جست‌وجوی استاد و پژوهشگر

• Functional Data Analysis
• Stochastic Optimization
• Reinforcement Learning
• +۵ مورد دیگر

Yafei Wang is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, Canada, since July 2023. Previously, they served as a Lecturer at the University of Essex (2022–2023) and held a postdoctoral research fellowship at the University of Alberta (2019–2022) under Drs. Linglong Kong and Bei Jiang. Their academic journey includes a PhD from the Beijing University of Technology (2019) and a visiting PhD program at the University of Alberta (2017–2018). Education: PhD in Statistics from Beijing University of Technology (2019), supervised by Dr. Zhongzhan Zhang. Visiting PhD Student at the University of Alberta’s Department of Mathematical and Statistical Sciences (2017–2018). Research focuses on complex data analysis, with expertise in robust statistical methods for functional data (e.g., brain imaging applications), stochastic optimization for data-driven decisions, and interpretability in reinforcement learning. Current projects explore large language models in medical imaging while prioritizing fairness and privacy. Key Funding: NSERC-RGPIN Grant (2024–2029): $130,000 (PI) for Robust Statistical Modeling under Uncertainty. NSERC-DGECR Grant (2024–2029): $12,500 (PI). Networking Grant (2024–2025): £18,620 (Co-I with Peng Liu) on AI in Medical Imaging. Teaching Experience: Fall 2024: STAT 537 (Statistical Methods for Applied Research). Winter 2024: STAT 562 (Discrete Data Analysis) and STAT 432/532 (Survival Analysis). Spring 2023: MA331 (Programming and Text Analytics with R).

Andrea A. Naghiمشاهده پروفایل

مدرس

• Econometrics
• Machine Learning
• Causal Machine Learning
• +۳ مورد دیگر

Dr. Andrea A. Naghi is a Lecturer in Econometrics and Data Science at Queen Mary University of London's School of Business and Management. She holds a PhD in Economics from the University of Warwick and previously served as Assistant Professor at Erasmus University Rotterdam's Econometric Institute. Her research focuses on econometrics, machine learning, causal inference, and their applications in economics and finance. Dr. Naghi has held grants including a €170,000 Marie Skłodowska-Curie Grant (2018-2020) and a €240,000 United Nations Sustainable Development Goals grant (2018-2022). She teaches undergraduate and postgraduate courses including Entrepreneurship and Data Science: Methods and Applications. Her work emphasizes advancing causal machine learning methodologies and robust statistical techniques. Education: PhD in Economics (University of Warwick), with a visiting research period at University of California San Diego. Professional Experience: Assistant Professor at Erasmus University Rotterdam (prior to Queen Mary appointment). Research Interests: Machine learning applications in econometrics, causal inference, applied econometrics, and robust statistical methods. She actively supervises PhD students exploring machine learning in economics contexts. Grants & Awards: European Commission Horizon 2020 Marie Skłodowska-Curie Individual Grant (2018-2020, PI); United Nations Sustainable Development Goals Funds (2018-2022, PI). Teaching: BUS025 Entrepreneurship (Undergraduate), BUSM193 Data Science: Methods and Applications (Postgraduate). Methodological Expertise includes Probit models with endogeneity, inflation forecasting, and causal discovery algorithms.

Ana-Maria Staicuمشاهده پروفایل

استاد

• Functional Data Analysis
• Biostatistics
• Longitudinal Data Analysis
• +۵ مورد دیگر

