Dr. Nicky van Foreest is an Associate Professor at the Faculty of Economics and Business , University of Groningen. His research bridges probability theory and optimization , focusing on applications in manufacturing, inventory, queueing, and service processes. His 15 most recent articles explore computational methods in probability, recursion, and entropy (e.g., Solving Wordle with Entropy ), geometric properties (e.g., Sagemath Proofs for Three Circles ), and stochastic system analysis (e.g., Memoryless Excursions ). He develops educational materials and open-source software, including contributions to stochastic_or and sicm_sagemath . Contact: n.d.van.foreest@rug.nl | University Profile | Personal Homepage .
Mao Xiaojie is an Associate Professor at the Department of Management Science and Engineering, Tsinghua University's School of Economics and Management . Holding a PhD in Statistics and Data Science from Cornell University (2021) and a bachelor's in Mathematical Economics and Finance from Wuhan University (2016), Mao specializes in causal inference and data-driven optimization decision-making . PhD: Cornell University (2016-2021) Bachelor: Wuhan University (2012-2016) Mao's research bridges machine learning , statistics , and operations research to address challenges in contextual optimization, algorithmic fairness, and robust causal inference. Recent work focuses on data combination , surrogate variables , and minimax methods for handling unobserved confounding and limited outcome data. Key trends in Mao's publications include: Advancing bandit algorithms for efficient contextual decision-making Developing debiased machine learning frameworks for quantile treatment effects Designing robust optimization models under noisy and incomplete covariates Scientific recognition includes: Applied Probability Society Best Student Paper Competition Finalist (2020) Multiple teaching excellence awards at Tsinghua University (2022-2024) Research grants from the National Natural Science Foundation of China Mao currently teaches Empirical Methods in Management Science (PhD), Data Analysis: Inference and Decision Making (Master), and Probability Theory and Mathematical Statistics (Undergraduate). Research collaborations span institutions like Cornell and MIT, with publications in top venues including NeurIPS , ICML , and Operations Research .
Dr. Anderson Alves is an Assistant Professor in Precision Livestock Science and Data Analytics at the University of Georgia's College of Agricultural & Environmental Sciences , Department of Animal & Dairy Science. Originally from Brazil, he earned his Master’s in Animal Science (2016) from the Federal University of Ceara and a Doctoral degree in Animal Breeding and Genetics (2019) from Sao Paulo State University. Education: Master’s (2016, Federal University of Ceara), Doctoral (2019, Sao Paulo State University) Prior Roles: Lecturer (Federal Institute of Education, Science, and Technology of Maranhao, Brazil) and Research Associate (University of Wisconsin-Madison) Dr. Alves' research integrates statistical learning , AI , and on-farm sensor data to create decision-making tools for sustainable livestock farming. His lab employs high-throughput phenotyping and modern molecular technologies to explore the genetic basis of hard-to-measure traits. Recent work includes applications of Siamese neural networks for cattle identification and machine learning for predicting animal health metrics. The Lab for Precision Livestock Science develops computational tools to improve animal production, reproduction, welfare, and health. Their projects span from automated pregnancy diagnosis using ultrasonography to anemia detection via ocular imaging. The lab’s GitHub and LinkedIn profiles are publicly accessible for collaboration and dissemination.
Rafael de Andrade Moral is a Professor of Statistics at Maynooth University's Faculty of Science & Engineering (since 2025), with prior roles as Associate Professor (2023-2025) and Assistant Professor (2018-2023). He holds a PhD in Statistics (University of São Paulo, 2014-2017) and dual bachelor's degrees in Biology and Education. His work bridges Statistical Ecology , Computational Biology , and Data Science , focusing on modeling ecological systems, agricultural pest dynamics, and biodiversity-ecosystem function relationships. Key research themes include Bayesian modeling , multivariate ecological forecasting , and machine learning applications . He founded the Theoretical and Statistical Ecology Research Group and serves on committees like the Young-ISA Chair . His recent articles span topics like insect abundance forecasting , weed-crop competition under climate change , and neuroinformatics-based learning analysis , reflecting interdisciplinary engagement. Scientific accolades include the Young Statistician Showcase Prize (2018), A-mu-sing Competition First Place (2021), and Maths Week Award (2022). He has advised three PhD students and contributed to over 50 peer-reviewed publications. Active in teaching innovation (e.g., Teaching Statistics through Music ), he also provides statistical consultancy to organizations like NIBIO and Jomakol .
