جست‌وجوی استاد و پژوهشگر

• Operational Research
• Management Science
• Optimisation
• +۸ مورد دیگر

Dr John Lamb is a Lecturer at the University of Aberdeen Business School, where he has been since 2002. He holds a BSc from Glasgow University and MSc and PhD from Nottingham University. His academic work spans Operational Research, Management Science, and Financial Mathematics. His educational background includes: BSc, University of Glasgow MSc, University of Nottingham PhD, University of Nottingham John's research focuses on applying mathematical, statistical, and computational techniques to management and finance. Key areas include Optimisation, Data Envelopment Analysis, Financial Risk Modelling, and Simulation. He has developed methodologies for portfolio selection, risk measurement using Cornish-Fisher expansions, and efficiency analysis in investment funds. His work bridges theoretical development and practical application in finance and operations. His recent publications show a strong trend in Operations Research applied to financial performance and risk, particularly using DEA and stochastic frontier models. He also contributes to interdisciplinary areas such as tourism studies and nuclear data analysis. Scientific recognitions include: Fellow of the Higher Education Academy (HEA) Fellow of the Royal Statistical Society Member of the Operational Research Society John supervises PhD students in Business Management and teaches Operations Management, Business Modelling, and the Context of Business at undergraduate and postgraduate levels. He has collaborated extensively with researchers like K. Tee and M. Monville. While no formal lab is mentioned, his work involves advanced computational modelling and data analysis.

• Causal Analysis
• Statistics
• Econometrics
• +۵ مورد دیگر

Professor Martin Huber is a leading academic in Applied Econometrics and Policy Evaluation at the University of Fribourg within the Faculty of Management, Economics and Social Sciences . He also serves as a Research Associate at the Centre for European Economic Research (ZEW) and collaborates with institutions like Soda Labs (Monash Business School) . PhD in Economics and Finance (2010) Assistant Professor at University of St.Gallen (2010-2014) Research stays at Harvard University (2011/2012) and University of Sydney (2014, 2019) His research focuses on data-based causal analysis , machine learning , and policy evaluation in labor, health, and education economics. He has pioneered the integration of causal machine learning with traditional econometric methods, developing novel algorithms for bid-rigging detection and dynamic treatment effect analysis. Recent publications demonstrate methodological advancements in graph attention networks , doubly robust estimation , and causal mediation analysis . His work addresses practical challenges like sample selection bias , instrument validation , and high-dimensional data in policy evaluation. Huber actively engages in teaching through initiatives like the ZEW summer school and Fribourg Winter School , where he instructs on R , Python , and causal analysis tools . He maintains a strong online presence through @causalhuber.bsky.social and has published Impact Evaluation in Firms and Organizations (MIT Press, 2023).

Mélisande Albertمشاهده پروفایل

دانشیار

• Non-parametric tests of independence
• Resampling methods
• Point processes
• +۶ مورد دیگر

Mélisande Albert is an Associate Professor in Mathematics at INSA Toulouse, working in the Department of Mathematical Engineering and Modeling (GMM). She is also a member of the Toulouse Institute of Mathematics (IMT) at Paul Sabatier University (UPS). Her educational background includes: PhD in Mathematics from University of Nice Sophia Antipolis (2015), with thesis titled "Tests of independence by bootstrap and permutation: an asymptotic and non-asymptotic study. Application to Neurosciences" Master's degree in Mathematics, specialization Research (2012) Admission to the External Agrégation of Mathematics (2011, rank: 47/288) Bachelor's degree in Mathematics from University of Nice Sophia Antipolis (2009) Dr. Albert's research focuses on developing and analyzing statistical methods for independence testing and resampling techniques. Her work bridges theoretical statistics with practical applications, particularly in neuroscience. She investigates non-parametric tests of independence using bootstrap and permutation approaches, studies concentration inequalities for randomly permuted sums, and applies these methods to analyze spike train data in neuroscience to detect neural synchrony. Her research spans from theoretical foundations to practical implementations, with contributions to both statistical theory and neuroscience applications. Her scientific achievements include: Marie-Jeanne Laurent-Duhamel Award from the French Society of Statistics (SFdS) for her doctoral thesis Dr. Albert serves as a reviewer for the Journal of Machine Learning Research and Computation Statistics and Data Analysis. She has supervised student projects at INSA Toulouse and has been involved in the ANR Project SansSouci (2018-2020). She is an elected member of the French Statistical Society Mathematical Statistics group committee. She is an active member of the Statistics and Optimization team and the Mathematics and Biology research group at the Institute of Mathematics of Toulouse (IMT). She has co-organized several scientific events including the weekly Statistical Seminar at IMT (2018-2022), the Journées de Statistique Mathématique at the Henri Poincaré Institute in Paris (2018), and the "Post-selection inference and multiple testing" Workshop in Toulouse (2018).