Dr. Ana-Maria Staicu is a Professor in the Department of Statistics at North Carolina State University (NCSU), where she has been since 2009, progressing from Assistant to Associate Professor. She holds a Ph.D. in Statistics (2007) from the University of Toronto and a B.Sc. in Mathematics from the University of Bucharest (2000). Her research focuses on functional data analysis, longitudinal studies, biostatistics, and applications in medical imaging and social media analytics. She leads grants funded by NIH, NSF, and industry partners, including work on social media disinformation and biomedical imaging. Education: Ph.D., University of Toronto (2007); B.Sc., University of Bucharest (2000) Affiliations: Director of the NIH T32 Biostatistics Training Grant; Associate Editor for Stat , Biometrics , and JABES Research Interests: Functional data analysis, longitudinal modeling, spatial statistics, and applications in neuroscience, environmental science, and healthcare. Notable contributions include methods for analyzing brain imaging data (e.g., DTI in MS) and wearable device data. Awards: NSF CAREER Award (2015), ASA SPES Best Paper (2021), NCSU Faculty Scholar (2016), Cavell Brownie Mentoring Award (2019). Over 60 peer-reviewed publications and 10+ grants totaling $6M+. Grants/Service: Current projects include NSF-funded social media pathway analysis and NIH-funded biostatistical training in cardiovascular research. Served on grant review panels for NSF, NIH, and international foundations. Labs/Teams: Leads the FDA Lab at NCSU; collaborates with Duke-NC State-DCRI training programs and SAMSI initiatives.

Denis Pelletierمشاهده پروفایل

استاد

• Time Series Econometrics
• Financial Economics
• Pension Economics
• +۳ مورد دیگر

Denis Pelletier is the Department Head and Professor of Economics at North Carolina State University's Poole College of Management. He holds a Ph.D. in Economics from the University of Montreal (2004), specializing in Econometrics and Financial Economics. His research focuses on Time Series Econometrics, Financial Economics, and Pension Economics, with contributions to volatility modeling, risk measurement, and policy analysis in retirement savings. Educations: PhD in Economics, University of Montreal (2004); M.A. in Economics, Laval University (1998); B.A. in Mathematical Economics, Laval University (1994). Research interests include econometric methods for financial markets, dynamic correlations, high-frequency volatility estimation, and the economic impacts of public policies. His recent work examines retirement savings behavior, volcanic risk economics, and stochastic volatility models. Awarded over 20 grants and fellowships, including the Institute of Consumer Money Management Grant (2025-2027) and NCSU Teaching Excellence Award (2016). He has advised numerous PhD and Master's students, contributing to their academic and professional success in academia and finance. Service roles include Chair of the Research Committee (2010-2016) and member of the University Research Committee (2012-2014). Active in editorial and conference roles, Pelletier leads the Econometrics Workshop at NCSU and collaborates on initiatives like the Business Analytics and AI Initiative.

• Macroeconomics
• International Economics
• Development Economics
• +۳ مورد دیگر

Dan Cao is an Associate Professor in the Department of Economics at Georgetown University, located in Washington, DC. His research focuses on macroeconomic theory, international economics, and the intersection of political institutions with economic development. He holds a prominent position at Georgetown's economics department and contributes to academic discourse through numerous peer-reviewed publications. Dr. Cao’s research interests include dynamic stochastic general equilibrium (DSGE) modeling, property rights dynamics in autocratic regimes, nonlinear economic relationships, and the effects of financial frictions on macroeconomic stability. His work often bridges theoretical frameworks with policy-relevant analysis, addressing topics like wealth inequality, monetary policy limits, and technological impacts on economic cycles. Recent publications highlight his exploration of global DSGE models, asymmetric adjustment costs in Phillips curves, and the implications of zero lower bound constraints. His 2023 papers emphasize institutional dynamics in autocracies and global macroeconomic frameworks, while earlier works (2017–2020) delve into wealth distribution mechanisms and real exchange rate theories. No scientific awards or grants are explicitly listed in the provided materials. Dr. Cao’s advising activities and laboratory affiliations are not detailed here, though his extensive publication record indicates active involvement in graduate training and research collaborations.

