Find Academic Supervisors

• Statistical Inference
• Machine Learning
• Bioinformatics
• +3 more

Lucy Gao is an Assistant Professor in the Department of Statistics at the University of British Columbia (UBC). Her research focuses on statistical inference, machine learning, and bioinformatics, with particular emphasis on clustering methods, high-dimensional data analysis, and optimal design theory. She holds a faculty position within UBC’s Faculty of Science and is affiliated with the Vancouver Campus. Her work spans theoretical developments in statistical methodologies and applications to biological data, such as single-cell RNA sequencing analysis. She has contributed to areas like selective inference for hierarchical clustering and algorithmic optimization for complex systems. Her email is lucy.gao@stat.ubc.ca, and she maintains a website at https://www.lucylgao.com/ . Recent publications highlight her expertise in data thinning techniques, latent variable modeling, and multiview network analysis. She has explored topics ranging from negative binomial count splitting in genomics to bilevel optimization algorithms. Her research bridges statistical theory and practical computational challenges in modern data science. No scientific awards or advising records are explicitly listed in the provided materials. Her academic contributions are primarily through peer-reviewed articles and methodological innovations.

Sven KlaaßenView profile

Research Fellow

• Machine Learning
• Causal Inference
• Deep Learning
• +1 more

Dr. Sven Klaaßen serves as a Research Fellow at the University of Hamburg's Hamburg Business School within the Professorship for Statistics with Application in Business Administration, collaborating closely with Prof. Dr. Martin Spindler since 2021. His research focuses on developing advanced statistical methodologies for complex data environments. His academic credentials include: Ph.D. in Statistics from Hamburg Business School (2020) Visiting Scholar at MIT Department of Economics (2022) M.Sc. in Business Mathematics from University of Hamburg (2016) BSc in Business Mathematics from University of Hamburg (2014) Dr. Klaaßen's research program centers on Machine Learning, Causal Inference, Deep Learning, and High-Dimensional Statistics, with particular emphasis on developing robust inference techniques for modern data challenges. His work bridges theoretical statistics with practical applications in business analytics and econometrics, often addressing the complexities of high-dimensional datasets where traditional methods fail. Analysis of his recent publications reveals a clear trajectory toward integrating machine learning with causal inference frameworks, exemplified by his leadership in the DoubleML software ecosystem. His research increasingly tackles multimodal data challenges while maintaining rigorous statistical foundations, with applications spanning economics, operations research, and business decision systems. As an active member of Prof. Spindler's research group, Dr. Klaaßen contributes to collaborative projects developing open-source statistical tools and advancing methodological frontiers in causal machine learning. The team maintains strong industry and academic partnerships focused on translating theoretical innovations into practical analytical solutions.

Patrick J.F. GroenenView profile

Professor

• Data Science
• Multidimensional Scaling
• Optimization Algorithms
• +5 more

Patrick J.F. Groenen is a full Professor of Statistics at the Erasmus School of Economics (ESE), Erasmus University Rotterdam , and currently serves as its Dean. His academic career spans leadership roles, including Director of the Econometric Institute (2014-2020) and President of the International Association for Statistical Computing (2015-2017). He has held visiting positions at Stanford University and contributed extensively to multidimensional scaling (MDS), data science , and statistical genetics . His research focuses on numerical algorithms for MDS, support vector machines , and optimization techniques applied to diverse fields. He is a founding editor of the open-access journal Journal of Data Science, Statistics, and Visualisation and has authored major textbooks including Modern Multidimensional Scaling and Applied Multidimensional Scaling and Unfolding . His work appears in top journals like Nature Genetics , Psychometrika , and Journal of Machine Learning Research . Recent publications highlight advancements in convex clustering , genomic prediction , and robust statistical methods . He has developed influential R packages such as GenSVM , SVMMaj , and smacof for multiclass classification and MDS software. His supervisory record includes 14 PhD students across economic psychology, genetics, and data science. Academic activities include editorial roles at Advances in Data Analysis and Classification and Psychometrika , alongside organizing international conferences like CARME 2007 on correspondence analysis. His contributions to nonlinear biplots and response style modeling have advanced visual data interpretation in social sciences.

