Find Academic Supervisors

• Phenotypic Evolution
• Morphometrics
• Genomics
• +4 more

Carmelo Fruciano is an Associate Professor of Zoology at the University of Catania, Italy. His research focuses on phenotypic evolution, morphometrics, and evolutionary biology, particularly in fish systems. He holds a PhD in Evolutionary Biology from the University of Catania (2010) and has held appointments at institutions including the University of Konstanz, Queensland University of Technology, and the Ecole Normale Supérieure in Paris. His work combines genomics, transcriptomics, and geometric morphometrics to study adaptation and speciation. Fruciano leads a lab focused on morphometric methods and has developed R packages like GeometricMorphometricsMix and resampleWGCNA . He has received awards including the Marie Curie Fellowship (2012) and the Accademia Gioenia dissertation prize (2013). Research Interests: Phenotypic evolution and integration Sympatric speciation mechanisms Geometric morphometrics methodology Cichlid fish adaptation Phenotypic plasticity Awards: Doctoral dissertation prize (Accademia Gioenia, 2013) Marie Curie Intra-European Fellowship (2012) ARC Discovery Project (2021) Labs/Teams: Fruciano Lab at the University of Catania Past collaborations with Axel Meyer’s lab (Konstanz) and Matt Phillips’ lab (Queensland)

Ivan KojadinovicView profile

Professor

• Multivariate Analysis
• Nonparametric Statistics
• Copulas
• +2 more

Ivan Kojadinovic is a Professor at the University of Pau and the Pays de l'Adour, specializing in multivariate analysis, nonparametric statistics, and copula modeling. His research develops statistical methods for environmental and financial applications. Previously he held positions at Polytech Nantes and the University of Auckland.

Saad MoutiView profile

Visiting Assistant Professor

• Statistics
• Causal Inference
• Financial Risk Management
• +5 more

Saad Mouti is a Visiting Assistant Professor at the Department of Statistics & Applied Probability, University of California, Santa Barbara, specializing in statistical modeling at the intersection of finance and public health. His academic work combines quantitative finance, causal inference, and epidemiological risk analysis. Education: PhD from Paris Dauphine University Research spans financial risk management and dietary impacts on metabolic diseases, with recent work on: Rough volatility models in financial markets Sustainable investing and drawdown analysis Causal pathways linking diet to cardiovascular risk Optimal hedging strategies for insurance liabilities The cross-disciplinary nature of his publications highlights methodological contributions to statistical finance and population health. Current work focuses on resampling techniques for two-point health time-series analysis.

• Statistics
• Data Science
• Machine Learning
• +3 more

Qian Zhao is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Their research focuses on statistical methodology, high-dimensional data analysis, and applications in computational biology. They are affiliated with the Lederle Graduate Research Tower (LGRT 1444) and actively contributes to interdisciplinary programs in data science and biostatistics. Dr. Zhao has been recognized for award-winning teaching and mentoring, including initiatives like near-peer mentoring in data science. Their work bridges theoretical statistics with practical applications in genomics and biomedical research. Education & Background: Details about educational background are not explicitly provided in the text, but their academic focus aligns with advanced statistical training typical for such roles. Research Interests: Dr. Zhao's research emphasizes developing statistical methods for high-dimensional data, with applications in genomics and computational biology. Their work often involves bootstrap methods, asymptotic theory, and interactive visualization tools for biological data analysis. Recent projects include exploratory gene ontology analysis and alignment techniques for cytometry data. Grants & Advising: Specific grants or advising records are not listed in the provided text. However, their involvement in mentoring programs and interdisciplinary initiatives suggests active engagement in student supervision and collaborative research projects. Labs/Teams: No specific lab or team affiliations are mentioned, but their research likely intersects with computational and biological science groups at UMass Amherst.

Marinho BertanhaView profile

Associate Professor

• Econometrics
• Causal Inference
• Applied Microeconomics
• +5 more

Marinho Bertanha is a tenured Associate Professor of Economics at the University of Notre Dame, serving as concurrent faculty in Statistics and a faculty fellow at the Kellogg Institute. His academic work bridges econometrics and applied microeconomics with significant contributions to causal inference methodologies. His educational background includes a Ph.D. in Economics from Stanford University (2015), an M.A. from Fundação Getúlio Vargas in Rio de Janeiro (2009), and a B.A. from Universidade de São Paulo (2006). Prior to his current position, he completed postdoctoral work at the University of Louvain and served as a visiting professor at the University of Chicago. Bertanha's research focuses on causal inference , resampling methods , and policy evaluation , with particular expertise in regression discontinuity designs, bunching estimation, and permutation testing. His methodological innovations address critical challenges in econometric identification and statistical inference, especially in contexts with strategic reporting and imperfect compliance. His publication record shows consistent high-impact output in top journals including the Journal of the American Statistical Association , Journal of Econometrics , and Review of Economics and Statistics . Recent work demonstrates increasing focus on practical implementation through software development (Stata and MATLAB packages) that has become standard in empirical economics research. Through his concurrent appointment in Statistics and faculty fellowship at the Kellogg Institute, Bertanha contributes to interdisciplinary research initiatives focusing on economic policy evaluation and statistical methodology development. His current work-in-progress examines causal effects in matching mechanisms, returns to education with strategic reporting, and tax elasticity estimation across multiple countries.