Juan Carlos Escancianoمشاهده پروفایل

استاد

• Econometric Theory
• Financial Econometrics
• Risk Management
• +۳ مورد دیگر

Juan Carlos Escanciano holds the position of Research Chair in Economics and Full Professor (Catedrático) at the Economics Department of Universidad Carlos III de Madrid. He obtained his PhD in Economics from the same university in 2004. Prior to his current role, he served as Assistant Professor at Universidad de Navarra (2004-2006), Full Professor at Indiana University (2006-2018), and held visiting positions at Yale University, Cornell, Rochester, and MIT. His research focuses on Econometric Theory, including identification, estimation, specification testing, and applications in Financial Econometrics and Risk Management. Escanciano holds editorial roles at leading journals including Econometric Theory , Econometric Reviews , and Journal of Business and Economic Statistics , and serves as Co-Editor of Advances in Econometrics . He is a Fellow of the Journal of Econometrics and has published extensively in top-tier journals such as the Journal of the American Statistical Association and The Annals of Statistics . His research interests emphasize semiparametric/nonparametric methods, specification testing, and empirical asset pricing. Notable contributions include work on backtesting financial risk measures, conditional moment restrictions, and the development of robust estimation techniques. His recent work explores machine learning applications in econometrics and systemic risk analysis. Scientific Awards: Fellow of the Journal of Econometrics Grants & Advising: Extensive record in grant-funded research; has advised numerous PhD candidates through the university's Economics Doctoral Program. Labs/Teams: Leads the Quantitative Economics Research Group at UC3M, collaborating with global institutions on structural econometric modeling.

Álvaro Escribanoمشاهده پروفایل

استاد

• Macroeconometrics
• Microeconometrics
• Financial Econometrics
• +۶ مورد دیگر

Álvaro Escribano is a Full Professor of Applied Economics at Universidad Carlos III de Madrid (UC3M) and Director of the Carlos III International School (C3IS). He holds leadership roles including membership in the Board of Governors of UC3M, Spain's Productivity Board, and AIReF's Advisory Board. His expertise spans macroeconometrics, financial econometrics, and climate modeling. Education: PhD in Economics from UC San Diego (199?). Research focuses on climate change forecasting using score-driven models, energy economics, cointegration analysis, and industrial productivity. Recent work includes long-term climate projections and oil market dynamics. Editorial roles: Associate Editor of Macroeconomic Dynamics, Studies in Nonlinear Dynamics and Econometrics, and Cambridge Elements in Economics of Emerging Markets.

Huixia Judy Wangمشاهده پروفایل

استاد

• Quantile regression
• Extreme value theory
• Bioinformatics
• +۴ مورد دیگر

Huixia Judy Wang is a Professor in the Department of Statistics at The George Washington University since 2017. Previously, she held positions at North Carolina State University from 2006 to 2017, advancing from Assistant to Associate Professor. Her expertise spans quantile regression, extreme value theory, bioinformatics, and longitudinal data analysis with applications in environmental science, health, and finance. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2006 M.S., Statistics, Fudan University, 2002 B.S., Statistics, Fudan University, 1999 Research focuses on developing robust statistical methods for analyzing extremes, high-dimensional data, and complex dependencies. Key themes include quantile-based approaches for spatial/temporal data, survival analysis with detection limits, and subgroup identification in clinical trials. She has contributed to methodological advancements in copula models, threshold regression, and Bayesian quantile inference. Her publications address applied problems such as climate variability impacts, biomedical data interpretation, and financial risk modeling. She maintains an active record of interdisciplinary collaboration across environmental science, genomics, and public health.

• Financial Mathematics
• Actuarial Science
• Stochastic Processes
• +۳ مورد دیگر

Francesca Biagini is a Professor of Mathematics at the University of Munich (LMU), specializing in Financial and Insurance Mathematics. She holds significant administrative roles, including Vice President for International Affairs and Diversity at LMU since 2019, and serves as President of the Bachelier Finance Society (2022–2023). Her research focuses on stochastic processes, financial mathematics, systemic risk, and actuarial science. She leads the Munich Risk and Insurance Center (MRIC) and contributes to initiatives like the quantLab workshop series. Her work bridges theoretical advancements with practical applications in finance and insurance, addressing topics such as asset pricing bubbles, credit risk modeling, and deep learning applications in finance. Research interests include stochastic calculus, fractional Brownian motion, risk-minimization strategies, and systemic risk modeling. Notable contributions span publications on financial derivatives, insurance valuation, and regulatory frameworks. She has held visiting positions and collaborates internationally, reflecting her leadership in academic and professional communities. Publications highlight innovative methods in financial modeling, such as neural network-based superhedging and liquidity-driven bubble analysis. Her work on xVA calculations and systemic risk transfer equilibrium underscores practical applications for financial institutions. Academic service includes roles in educational programs and professional societies, emphasizing interdisciplinary research and international collaboration.