Matthew ThorpeView profile

Associate Professor

• Machine Learning
• Data Science
• Applied Mathematics
• +4 more

Matthew Thorpe is an Associate Professor in the Department of Statistics at the University of Warwick and a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). His research focuses on applying methods from applied analysis—including partial differential equations (PDEs), calculus of variations, and optimal transport—to machine learning and data science challenges. He has organized workshops such as the 'Machine Learning in Infinite Dimensions' at ETH Zurich and the 'LMS-Bath Symposium on Inverse Problems and Artificial Intelligence in Medicine.' Thorpe currently seeks PhD students for his research projects. His work bridges theoretical mathematics with practical applications in data-driven fields. Thorpe's research interests include manifold learning in Wasserstein space, PDE-based approaches to data science, and convergence analysis of graph-based learning algorithms. He has contributed to understanding the impact of imputation quality on machine learning models and developed novel transportation distances for pattern recognition. His interdisciplinary approach integrates mathematical rigor with advancements in artificial intelligence. He has co-organized the One World Seminar Series on the Mathematics of Machine Learning and remains active in promoting collaborative research initiatives. Despite no explicitly listed awards, his prolific publication record reflects sustained academic impact. Thorpe’s advising focuses on training students in the intersection of applied mathematics and modern data science techniques.

Joseph RomanoView profile

Professor

• Nonparametric Statistics
• Bootstrap Methods
• Multiple Testing
• +7 more

Joseph Romano is a Professor of Statistics and Economics at Stanford University, where he has served since 1986. He holds dual appointments in both the Department of Statistics and the Department of Economics. His research focuses on theoretical statistics, nonparametric methods, bootstrap techniques, multiple testing, and their applications in econometrics, climate science, genetics, and clinical trials. He has received numerous awards, including the LGBTQ+ Scientist of the Year (2021) and the Presidential Young Investigator Award (1989–1994). Education: Ph.D. in Statistics, University of California, Berkeley (1986) M.S. in Statistics, University of California, Berkeley (1983) B.A. in Statistics, Princeton University (1982), Summa Cum Laude Research interests include developing robust statistical methodologies for high-dimensional data, controlling errors in multiple testing, and applying resampling techniques to complex datasets. He has authored over 100 papers, focusing on topics like permutation tests, bootstrap methods, and econometric modeling. Notable works include advancements in fixed sequence testing and randomized inference for modern data challenges. Awards and Recognition: Fellow, Institute of Mathematical Statistics (2000) Fellow, International Association of Applied Econometrics (2020) Canadian Journal of Statistics Award (1989) Romano has advised numerous students, including doctoral candidates in statistics and economics. He has held administrative roles at Stanford, such as Associate Chair and member of faculty committees. His grants include NSF-funded projects on resampling methods, multiple testing, and econometric theory. He is a vocal advocate for LGBTQ+ visibility in academia and has contributed to initiatives like 500 Queer Scientists. Beyond academia, he enjoys music, tennis, cooking, and architecture.

• Sufficient Dimension Reduction
• Variable Selection
• High Dimensional Analysis
• +1 more

CHEN Xin is an Associate Professor in the Department of Statistics and Data Science at Southern University of Science and Technology (SUSTech), where he has been employed since January 2019. He initially joined the Department of Mathematics before transitioning to his current department in August 2019. Prior to SUSTech, he held academic positions as Assistant Professor at the National University of Singapore (2011-2019) and Syracuse University (2010-2011). Education History: PhD in Statistics from University of Minnesota, USA (2005-2010) MS in Statistics and Applied Probability from National University of Singapore (2001-2003) BS in Mathematics from Nankai University, China (1994-1999) Research focuses on developing statistical methodologies for high-dimensional and complex data analysis. Primary research domains include: sufficient dimension reduction techniques for efficient data representation; variable selection methods for high-dimensional models; robust statistical approaches for complex datasets; and optimization of computational efficiency in statistical estimation. His work consistently addresses theoretical foundations while developing practical implementations for modern data challenges. Publications demonstrate sustained focus on statistical methodology development, particularly in dimension reduction and high-dimensional analysis. Recent works emphasize robust methods for ultrahigh-dimensional data, efficient computation algorithms, and model diagnostics. Research consistently appears in top-tier statistics journals including Biometrika, Journal of the American Statistical Association, and Annals of Statistics. No scientific awards, student advising relationships, or research grants are mentioned in the source materials. Similarly, there is no information available regarding laboratory affiliations or research team composition.

