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Raghu PasupathyView profile

Professor

• Stochastic Optimization
• Simulation
• Uncertainty Quantification
• +4 more

Raghu Pasupathy is a Professor of Statistics at Purdue University, affiliated with the Department of Statistics within the College of Science. He holds a Ph.D. from Purdue University (2005) and a B.Tech from the Indian Institute of Technology, Chennai (1995). His research focuses on stochastic optimization, simulation methodologies, uncertainty quantification, empirical processes, and stochastic processes. He teaches courses such as Stochastic Processes (STAT/MATH 532) and has contributed to software tools like RA-Level for stochastic linear programs and PyMOSO for multiobjective simulation optimization. His work emphasizes bridging theoretical foundations with practical applications, including algorithm development for simulation optimization and statistical inference methods. Notable contributions include ASTRO-DF for derivative-free optimization and R-SPLINE for integer-valued problems. He has advised several PhD students, including Jingyuan Chen and Guy Feldman. Pasupathy's research also involves collaborations on computational solvers and experimental comparisons through platforms like SimOpt.

• Robust Statistics
• Bias Reduction Methods
• Estimation Theory
• +5 more

Nicola Lunardon is an Associate Professor of Statistics at Ca' Foscari University of Venice. He holds a PhD in Statistics from the University of Padua and has held academic positions at the University of Milan-Bicocca, University of Trieste, and others. His research focuses on robust statistics, bias reduction methods, pseudo-likelihoods, and asymptotic theory. He has contributed to advancements in estimation functions and computational statistical methodologies. Current Affiliation: Department of Environmental Sciences, Computer Science and Statistics Previous Roles: Associate Professor at University of Milan-Bicocca (2019–2023), Research Fellowships at Padua and Trieste Universities (2012–2016) Research Interests: Lunardon's work emphasizes improving inferential procedures through robust statistical techniques. He has developed methods to address bias in estimating functions, tackle incidental parameters, and enhance computational efficiency. His Bayesian non-parametric approaches have been applied to molecular systems analysis. Publications span methodological contributions in top journals like Biometrika , Journal of the Royal Statistical Society , and Biometrics . Themes include global sensitivity analysis, longitudinal data modeling, and small-sample GEE estimation. Notable Software: Co-developed the ROSE package for imbalanced learning in R

• Operational Research
• Management Science
• Optimisation
• +8 more

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
• +5 more

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 AlbertView profile

Associate Professor

• Non-parametric tests of independence
• Resampling methods
• Point processes
• +6 more

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 KalanchigeView profile

Lecturer

• 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
• +3 more

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 .

• Econometrics
• Time Series Analysis
• Statistical Learning
• +6 more

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.

Matúš MaciakView profile

Associate Professor

• nonparametric regression
• change-points
• structural breaks
• +5 more

Matúš Maciak is an Associate Professor in the Department of Probability and Mathematical Statistics at the Faculty of Mathematics and Physics, Charles University . His research spans nonparametric regression , change-point detection , and statistical modeling in financial and biological contexts . Key research areas: Nonparametric smoothing and robustness Structural break analysis Shape-constrained inference Sparsity and LASSO-based methods Bootstrapping dependent data Recent publications focus on exogenous market changes , longitudinal data modeling , and biological applications like diel movement patterns in fish. Technical reports include contributions to ecological assessment methods and intercalibration processes for water quality. Contact: Email: Matus.Maciak@mff.cuni.cz , maciak@karlin.mff.cuni.cz Office: K151, Sokolovská 83, Prague 8

• Statistical Experimental Design
• Screening Designs
• Supersaturated Designs
• +4 more

Anthony Cossari is a University Researcher in Statistics (SECS-S/01) at the Department of Economics, Statistics, and Finance "Giovanni Anania" (DESF) at the University of Calabria, where he has been employed since September 1, 2003. He holds a Laurea in Scienze Statistiche ed Attuariali from the University of Calabria (1995) with highest honors. Cossari is a member of the Italian Statistical Society (SIS) and the European Network for Business and Industrial Statistics (ENBIS). His research interests center on statistical experimental design methodologies: Screening designs for identifying significant factors in preliminary experimental phases Supersaturated designs for studying numerous factors with limited experimental runs Follow-up designs to enhance initial experimental analyses Robust designs for minimizing variability from environmental factors Cossari maintains active research collaborations across disciplines, particularly with medical researchers from the Catholic University of the Sacred Heart in Rome and mechanical engineering researchers from the University of Calabria. His publication record shows a transition from purely statistical experimental design research to increasingly collaborative medical research, especially in sepsis prognosis, alcoholic cardiomyopathy, and dental health in patients with alcohol use disorders. Recent publications (2021-2025) demonstrate strong interdisciplinary work while maintaining his core statistical expertise. His academic service includes membership on the Departmental Board (2004-2006) and the Scientific-Technical Committee of the Interdepartmental Library of Economic and Social Sciences "E. Tarantelli" (2007-present). He has served as thesis advisor for numerous undergraduate and graduate students across various statistical topics and was a member of the Doctoral Committee for the PhD program in "Economic History, Demography, Institutions and Society in Mediterranean Countries" (2007-2009).

Benjamin KedemView profile

Professor

• Time Series Analysis
• Spatial Statistics
• Spectral Analysis
• +5 more

Benjamin Kedem is a Professor in the Department of Mathematics at the University of Maryland, College Park, with affiliations at the Institute for Systems Research (ISR). His academic career spans several decades with significant contributions to time series analysis, spatial statistics, and statistical methodology. Dr. Kedem's research focuses on time series analysis, space-time statistical problems, and combination of information from multiple sources. His work includes significant contributions to higher order crossings (HOC), contraction mapping methods in spectral analysis, Rice formula applications, threshold methods for rainfall estimation, partial likelihood approaches, Bayesian-transformed-Gaussian spatial prediction, and statistical data fusion. His research has practical applications in environmental statistics, meteorology, and public health. His recent publications demonstrate a continued focus on semiparametric methods, statistical data fusion, and computational approaches to time series and spatial analysis. The 15 most recent articles span from 2017 back to 1994, showing both contemporary relevance and foundational contributions to the field of statistics. Dr. Kedem has directed 15 PhD dissertations, with students completing between 1983 and 2007, indicating his long-standing commitment to mentoring the next generation of statisticians. His former students include George Reed, Donald E.K. Martin, Silvia R.C. Lopes, Haralabos Pavlopoulos, James Troendle, Ta-Hsin Li, John Barnett, Konstantinos Fokianos, Victor De Oliveira, Neal Jeffries, Boris Kozintsev, Richard Gagnon, Haiming Guo, Guanhua Lu, and Shihua Wen. He has authored or co-authored several books including 'Regression Models for Time Series Analysis' (2002), 'Time Series Analysis by Higher Order Crossings' (1994), and 'Statistical Data Fusion' (2017). His teaching portfolio includes graduate courses such as STAT 730 (Time Series Analysis), STAT 740 (Linear Models I), and STAT 741 (Linear Models II).

• Multiple testing / false discovery rate
• Asymptotic statistics / large deviations theory
• Theory of nonparametric tests
• +3 more

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.

Eugen PircalabeluView profile

Lecturer

• High-dimensional statistics
• Probabilistic graphical models
• Social network models
• +6 more

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
• +3 more

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 KaplanView profile

Associate Professor

• Econometrics
• Quantile Regression
• Statistical Inference
• +3 more

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.