• Real Estate Economics
• Urban Economics
• Environmental Economics
• +3 more

Hans Isakson is a Professor of Economics at the University of Northern Iowa, holding this position since 1990. His academic career spans over three decades, initially in the Department of Finance (1990-2001) before transitioning to the Economics Department (2002–present). His work focuses on real estate economics, environmental impacts on property values, and quantitative methodologies in valuation. He has contributed extensively to the understanding of housing market dynamics, automated valuation models (AVMs), and the interplay between environmental hazards and real estate markets. Research interests include spatial econometrics, public policy implications of real estate practices, and business ethics education. Notable contributions address topics like contamination effects on housing values, school closure impacts, and agricultural CAFOs' rural property valuations. Over 50 publications demonstrate his expertise in real estate analytics, environmental economics, and urban development. No scientific awards are listed in the provided records. His career includes roles such as Director of the Real Estate Education Program, reflecting leadership in academic program development. Though no formal grants or advising roles are noted, his publications indicate sustained research engagement. No lab affiliations or teams are explicitly mentioned in the source materials.

Keerthan PoologanathanView profile

Associate Professor

• Steel Structures
• Thin-Walled Structures
• Structural Optimization
• +7 more

Dr. Keerthan Poologanathan is an Associate Professor and Head of Structural Engineering at Northumbria University. His expertise spans steel and aluminum structures, fire safety, modular construction, and advanced numerical modeling. He leads over 30 PhD students and has secured grants from Innovate UK, EU, and industry partners. Education: PhD in Engineering (2010–present). Research focuses on cold-formed steel beams, modular building systems, lightweight concrete, and fire performance of structures. Key areas include web crippling behavior, composite materials, and sustainable construction. Awards include multiple Vice-Chancellor’s Performance Awards and the Holcim Award. Grants: ERDF IIIP projects, KTP collaborations with ESS Modular Construction and Redbrooks. Supervised 12 PhD students on topics like modular bracing, stainless steel beams, and FRP strengthening. Professional activities include membership in Eurocode committees (Aluminium/Steel) and editorial roles in journals like Structures and Fire Safety Journal .

Charles J GeyerView profile

Professor

• Statistics
• Maximum Likelihood Estimation
• Exponential Families
• +7 more

Charles J Geyer is a Professor in the Department of Statistics at the University of Minnesota, Twin Cities, within the College of Science and Engineering. He has been an active researcher since at least 1988, with a sustained record of scholarly output in statistical theory and methodology. His research focuses on advanced statistical methods including maximum likelihood estimation, exponential families, Markov Chain Monte Carlo (MCMC), likelihood-free inference, and aster models. These methods are applied in interdisciplinary contexts such as evolutionary biology, genetics, and ecological modeling, particularly in life history analysis and phenotypic selection. His work bridges theoretical statistics with practical computational tools for complex data. The recent publications highlight a strong trend toward computationally efficient inference, especially in models where traditional maximum likelihood fails. He has contributed to the development of the R package glmm for generalized linear mixed models and has worked extensively on envelope methods and variance reduction techniques. His research outputs include numerous peer-reviewed articles, book chapters, and publicly shared datasets, reflecting a commitment to open science. Scientific contributions include: Development of MCMC methods for dependent data Foundational work on likelihood inference when MLE does not exist Integration of aster models with envelope methodology Applications in evolutionary and ecological statistics He has collaborated with researchers such as D. J. Eck, R. G. Shaw, and R. D. Cook. While formal advisee relationships are not listed, his collaborative work suggests mentorship and academic leadership. He has not received any explicitly mentioned awards in the provided text, but his sustained impact is evident through citations and methodological influence. His datasets are archived in the University of Minnesota Data Repository, supporting reproducible research.