Ewa SynówkaView profile

Researcher

• Fixed Point Theory
• Nonlinear Analysis
• Graph Theory
• +5 more

Ewa Synówka is a researcher at the Institute of Mathematics, University of Zielona Góra , Poland. Her academic work spans interdisciplinary applications of mathematics and computer science. Research focuses on iterative methods for fixed point problems in Hilbert spaces Contributions to graph theory, particularly coloring models and combinatorial geometry Investigations into nonlinear wave propagation and stochastic equations Applications of computer science in secure data transmission and supply chain privacy She teaches advanced statistical methods, econometric modeling, and multivariate data analysis. Her work also emphasizes modern pedagogy using open-source tools for primary/secondary school outreach.

Xiangfeng YangView profile

Associate Professor

• Large Deviations Theory
• Probability Theory
• Stochastic Processes
• +11 more

Xiangfeng Yang is an Associate Professor and Docent in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical probability and applied mathematical modeling, with a strong emphasis on large deviations theory and stochastic processes. His research interests include: Large Deviations Theory Random Matrices and Sample Covariance Matrices Stochastic Bridges (Bernstein and Markov) Longest Runs, Gaps, and Extremal Eigenvalues Hidden Markov and Reciprocal Processes Modern Multivariate Statistical Analysis Xiangfeng Yang's recent publications (2015–2024) reflect a consistent and deep engagement with probabilistic asymptotics and extreme behavior in stochastic systems. His work spans theoretical foundations in probability, applications in random matrix theory, and modeling in financial and environmental contexts. A notable trend is his focus on large deviation principles across diverse settings—ranging from eigenvalues of random matrices to gaps in Poisson processes and runs in Markov chains. He frequently collaborates with PhD students, indicating an active supervisory role. His scientific contributions are published in reputable journals such as Journal of Applied Probability , Stochastic Processes and their Applications , and Theory of Probability and Mathematical Statistics . While no explicit awards are listed, the quality and volume of his output suggest recognition within the mathematical community. Xiangfeng Yang has supervised several PhD students to completion, including Joseph Okello Omwonylee, Denise Uwamariya, and Stefane Saize, with research topics closely aligned with his expertise in large deviations and stochastic modeling. His research likely involves collaboration within the Applied Mathematics division, though no specific lab or team name is mentioned. He continues to contribute actively to modern theoretical and applied probability.

Stephen FranceView profile

Associate Professor

• Quantitative Marketing
• Data Mining
• Psychometrics
• +4 more

Stephen France is an Associate Professor of Quantitative Analysis at Mississippi State University's College of Business. His research focuses on integrating marketing, psychometrics, applied statistics, and data mining to solve complex industry problems. He leads the Psychmine research group, which develops analytical techniques for data interpretation in marketing and social media contexts. Education includes a Ph.D. in Quantitative Marketing and Supply Chain Management from Rutgers University (2008), an MS in Statistics and Management Science from the University of the West of England (2004), and a BS in Computer Science from the University of Durham (1997). Research interests span cluster analysis, visualization methodologies, cultural consensus analysis, sentiment analysis, and measurement techniques. His work emphasizes algorithmic efficiency and scalable solutions for modern data challenges. The Psychmine group maintains an active presence through GitHub, Google Scholar, and ResearchGate. Publications highlight innovations in data-driven marketing strategies, survey methodologies, and interdisciplinary collaborations between information systems and marketing. His software tools, such as FlexCCT and DEMScale, demonstrate practical applications in ratings analysis and customer data visualization.

• Quantitative Finance
• Portfolio Optimization
• Covariance Matrix Estimation
• +3 more

Gianluca De Nard serves as a Senior Research Associate at the University of Zurich's Department of Economics, Research Fellow at New York University's Volatility and Risk Institute, and Head of Quantitative Research at OLZ AG in Zurich. His career bridges academic research and practical finance applications, with significant contributions across multiple institutions. Dr. De Nard specializes in quantitative finance with expertise in portfolio optimization, covariance matrix estimation, and financial econometrics. His research focuses on developing robust statistical methods for handling large-dimensional financial data, with particular emphasis on shrinkage techniques and factor models. His work addresses fundamental challenges in modern portfolio theory, including nonstandard errors in financial research, climate risk integration, and AI applications in investment management. Analysis of his recent publications reveals a clear evolution toward integrating machine learning with traditional financial econometrics. His 2024-2025 work shows increasing sophistication in applying AI techniques to portfolio construction while maintaining rigorous statistical foundations. The recurring themes across his research include improving covariance matrix estimation, developing factor models for portfolio selection, and addressing methodological challenges in financial research. Dr. De Nard has established collaborations with leading researchers including Nobel laureate Robert F. Engle, Olivier Ledoit, and Michael Wolf. His work appears in top finance journals including the Journal of Finance, Journal of Financial Econometrics, and Journal of Banking and Finance, with his 2024 paper 'Nonstandard Errors' accumulating over 17,000 downloads. At OLZ AG, he applies his academic research to practical investment challenges as Head of Quantitative Research, demonstrating his ability to translate theoretical advances into real-world solutions. His dual academic-industry roles position him at the forefront of quantitative finance research and application.