Steven Liمشاهده پروفایل

استاد

• Financial Market Efficiency
• Asset Pricing
• Corporate Finance
• +۷ مورد دیگر

Professor Steven Li is a Professor of Finance at the College of Business and Law (COBL), RMIT University, with a PhD from Delft University of Technology, an MBA from Melbourne Business School, and a BSc from Tsinghua University. He specializes in financial market efficiency, asset pricing, corporate finance, derivatives, FinTech, and sustainable finance, with over 100 published papers. His teaching includes Financial Management, FinTech, and Sustainable Finance. He has held visiting scholar roles at institutions like the Bank of Japan and University of Wollongong. His consulting expertise spans News Corporation, Bank of Japan, and Australian government agencies. Research interests span quantitative finance, corporate finance, derivatives pricing, market efficiency, and sustainable finance. Recent articles focus on optimization algorithms, financial markets, and multi-UAV systems. He actively supervises PhD and Master’s students in finance-related topics. His work integrates mathematical modeling to address real-world business challenges.

Max Farrellمشاهده پروفایل

دانشیار

• Machine Learning
• Econometrics
• High-Dimensional Data Analysis
• +۵ مورد دیگر

Max Farrell is an Associate Professor of Economics and the Mellichamp Chair of Mind and Machine Intelligence at the University of California, Santa Barbara (UCSB) , within the Department of Economics of the College of Letters & Science . He is also an Amazon Scholar and serves as an associate editor for the Review of Economics and Statistics and Journal of Applied Econometrics , with editorial board membership at the Journal of Machine Learning Research . His research focuses on machine learning , high-dimensional data , semiparametric/nonparametric methods , and causal inference , with applications in econometrics and statistics. Farrell holds a Ph.D. in Economics and M.A. in Statistics from the University of Michigan, and S.B. degrees in Mathematics and Economics from MIT. His work emphasizes developing novel statistical methods, such as open-source software tools like rdrobust (for regression discontinuity designs), binsreg (binscatter regression), and nprobust (nonparametric kernel-based estimation). These tools have been recognized for their practical impact in applied research. His research trends span regression discontinuity designs , deep learning applications in causal inference, and nonlinear binning methods for visualization and estimation. His recent work explores individual heterogeneity in treatment effects and high-dimensional econometric models . Farrell’s software contributions have been widely adopted in empirical economics and statistics. Scientific Awards : His software packages have received awards for their innovation and utility in statistical analysis. Advising & Grants : While no students are explicitly listed here, his software development and editorial roles reflect significant contributions to the field. His work is supported by grants from Amazon and UCSB’s Mellichamp Chair. Labs/Teams : Collaborates extensively with researchers like Matias Cattaneo , Tengyuan Liang , and Sebastian Calonico on projects in econometrics and machine learning.

Kostas Kalogeropoulosمشاهده پروفایل

دانشیار

• Bayesian Inference
• Stochastic Differential Equations
• Markov Chain Monte Carlo
• +۶ مورد دیگر

Kostas Kalogeropoulos is an Associate Professor and MSc Statistics Programme Director in the Department of Statistics at the London School of Economics and Political Science (LSE). He previously served as a post-doctoral researcher at the University of Cambridge's Signal Processing Laboratory and holds a PhD from the Athens University of Economics and Business. His research focuses on Bayesian inference methodologies applied to stochastic differential equations, with applications in financial econometrics, biomedical modeling (e.g., epidemic dynamics), and growth curve analysis. Dr. Kalogeropoulos teaches advanced courses including ST451: Bayesian Machine Learning and ST308: Bayesian Inference at LSE, as well as the summer school course ME315: Machine Learning in Practice. He is actively involved in academic administration as Programme Director of LSE’s MSc in Statistics and organizes the Theory of Machine Learning Reading Group. His professional affiliations include the Data Science Group and Social Statistics Group within the LSE Department of Statistics, as well as leadership roles in the Greek Stochastics Team. His research outputs span computational statistics and applied Bayesian methods, with notable contributions to spatio-temporal epidemic modeling, stochastic volatility analysis, and dynamic term structure modeling. He has collaborated with international institutions on projects addressing public health challenges and financial econometric applications.