Emil Alstrup JensenView profile

Researcher

• Raman Spectroscopy
• Flow Cytometry
• Biomedical Engineering
• +4 more

Emil Alstrup Jensen is a PhD student at the Department of Applied Mathematics and Computer Science , Technical University of Denmark. His research focuses on integrating Raman spectroscopy , machine learning , and microfluidic platforms for biomedical applications, including blood typing and flow cytometry innovations. Current projects include Raman spectroscopy and Multi-Modal Machine Learning Models (2024–2027), supervised by L. K. H. Clemmensen, A. Kristensen, and L. E. Pedersen. Collaborations span microfluidics , biomedical engineering , and applied data analysis . His publications highlight advancements in high-throughput Raman spectroscopy , label-free blood analysis , and viscoelastic fluid-based cytometry , often utilizing optical fiber coupling and particle suspension dynamics to improve diagnostic accuracy and efficiency. Project details: Raman spectroscopy and Multi-Modal Machine Learning Models (Active PhD project, 2024–2027) Supervisors: Lars Kai Hansen (Main Supervisor) Anders Kristensen Lars Erik Pedersen Research trends: Combining Raman spectroscopy with artificial intelligence for clinical diagnostics Designing microfluidic platforms for capillary flow cytometry using viscoelastic fluids Optimizing non-transparent solution analysis via flow cell engineering Contact: ealje@dtu.dk

Denis ChetverikovView profile

Professor

• Econometrics
• High-Dimensional Models
• Empirical Process Theory
• +5 more

Denis Chetverikov is a Professor of Economics at the University of California, Los Angeles (UCLA). His research focuses on econometric theory, with emphasis on high-dimensional models, empirical process theory, bootstrap methods, and applications to asset pricing and policy analysis. He has published in top journals such as Econometrica , Review of Economic Studies , and Annals of Statistics . Education: PhD from the Massachusetts Institute of Technology (MIT). His work bridges theoretical econometrics and computational methods, addressing challenges in modern data analysis. Key contributions include advancements in nonparametric estimation, rank-based inference, and regularization techniques for high-dimensional datasets. Research trends in his articles emphasize methodological innovations for handling complex economic data structures, including factor models, quantile regression, and robust inference frameworks. He has developed statistical software tools like the csranks R package for rank-based analysis. His grants and lab affiliations (if any) are not explicitly detailed in the provided text.

• Statistics
• Regression Analysis
• Bootstrap Methods
• +3 more

William Cyrus Navidi is a Professor in the Department of Applied Mathematics and Statistics at Colorado School of Mines. He earned his Ph.D. in Statistics from the University of California, Berkeley in 1986 under Jacob Freedman with a dissertation titled "Edgeworth Expansions for Bootstrapping Regression Models". His research spans theoretical and applied statistics: Regression Analysis Bootstrap Methods Edgeworth Expansions Asymptotic Theory Statistical Education Dr. Navidi's work contributes to statistical inference foundations and has influenced statistical pedagogy through textbook authorship. His methodological research focuses on improving approximation techniques for regression models using resampling methods. Professional contact: wnavidi@mines.edu

Merrill LiechtyView profile

Clinical Professor

• Bayesian Statistics
• Portfolio Selection
• Higher Moment Estimation
• +5 more

Merrill Liechty is a Clinical Professor in the Decision Sciences and MIS department at Drexel University's LeBow College of Business . While his primary responsibility involves teaching statistics, his research emphasizes Bayesian statistics applied to portfolio selection, higher moment estimation, and social interaction analysis using functional Near-Infrared Spectroscopy (fNIRS). Research interests span Bayesian statistical modeling, financial econometrics, and computational methods for multivariate analysis. He has also pioneered studies on face-to-face human communication through non-obfuscated fNIRS data, co-founding Brytfish to translate these findings into practical conversation skill training. Publication trends reveal expertise in Bayesian portfolio optimization, MCMC algorithms, and statistical modeling for finance and supply chain reliability. His work frequently integrates theoretical rigor with real-world financial applications. 2016 : Fellow of the Institute of Strategic Leadership (LeBow College of Business) 2015 : Fellow of the Institute for Strategic Leadership 2004-2005 : Mini-Grant recipient (LeBow College Center of Teaching Excellence) Liechty has also served as a statistical consultant at Duane Morris LLP and Zeichner Ellman & Krause LLP, and previously held a corporate role as Chief Data Officer at Old Dominion Racing.