Johan BraekenView profile

Professor

• Psychometrics
• Item Response Theory (IRT)
• Computerized Adaptive Testing (CAT)
• +3 more

Johan Braeken is a Professor at the University of Oslo's Centre for Educational Measurement (CEMO). He holds expertise in psychometric modeling, particularly in modern test design, including computerized adaptive testing (CAT) and item response theory (IRT). His research focuses on improving assessment methodologies in education and large-scale evaluations. Education: PhD in Psychology (Psychometrics) from K.U.Leuven, Belgium (2008). Previous roles include Associate Professor (2014–2017) and Assistant Professor positions at Wageningen University and Tilburg University in the Netherlands. He has also worked as a psychometrician at CITO (2008–2009). Research Interests: Development and application of latent variable models, CAT optimization, and evaluation of international educational assessments. He explores statistical methods for improving measurement precision and addressing model violations. Software Contributions: Creator of the 'Empirical Kaiser Criterion' app for factor analysis, 'Fixed-precision MCAT selection rules' for R's mirtCAT package, and an Item Characteristic Curve visualization tool. Labs/Groups: Active in the CREATE and FREMO research groups, focusing on educational measurement and frontier research in psychometrics.

• Urban Geography
• Regional Development
• Socio-Spatial Transformation
• +5 more

Kostyantyn Mezentsev is Professor and Head of the Department of Economic and Social Geography at Taras Shevchenko National University of Kyiv, Ukraine, having progressed from Assistant Professor (1997) to Professor (2007) before assuming departmental leadership in 2015. His career spans nearly three decades focused on Ukrainian urban and regional dynamics. His academic credentials include: MSc in Economic and Social Geography (1993, Taras Shevchenko National University of Kyiv) CSc (PhD) in Economic and Social Geography (1997, Taras Shevchenko National University of Kyiv) DSc in Economic and Social Geography (2006, Taras Shevchenko National University of Kyiv) Mezentsev's research centers on socio-spatial transformation in post-Soviet Ukraine, with expertise in urban modernization models, migration patterns, and regional environmental safety. He employs mixed-method approaches including GIS modeling, mental mapping, and multivariate statistical analysis, with current emphasis on participatory planning, gentrification processes, and symbolic dimensions of urban space. His 2012-2015 publications reveal persistent focus on Ukrainian urban systems, particularly Kyiv's metropolitan region, analyzing regional polarization through lenses of global crises, migration flows, and post-Soviet transition. International collaborations with German, Hungarian, and Eastern European institutions dominate his work, highlighting cross-border comparative frameworks for understanding spatial inequality. No scientific awards were documented in the source material. Mezentsev directs multiple funded projects including 'Urban Reconfiguration in post-Soviet Space' (Leibniz Community, Germany), 'Civil Society Involvement in Urban Development' (DAAD, Germany), and Visegrad Fund migration studies, demonstrating extensive grant acquisition capacity focused on Eastern European urban challenges. He leads the Urban and Regional Development Research group at Taras Shevchenko University, advancing spatial modeling techniques while investigating municipal GIS applications and cultural dimensions of urban space transformation.

Spiridon PenevView profile

Professor

• Wavelet Methods in Non-Parametric Curve Estimation
• Edgeworth Expansions and Saddlepoint Approximation Methods
• Structural Equation Models
• +4 more

Professor Spiridon Penev is a faculty member at the School of Mathematics & Statistics , University of New South Wales (UNSW), Sydney. After completing his PhD in Mathematical Statistics at Humboldt University, Berlin, he worked at Technical University of Sofia (Bulgaria) for 10 years, becoming Associate Professor in 1991. He joined UNSW in 1992, progressing from Lecturer to Professor in 2019. His teaching focuses on Statistical Inference , Multivariate Analysis , Longitudinal Data Analysis , and related advanced courses. Research Interests: Wavelet Methods in Non-Parametric Curve Estimation, Edgeworth Expansions, Saddlepoint Approximation, Structural Equation Models, Inference in Semiparametric Models, and Stochastic Risk Modelling. Article Trends: His work spans from foundational wavelet methods (pre-2010) to modern applications in climate model ensembles , portfolio optimization , marine engineering , and machine learning . Keywords include Statistics, Finance, Climate Science, Structural Health Monitoring, and Optimization. Awards: DAAD Award, Elected Member of the International Statistical Institute (ISI). Grants: Led ARC Discovery Project (2016–2018), ARC Linkage Project (2018–2022), and industry collaborations like SCA water quality analysis (2014–2019). Location: School of Mathematics and Statistics, UNSW Sydney, Room 1038, The Red Centre.