Himali Kalanchigeمشاهده پروفایل

مدرس

• Statistics
• Bootstrap

Himali Kalanchige is a Lecturer in the Department of Mathematics and Statistics at the University of Vermont, affiliated with the College of Engineering and Mathematical Sciences. She teaches courses such as STAT 1410 - Basic Statistical Methods . Education PhD MS BSc Research Interests Her areas of expertise include Statistics and the Bootstrap method, a resampling technique widely used in statistical analysis.

• Machine Learning
• Statistics
• Actuarial Science
• +۳ مورد دیگر

Dr. Adrian O'Hagan is a Lecturer in Statistics and Actuarial Science at University College Dublin (UCD) School of Mathematics and Statistics, with a focus on machine learning and statistical modeling. He completed his PhD in Statistics at UCD in 2012 and holds an MSc in Statistics from UCD (2006). As a funded investigator at the Insight Centre, his work bridges computational statistics with practical applications in insurance, healthcare, and manufacturing. Education: PhD Statistics, UCD (2012) MSc Statistics, UCD (2006) Bachelor of Actuarial and Financial Studies, UCD Research Interests span machine learning, Bayesian inference, network analysis, and actuarial science. His recent work includes: Developing probabilistic models for network analysis and EHR-based phenotyping Advancing clustering algorithms with uncertainty quantification and optimization Applying statistical methods to insurance risk, manufacturing analytics, and data compression Publications highlight his expertise in variational Bayes, Gaussian mixtures, and multivariate claim modeling, with interdisciplinary impacts in healthcare and finance. He collaborates on real-world data analysis and contributes to open-source actuarial tools like mvClaim .

• Multiple testing / false discovery rate
• Asymptotic statistics / large deviations theory
• Theory of nonparametric tests
• +۳ مورد دیگر

Thorsten Dickhaus is a Professor of Mathematical Statistics at the University of Bremen , affiliated with Faculty 3 - Mathematics and the Institute for Statistics . His research focuses on multiple testing procedures, asymptotic statistics, nonparametric methods, and computational applications in life sciences. He holds a Ph.D. in Mathematics from Heinrich-Heine-University Düsseldorf (2008) and has held academic positions at Humboldt University Berlin and the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) Berlin before joining the University of Bremen in 2015. Key professional roles include: Principal Investigator in the Research Training Group (RTG) 2224 π³ (Parameter Identification) Principal Investigator in the Helmholtz School for Marine Data Science (MarDATA) Editorial roles for journals like Journal of Statistical Planning and Inference and Statistics (Berlin) Research projects include funded initiatives on multiple testing methodologies, neuroeconomics, and statistical inference in behavioral genetics. His Erdős number is 3, and he actively contributes to academic service through conference organization and editorial work.

• Econometrics
• Time Series Analysis
• Statistical Learning
• +۶ مورد دیگر

Karsten Reichold is an Assistant Professor at TU Wien, affiliated with the Institute of Statistics and Mathematical Methods in Economics within the Faculty of Mathematics and Geoinformation. His research bridges econometrics, time series analysis, and statistical learning, with a focus on robust inference in cointegrating regressions and forecasting applications in macroeconomics. His research interests include: Econometrics and time series modeling Statistical learning methods in economics Bootstrap and resampling techniques Empirical macroeconomic forecasting Stochastic processes and cointegration Panel data and polynomial cointegration The recent publication trend indicates a strong methodological focus on bootstrap inference, particularly self-normalized test statistics in cointegrating regressions, with implementation provided through open-source MATLAB code. His work emphasizes practical, ready-to-use econometric tools for robust statistical inference. Karsten Reichold has not been mentioned as receiving any scientific awards in the provided material. He is actively involved in teaching, offering courses such as Selected Topics in Econometrics, Stationary Processes and Time Series Analysis, and Introduction to Stochastic Processes. No information is available regarding student advisement or external research grants. He contributes to the academic community by sharing reproducible research code on GitHub. He leads and maintains research software repositories related to cointegration and panel FM-OLS estimation, promoting open science and computational reproducibility in econometrics.

