil in Data Science program. His research focuses on econometrics, statistics, factor models, and large random matrices. PhD, Harvard University (2001) Formerly at Columbia University (2001–2010) Current affiliation at Cambridge since 2010 Research Interests encompass high-dimensional statistical inference, factor models, and applications of random matrix theory to econometrics. His work addresses challenges in cointegration, unit root testing, and spectral analysis of large datasets. Article Trends include methodological advancements in factor model validation, asymptotic theory for high-dimensional time series, and spectral distribution analysis. Key themes are econometric theory, statistical robustness, and computational economics. Advising roles include supervision of PhD students Yinfeng Zeng, Christian Tien, and Radu Cristea, with research topics spanning causal inference, factor models, and policy analysis. Collaborations include affiliation with the Janeway Institute and Churchill College.

Jesús GonzaloView profile

Professor

• Time Series Econometrics
• Quantitative Climate Change
• Long Memory Processes
• +5 more

Jesús Gonzalo is a Full Professor in the Department of Fundamentals of Economic Analysis at University Carlos III de Madrid (UC3M), where he has held a professorship since 2003. He previously served as an Associate Professor at Boston University (1991-1996) and UC3M (1996-2003). With over 7,700 Google Scholar citations and recognition in REPEC rankings, he is a leading figure in time series econometrics and climate change research. His research focuses on Time Series Econometrics , including long memory processes , nonlinear dynamics , and predictive modeling , with significant contributions to Climate Change Quantification and Financial Econometrics . Recent work includes quantile factor models and heterogeneous predictive associations of CO2 emissions with global warming. His scientific awards include the Sloan Ph.D. Fellowship (1990-91) Who's Who Among Students in American Universities (1991) UC3M Research Award (1999-2025) Fellow of the Journal of Econometrics (2014) Fellow of the International Association for Applied Econometrics (2019) . He has also been acknowledged in global rankings as a top 3% and 5% economist in REPEC journal impact metrics. At UC3M, he teaches Undergraduate Econometric Techniques and Master-level Econometrics III courses. His collaborative work with researchers like Juan José Dolado and Jean-Yves Pitarakis has produced influential publications in journals such as Econometrica , Journal of Econometrics , and International Economic Review .

Ostap OkhrinView profile

Professor

• Econometrics
• Statistics
• Transportation Systems
• +4 more

Ostap Okhrin serves as a Professor of Econometrics and Statistics at Dresden University of Technology, holding the Chair of Econometrics and Statistics with a special emphasis on Transportation Systems. His academic career is marked by a strong focus on methodological advancements in econometrics and statistics, applied to complex real-world problems in transportation and finance. Professor Okhrin's research interests span econometrics, statistical theory, copula modeling, time series analysis, and financial risk management. He has significantly expanded into machine learning and reinforcement learning applications for autonomous systems, with deep expertise in traffic flow modeling, autonomous driving, maritime navigation, and financial volatility estimation. His work bridges theoretical statistics with practical engineering challenges, particularly in transportation systems and risk forecasting, addressing high-dimensional data and dynamic environments through innovative methodological frameworks. Analysis of Okhrin's recent publications (2024-2025) reveals a pronounced interdisciplinary trajectory integrating reinforcement learning with transportation engineering. Key themes include drone-based trajectory data collection for traffic monitoring, algorithms for autonomous ships on inland waterways, and Sim2Real transfer frameworks for autonomous driving. Concurrently, he advances financial econometrics through high-frequency risk forecasting models incorporating realized moments. This dual focus demonstrates his ability to transfer statistical innovations across domains while maintaining rigorous theoretical foundations in copula theory and time series analysis.

Hao YuView profile

Professor

• Statistical Computing
• Parallel Programming
• Financial Time Series
• +4 more

Hao Yu is a full-time Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario . Holding a Ph.D. from Carleton University (1993), he specializes in statistical computing, financial time series analysis, and stochastic modeling. Educational Background Ph.D., Carleton University (1993) Research Interests focus on computational statistics, time series analysis, and probabilistic modeling. His work includes developing statistical algorithms for financial applications ( NSERC-funded 2013–2018) and theoretical advancements in residual analysis for ARMA/GARCH models. Academic Contributions include co-developing R packages for time series analysis, advancing maximum likelihood unit root tests, and creating novel methods for heavy-tailed risk factor simulation in finance. His research intersects computational methodology with financial applications. Scientific Awards are not explicitly mentioned in the provided text. Advising notable students: Yifan Li (PhD) Xixian Liu (MSc Financial Mathematics) Xiyang Zhang (MSc Thesis Track in Statistical Sciences)