Vijayanarasimha Hindupur PakkaView profile

Senior Lecturer

• Electrical Power Systems Analysis
• Smart Grids
• Microgrid Operation and Control
• +25 more

Vijayanarasimha Hindupur Pakka is a Senior Lecturer in Electrical Power Systems & Smart Grids at De Montfort University, UK, based in the School of Engineering and Sustainable Development within the Faculty of Computing, Engineering and Media. He is affiliated with the Institute of Energy & Sustainable Development (IESD) and the Engineering & Physical Sciences Institute (EPSI), contributing to cutting-edge research in sustainable energy systems and smart grids. Education: PhD in Computer Science, University of Southampton, UK (2005–2009) MSc (Engg) in Electrical Engineering, Indian Institute of Science, Bangalore, India (2002–2004) B.E. in Electrical & Electronics Engineering, Bangalore University, India (1997–2001) PGCertHE, De Montfort University, UK (2016–2017) His research focuses on smart grids, distributed energy integration, microgrid control, voltage regulation, electricity markets, and agent-based modeling. He applies advanced techniques such as machine learning, multi-agent systems, statistical models, and metaheuristics to power system challenges. His teaching includes power system analysis, electrical principles, and power electronics at both undergraduate and postgraduate levels. His recent publications (2022–2024) reflect a strong trend toward intelligent optimization in distribution systems, local energy planning, water quality modeling, and real-time occupancy sensing for energy efficiency. These works span disciplines including electrical engineering, environmental science, and building automation, with a clear emphasis on sustainability, data-driven modeling, and smart infrastructure. Scientific Awards and Recognition: Higher Certificate in Statistics, Royal Statistical Society (2013) Fellow of the Higher Education Academy (FHEA, 2017) Reviewer for EPSRC, Netherlands Organization for Scientific Research, IET, MDPI Energies, and several international journals Vijay Pakka actively supervises MSc and PhD students and has led or contributed to major research grants such as CASCADE, AMEN (EPSRC), and the UKRI-funded CEPREC project. His consultancy includes power and statistical system analysis. He is involved in KTP projects with industry partners like Advanced Infrastructure Technology Ltd and Midas Productions Ltd, focusing on data-driven energy planning and demand optimization. He is a member of IEEE, IET, CIGRE, and the Energy Institute, and regularly presents at international conferences such as CIRED, EEM, and IEEE SmartGridComm. His work bridges technical power systems with socio-economic and behavioral aspects of energy transitions, positioning him at the forefront of interdisciplinary energy research.

Minge XieView profile

Professor

• Statistical Inference
• Data Science Foundations
• Meta-Analysis
• +6 more

Minge Xie is a Distinguished Professor and Director of the Office of Statistical Consulting at Rutgers, The State University of New Jersey . His research focuses on foundational aspects of statistical inference, data science, and interdisciplinary applications. He serves as a leader in the field of statistics, contributing to methodologies in meta-analysis, reproducibility, and computational inference. Education : Ph.D. from the University of Illinois at Urbana-Champaign Xie’s research spans statistical theory, computational methods, and practical applications in data science. He has pioneered approaches in confidence distributions , fusion learning , and high-dimensional modeling , with notable work on zero-inflated data, conformal prediction, and reproducibility frameworks. His recent publications emphasize the integration of Bayesian, frequentist, and fiducial paradigms, advancements in meta-analysis for rare events, and scalable methods for big data analysis. Xie’s work bridges theoretical rigor with real-world applications, including microbiome studies, nuclear detection, and maritime threat modeling. Scientific Awards : ASA Fellow IMS Fellow ISI Fellow Xie actively contributes to statistical education and interdisciplinary collaboration, leading the Office of Statistical Consulting at Rutgers. His methodological innovations continue to influence fields requiring robust and interpretable statistical inference.