Eugen Pircalabeluمشاهده پروفایل

مدرس

• High-dimensional statistics
• Probabilistic graphical models
• Social network models
• +۶ مورد دیگر

Eugen Pircalabelu is a Lecturer at UCLouvain (Université catholique de Louvain) working at the Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA) within LIDAM at the Faculty of Science. Prior to his current position, he held a Visiting Professor position at Ghent University and a Postdoctoral position at KU Leuven. Dr. Pircalabelu's research focuses on high-dimensional statistics, with particular emphasis on probabilistic graphical models, social network analysis, copula models, and information criteria. His work bridges theoretical statistics with practical applications in fields such as neuroscience (fMRI data analysis) and epidemiology (COVID-19 modeling). His recent publications reveal a strong trend toward distributed and federated learning approaches for high-dimensional graphical models, with applications spanning from financial time series to brain connectivity networks. His methodological contributions include innovations in model selection criteria, sparse estimation techniques, and time-varying network models that have advanced the field of high-dimensional statistical inference. Dr. Pircalabelu actively supervises multiple PhD students including Mengxue Li, Lise Léonard, and Lara Wautier, and has recently guided Ensiyeh Nezakati and Alexandre Jacquemain to completion of their doctoral studies. He teaches courses in nonparametric statistics, numerical methods for statistics, and statistical learning. He serves the statistical community through organizing regular Statistical/Econometrics seminars at ISBA and contributing to the RShiny@UCLouvain platform for open educational resources. Notably, during the COVID-19 pandemic, he developed a SHINY app for predicting hospitalizations and ICU admissions in Belgium.

• Data Analytics
• Statistics
• Regression Analysis
• +۳ مورد دیگر

Dr. Mansi Khurana is a Lecturer in the Department of Computing at Sheffield Hallam University, within the College of Business, Technology and Engineering. With over 12 years of teaching and research experience, she specializes in Data Analytics, Statistics, and Regression Analysis. She teaches modules such as Data Management Project and Tools & Techniques for Data Analytics, and has conducted workshops on topics like R programming and LATEX. Her research focuses on statistical methodologies including resampling techniques, ridge regression estimation, and bias reduction in linear models. She has published in journals such as Communications in Statistics and Far East Journal of Mathematical Sciences. Notable awards include the Best Graduate Poster Award (2011) and a Canada Commonwealth Scholarship (2011). Dr. Khurana has also worked as a Research Assistant at Concordia University, Montreal (2013), and has contributed to international conferences including STATISTICS 2011 in Canada.

David Kaplanمشاهده پروفایل

دانشیار

• Econometrics
• Quantile Regression
• Statistical Inference
• +۳ مورد دیگر

David Kaplan is an Associate Professor and Director of Doctoral Studies in the Department of Economics at the University of Missouri, within the College of Arts and Science. His research focuses on Econometrics, particularly in quantile regression, statistical inference, and policy analysis. He holds a Ph.D. (details unspecified) and has advised notable students including Qian Wu, Wei Zhao, Xin Liu, and Longhao Zhuo. His work bridges theoretical econometrics and applied policy evaluation, with a strong emphasis on methodological rigor and real-world applications. Key research themes include auction theory, ordinal data analysis, and health economics. His recent publications explore topics such as consensus ranking of distributions and the impact of robust norming in clinical classification. He has contributed to high-impact journals like the Journal of Econometrics and the Journal of Business & Economic Statistics. Dr. Kaplan’s academic leadership includes directing doctoral studies, ensuring rigorous training in econometric theory and applied methods. His work frequently involves collaborations and methodological innovations, addressing challenges in policy evaluation and statistical modeling. He maintains an active presence in academic communities through Google Scholar and institutional affiliations.

Margherita Gerolimettoمشاهده پروفایل

استاد

• Economic Statistics
• Time Series Analysis
• Regional Economics
• +۲ مورد دیگر

Margherita Gerolimetto is a Full Professor at Ca' Foscari University of Venice's Department of Economics, where she holds multiple leadership roles including Department Committee Member, Communications Delegate, and Third Mission Activities Delegate. Her research centers on advanced statistical methodologies applied to economic systems, with particular focus on time series analysis and regional economic dynamics. Research Focus: Professor Gerolimetto's work integrates econometric innovation with regional economic analysis: Development of bootstrap methods for economic time series Spatial analysis of regional economic convergence Urban institutional structure and performance metrics Statistical modeling of migration and labor markets Publication Trends: Her recent scholarship demonstrates: Innovations in bootstrap methodologies for forecasting Applications to environmental and health economics Analysis of urban governance structures Spatio-temporal modeling of economic phenomena