Clifford Lamمشاهده پروفایل

استاد

• Statistical Learning
• Time Series Analysis
• High-Dimensional Data Analysis
• +۳ مورد دیگر

Clifford Lam is a Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). He joined LSE in 2008 after earning his PhD in Operations Research & Financial Engineering from Princeton University. His research focuses on high-dimensional data analysis, time series modeling, and spatial econometrics. Key areas of expertise include statistical learning techniques, covariance matrix estimation, and factor modeling for high-dimensional time series. His research interests span several domains: developing methodologies for high-dimensional data (e.g., semiparametric modeling and regularization), time series analysis (identifying latent factors in large datasets such as financial markets), and improving spatial econometric models by refining spatial weight matrix estimation for spatial panel data. No scientific awards or honors are explicitly mentioned in the provided information. His work has contributed to grants and projects in statistical methodologies and econometrics, though specific grants are not detailed here. He advises students and researchers in his field, though names are not listed. Lam is affiliated with the LSE Department of Statistics and maintains an office in COL.6.09. Office hours are bookable via the Student Hub.

• Monetary Policy
• Inflation Expectations
• Macroeconomics
• +۵ مورد دیگر

Stefano Eusepi is a Professor of Economics at Brown University, specializing in macroeconomic theory, monetary policy design, and the role of expectations in economic dynamics. His research focuses on understanding inflation expectations, central bank communication strategies, and the effectiveness of monetary policy under imperfect knowledge conditions. He has contributed significantly to the development of large-scale macroeconomic models such as the FRBNY DSGE framework. His scholarly work examines topics including the Phillips curve in crisis contexts, learning mechanisms in economic models, and the implications of policy uncertainty for long-term debt pricing. Eusepi’s publications span journals and working papers addressing key issues in modern macroeconomics, including fiscal-monetary coordination, expectations-driven fluctuations, and unconventional monetary policies. Notably, his research emphasizes the importance of adaptive learning in explaining economic behavior and policy outcomes, challenging traditional rational expectations assumptions. While no specific awards are listed, his consistent output in top-tier economic forums underscores his scholarly impact. Advising and grants are not detailed in the provided texts. He is affiliated with Brown University’s Department of Economics and likely contributes to academic initiatives there. His work often intersects with policy-oriented research, reflecting collaboration with institutions like the Federal Reserve Bank of New York.

Vassilis Hajivassiliouمشاهده پروفایل

دانشیار

• Applied Microeconometrics
• Theoretical Econometrics
• Simulation-Based Inference
• +۶ مورد دیگر

Vassilis Hajivassiliou is an Associate Professor of Economics and Programme Director of the MSc in Econometrics and Mathematical Economics at the London School of Economics (LSE). He holds affiliations with STICERD’s Econometrics Programme and the Financial Markets Group (FMG). His expertise spans applied and theoretical microeconometrics, simulation-based inference, panel data analysis, and corporate finance, with a focus on financing constraints and firm behavior. Education includes a PhD in Economics from MIT (1985), MSc (Distinction) and BSc (First Class Honours) from LSE. He has held academic positions at Yale University (1985–1995) and visiting roles at institutions like SciencesPo Paris and Columbia University. Research interests emphasize econometric methods for panel data, thick-tailed distributions, and simulation techniques. Notable contributions include work on LDV models, bimodal t-ratios, and firm financing dynamics. He has received awards such as the Pareto Prize (1998) and the Ely Devons Prize (1981). Teaching includes advanced econometrics modules (EC402, EC443) and leadership in LSE’s graduate admissions and summer school programs. His software contributions, such as SIMMNP and NRPSIM.LIB, advance computational econometrics.