Offer LiebermanView profile

Professor

• Econometric Theory
• Econometric Practice
• Time Series Analysis
• +10 more

Offer Lieberman is a Tenured Professor in the Department of Economics at Bar-Ilan University, Ramat Gan, Israel, a position he has held since 2012. Prior to this, he served as a Tenured Associate Professor and Senior Lecturer at the Technion-Israel Institute of Technology (2000-2007, 1999-2000), and as a Tenured Professor at Haifa University (2007-2012). He has also held multiple Visiting Professor roles at Yale University (1999-2002, 2017-2018) and the University of Iowa (2000). His research spans Econometric Theory Time Series Analysis Spatial Econometrics Long Memory Processes Multivariate GARCH Derivative Pricing Behavioral Finance . Education : Ph.D. in Econometrics, Monash University (1993) M.Com. in Economics (First Class Honors), University of Canterbury (1990) B.Com. in Economics, University of Canterbury (1989) Lieberman’s scholarly work focuses on advanced econometric techniques, including Hybrid Stochastic Local Unit Root Models Temporal Aggregation in Financial Indices Similarity-Based Spatial Autoregressive Models High-Dimensional and Nonlinear Time Series . His methodological contributions have been applied to financial volatility, housing markets, and nonlinear dynamics. Scientific Awards and Recognitions : William Gittes Chair in Macroeconomics (2012-present) Research Fellow, Centre de Recherche en Economie et Statistique, Paris, France (1992-1993) Lieberman co-founded the Research Institute for Econometrics (RIE) at Bar-Ilan University (2012-2017) and has served on the program committees of major Econometric Society conferences. He is an Associate Editor for Econometric Theory and The Econometrics Journal , and a member of The Econometric Society since 1996.

Evren Mert TuranView profile

Lecturer

• Process Systems Engineering
• Machine Learning
• Optimization
• +3 more

Dr. Evren Mert Turan is a Lecturer at ETH Zürich's Department of Energy and Process Systems Technology. His academic background includes a Bachelor's and Master's in Chemical Engineering from the University of Cape Town, followed by a PhD in Process Systems Engineering at the Norwegian University of Science and Technology. His research focuses on integrating machine learning and optimization techniques to address decision-making challenges under uncertainty in energy systems and process engineering. Evren's expertise spans model predictive control, real-time optimization, and data-driven approaches for complex systems. He has contributed to advancements in semi-infinite programming, feedback control policies, and steady-state detection algorithms. His work emphasizes practical applications in sustainable energy systems and industrial process optimization. Key research trends include the development of neural network-based control strategies, convex optimization methods for reduced computational complexity, and experimental validation of novel algorithms. His publications highlight interdisciplinary approaches blending machine learning with traditional engineering methodologies. Evren currently teaches the course 'Introduction to Modeling and Optimization of Sustainable Energy Systems' and actively engages in collaborative research at ETH Zürich. His contributions to scientific machine learning aim to enhance robustness and reliability in dynamic systems analysis.

• Nonstationary time series econometrics
• Nonparametric statistics
• Econometric Theory
• +4 more

Qiying Wang is a Professor of Statistics and Econometrics in the School of Mathematics and Statistics at the University of Sydney. His research focuses on nonstationary time series econometrics, nonparametric statistics, econometric theory, local time theory, and self-normalized limit theory. He is a member of the Statistics research group. Research Interests : Nonstationary time series econometrics Nonparametric statistics Econometric Theory Local time Theory Self-normalized limit theory Publications : Dr. Wang has published extensively in top journals such as Econometric Theory, Journal of Econometrics, and Statistica Sinica. Recent work includes advancements in nonlinear cointegration, optimal bandwidth selection, and stochastic processes analysis. His contributions span theoretical econometrics, time series modeling, and statistical methodologies. Awards/Grants : While no explicit awards are listed, his prolific publication record underscores sustained academic excellence. His research has been supported through collaborations with institutions and co-authors globally. Lab/Teams : Engaged with the Statistics research group at the University of Sydney, contributing to collaborative projects in econometric theory and statistical inference.

Shuping ShiView profile

Professor

• Financial Econometrics
• Applied Economics
• Bubble Detection
• +2 more

Shuping Shi is a Professor in the Department of Economics at Macquarie University, affiliated with the Centre for Risk Analytics and Housing and Urban Research Centre. Her research focuses on financial econometrics with applications to real estate markets and bubble detection. Key research areas include: Development of PSY bubble monitoring technique used by central banks High-frequency data analysis for financial markets Risk assessment in global housing markets Causal change detection in economic systems Publications demonstrate strong focus on empirical finance methodologies, particularly volatility modeling, unit root testing, and bubble detection algorithms. Recent work advances statistical methods for real-time market monitoring. She has secured over $1 million in external funding including Australian Research Council grants: Discovery Early Career Researcher Award (2019-2021) and Discovery Project Awards (2015-2018, 2019-2021). Her bubble detection techniques have been adopted by institutions including Federal Reserve Bank of Dallas and Hong Kong Monetary Authority.