• Probability
• Statistics
• Biostatistics
• +۳ مورد دیگر

Bee Leng Lee is a Professor in the Department of Mathematics and Statistics at San José State University. She holds a Ph.D. and M.S. in Statistics from the University of Wisconsin-Madison and a B.Soc.Sci. in Statistics from the National University of Singapore. Her research focuses on probability, statistics, and biostatistics, with a strong emphasis on Bayesian methods, clinical trial design, and survival analysis. She has contributed to high-impact works such as dose-finding algorithms for phase I trials and applications of statistical methods in resolving legal disputes. Dr. Lee teaches a wide range of courses including Applied Probability and Statistics, Bayesian Data Analysis, Survival Analysis, and Computational Statistics. She has advised numerous student projects, including studies on stepwise variable selection limitations, generalized linear mixed models for categorical data, and Monte Carlo methods for financial derivatives. Her teaching also extends to foundational courses like Calculus I and Finite Mathematics. Her research projects include collaborations with industry sponsors like IBM (Almaden Research Center) on simulations and metamodeling. She actively publishes in top journals, with recent focus on balancing effective sample sizes in Bayesian designs and resolving contract disputes via statistical experimental design. Her work bridges theoretical statistics with practical applications in medicine, engineering, and public policy. Dr. Lee's contributions to statistical education and clinical trial methodology have positioned her as a key figure in advancing data-driven solutions at both academic and applied levels. She mentors students in cutting-edge statistical programming (R) and oversees special study projects that tackle real-world data challenges.

Soumendra Lahiriمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Machine Learning
• Resampling Methods
• +۵ مورد دیگر

Soumendra Lahiri is a Professor and Stanley A. Sawyer Professor in Mathematics and Statistics at Washington University in St. Louis's Department of Statistics and Data Science. He earned his PhD from Michigan State University in 1989 and has held faculty positions at Iowa State University, Texas A&M University, and North Carolina State University before joining WashU in 2019. His research spans theoretical and applied statistics with cross-disciplinary impact. Lahiri's research integrates higher-order asymptotic theory , resampling methods , and high-dimensional inference . He develops novel statistical methodologies for complex data structures in neuroscience, astrophysics, and econometrics. His recent work focuses on bootstrap techniques for modern data challenges like network analysis and machine learning model uncertainty. His publication portfolio shows a consistent focus on: Advancing resampling methods for dependent and high-dimensional data Developing theoretical guarantees for machine learning algorithms Creating inference tools for spatial, temporal, and network-structured data Bridging statistical theory with applications in natural and social sciences Honors include the endowed Stanley A. Sawyer Professorship. He maintains an active research program with recent publications in statistical methodology and interdisciplinary applications, particularly in electoral modeling, network analysis, and extreme value theory.

Robert Lundeمشاهده پروفایل

استادیار

• Statistical Network Analysis
• Time Series
• Resampling Methods
• +۲ مورد دیگر

Robert Lunde is an Assistant Professor in the Department of Mathematics and Statistics at Washington University in St. Louis. He holds a PhD in Statistics and Data Science from Carnegie Mellon University and completed postdoctoral research at the University of Michigan and University of Texas. Lunde's research focuses on statistical inference for complex data structures including networks and time series. His expertise encompasses resampling methods, distribution-free inference, and high-dimensional statistics. Specific interests include conformal prediction for network data, validity of jackknife methods for graphs, and bootstrap techniques for streaming algorithms. His recent publications explore subsampling sparse graphons, bootstrap error analysis for Oja's algorithm, and theoretical foundations for network-assisted regression. Lunde teaches courses in mathematical statistics and probability theory, employing his research expertise in statistical learning and inference methods.

Henry Horng-Shing Luمشاهده پروفایل

استاد

• Statistics
• Image Science
• Bioinformatics
• +۵ مورد دیگر

Henry Horng-Shing Lu is a Distinguished Professor at the Institute of Statistics, National Yang Ming Chiao Tung University (NYCU), Taiwan. He holds adjunct roles at Cornell University and Taipei Veterans General Hospital. His research spans statistics, bioinformatics, medical imaging, and machine learning, with a focus on interdisciplinary applications in healthcare and engineering. Education: PhD in Statistics (Cornell University, 1994), M.S. Statistics (Cornell, 1990), B.S. Electrical Engineering (National Taiwan University, 1986). Research Interests: Statistical methods in medical imaging (e.g., MRI, PET), bioinformatics (gene networks, protein interactions), and machine learning for precision medicine. He develops algorithms for image segmentation, disease classification, and genomic data analysis. Awards: Elected ISI Member (2011), PFHEA (2020), Outstanding Research Award (2022), IEEE Senior Member (2022). He has served as Dean of Academic Affairs at NYCU and led initiatives in big data and AI. Labs: Director of the Lu Lab at NYCU, focusing on medical AI, statistical bioinformatics, and secure random number generation. Collaborates with Taipei Veterans Hospital on clinical imaging systems.