Laurent DonzéView profile

Professor

• Applied Statistics
• Econometrics
• Survey Methodology
• +7 more

Laurent Donzé is a Professor of Applied Statistics and Modelling at the Department of Informatics, Faculty of Economics and Social Sciences, University of Fribourg. He is also a Research Professor at KOF ETH Zurich and a Professor of Econometrics at the University of Neuchâtel. He leads the ASAM research group and has extensive experience in teaching mathematics, econometrics, and statistics. His educational background includes a Ph.D. in Econometrics from the University of Fribourg, followed by research roles at IRE and KOF ETH Zurich. His academic journey reflects deep engagement with statistical methodology and applied economic research. Donzé's primary research interests lie in applied statistics, particularly survey methodology, fuzzy statistics, imputation, causal inference, matching techniques, and wage discrimination analysis . He has made significant contributions to the development and application of fuzzy statistical tools, especially in defuzzification and fuzzy regression. His recent publications (2019–2025) demonstrate a consistent focus on fuzzy confidence intervals, fuzzy p-values, fuzzy ANOVA, and fuzzy regression models , often applied to real-world datasets like SHARE and Swiss SILC. These works emphasize robust statistical inference under uncertainty and contribute to both theoretical and applied advancements in fuzzy statistics. Among his scientific recognitions is the Best Student Paper Award at IJJCI 2020 . He has also edited special issues and contributed to leading journals and conferences in computational intelligence and fuzzy systems. Donzé has been involved in numerous research and teaching initiatives, including grants from the Swiss National Science Foundation, mentoring at the Swiss Study Foundation, and leadership in statistical societies. He has supervised research projects and collaborated with institutions such as Nestlé, the Swiss Federal Statistical Office, and pharmaSuisse. He is actively involved in academic and professional communities, serving as president of the Education and Research section of the Swiss Statistical Society and contributing to the development of statistical infrastructure in social sciences.

Bartosz KołodziejekView profile

Associate Professor

• Probability Theory
• Statistics
• Stochastic Fixed Point Equations
• +7 more

Bartosz Kołodziejek is an Associate Professor at the Faculty of Mathematics and Information Science, Warsaw University of Technology. His research lies at the intersection of probability theory and mathematical statistics, with a focus on stochastic fixed point equations, free probability, and graphical models. He actively publishes in top-tier journals and maintains a strong collaborative network. His research interests include: Stochastic fixed point equations and their applications Characterizations of probability distributions on symmetric and homogeneous cones Free probability and free convolution Graphical models and high-dimensional statistics Symmetry in Gaussian models and model selection Perpetuities and tail behavior of stochastic recursions The analysis of his recent publications reveals a consistent trend in theoretical probability and statistical methodology, particularly in high-dimensional inference, random matrix theory, and invariant models. His work often involves deep analytical techniques and connections across fields such as convex analysis, exponential families, and stochastic processes. He has no listed scientific awards on his homepage. While no students or grants are explicitly mentioned, his active publication record and software contributions (such as the gips R package) suggest ongoing research leadership. He collaborates with prominent researchers in probability and statistics across Europe. He is involved in the development of statistical software, notably contributing to the theoretical foundations of the gips package for Gaussian models with permutation symmetry, used in high-dimensional data analysis.

Andreas KurzView profile

Researcher

• Psychological Diagnostics
• Psychometrics
• Statistical Inference
• +6 more

Andreas Kurz is a Senior Scientist at the Department of Psychology, University of Salzburg, where he conducts research in psychological diagnostics and psychometric modeling. His work focuses on statistical inference in small sample contexts and conditional likelihood methods, particularly in educational assessment and admission procedures. Position: Senior Scientist Institution: University of Salzburg Department: Department of Psychology Research Group: Admission Procedure for Teaching Professions in the Central Cluster Andreas Kurz holds a Master of Science in Psychology from UMIT TIROL (2021) and a Diploma in Civil Engineering (equivalent to MSc ETH) from ETH Zurich (1995). His academic journey includes roles as a Student Assistant at UMIT TIROL (2019–2022) and a Scientific Assistant at ETH Zurich (2007–2009). His research lies at the intersection of psychology and statistics, with a strong emphasis on methodological rigor in psychometric testing. He develops and applies advanced statistical techniques for small sample inference, particularly using resampling and conditional likelihood frameworks. His work supports valid and reliable assessment in educational and psychological settings. The publications and software tools developed by Andreas Kurz reflect a consistent trend in advancing statistical methodology for psychometrics. His contributions include peer-reviewed articles on the gradient test and conditional inference, as well as R packages like tcl and tclboot that implement these methods for practical use in research and assessment. Andreas Kurz has actively participated in international conferences such as IMPS 2021 and IMPS 2022, presenting on statistical testing in conditional likelihood frameworks. His work is supported by research in psychometrics and statistical computing, with no explicit mention of external grants or advising roles. He is involved in the development of admission procedures for teaching professions, indicating applied research with societal impact in education. His technical expertise bridges civil engineering, psychology, and statistical programming, contributing to robust assessment systems.