Goncalo Abecasis is the Felix E. Moore Collegiate Professor of Biostatistics at the University of Michigan School of Public Health, where he leads the Center for Statistical Genetics. He received his DPhil in Human Genetics from the University of Oxford in 2001 and joined the University of Michigan faculty that same year. His research focuses on developing statistical and computational tools for identifying genetic variants associated with human disease and variation. His research interests include the development of analytical methods for mapping complex traits, utilization of linkage disequilibrium in gene mapping, quantitative trait analysis, and characterization of human genetic variation. He has pioneered computational approaches that enable the analysis of large genomic datasets generated by high-throughput technologies. His work addresses practical challenges in handling real-world genetic data, including genotype error detection and relationship verification. Abecasis has developed numerous widely-used software tools including Merlin (for pedigree analysis), MACH (for haplotype estimation), LAMP (for association testing), QTDT (for quantitative trait analysis), GOLD (for linkage disequilibrium visualization), and GRR (for relationship error detection). His publications span statistical genetics methodology, software development, and applications to diseases including diabetes, glaucoma, age-related macular degeneration, schizophrenia, and aging-related traits. His recent work involves large-scale genome-wide association studies with hundreds of thousands of participants, focusing on complex traits related to substance use, cardiovascular health, metabolic disorders, and eye diseases. The computational tools he develops are used in hundreds of gene-mapping projects worldwide. Abecasis collaborates extensively with researchers studying diabetes (Dr. Michael Boehnke), eye diseases (Drs. Julia Richard and Anand Swaroop), schizophrenia (Dr. Maria Karayiorgou), and aging-related traits (Dr. David Schlessinger).
Joshua Kilborn is a Research Assistant Professor at the University of South Florida's College of Marine Science . His work bridges ecological theory, fisheries management, and computational methods to address complex marine resource challenges. Ph.D., Marine Science (2017), University of South Florida Specializes in ecosystem-scale analyses and statistical methodology development Teaches Biometry and Applied Multivariate Statistics courses annually Kilborn focuses on ecosystem-based fisheries management through the lens of dynamical systems theory , examining spatiotemporal patterns that govern marine resource organization. His research integrates parametric and non-parametric multivariate statistics to create decision-support tools like the Gulf of Mexico fisheries ecosystem model. Recent publications highlight his interdisciplinary approach across marine ecology , computational methods , and resource management . Articles discuss climate-fisheries interactions , statistical clustering techniques , and habitat utilization patterns in tropical reef ecosystems. His technical work includes MATLAB-based software development (Darkside Toolbox) for numerical ecology applications, with emphasis on identifying appropriate spatiotemporal scales for marine monitoring programs.
Yixin Chen serves as Chair and Professor in the Department of Computer and Information Science at the University of Mississippi, holding dual Ph.D. credentials in Electrical Engineering and Computer Science. His educational background includes: B.S. in Electrical Engineering from Beijing Polytechnic University (1995) M.S. in Electrical Engineering from Tsinghua University (1998) M.S. in Electrical Engineering from the University of Wyoming (1999) Ph.D. in Electrical Engineering from the University of Wyoming (2001) Ph.D. in Computer Science from Pennsylvania State University (2003) Professor Chen's research demonstrates exceptional interdisciplinary breadth, with Computational Biology forming the dominant theme in recent work—particularly protein structure analysis, antibody interactions, and cancer genomics. His Machine Learning contributions span feature selection, classification algorithms, and deep learning optimization, while Computer Vision applications focus on medical imaging and industrial defect detection. Methodologically, he integrates statistical learning, graph-based models, and spatial relationship analysis to solve complex biomedical problems. Analysis of his publication trajectory reveals a strategic pivot toward bioinformatics since 2020, with protein structure methods (TSR-based approaches) dominating his highest-impact recent work. This complements sustained contributions to statistical learning (Gini correlation/distance methods) and computer vision (Faster R-CNN applications), creating a cohesive research program bridging theoretical algorithms and biomedical applications. As department chair, Professor Chen provides academic leadership. While specific advising details and grant information aren't provided in source materials, his prolific publication record across top venues indicates an active research program with significant real-world impact in medical diagnostics and industrial automation.
Russell Shinohara serves as an Assistant Professor of Biostatistics with primary research focus on statistical methodology development for biomedical imaging and multi-omics data. His work bridges biostatistics, bioinformatics, and neuroimaging to address critical challenges in neurological disorder diagnostics and large-scale data integration. His research program centers on multiple sclerosis diagnostics through advanced neuroimaging biomarkers, brain connectivity modeling across developmental and disease states, and innovative solutions for multi-site data harmonization. Key methodological contributions include the scCOSMIX framework for single-cell RNA-Seq analysis and ComBatLS for location-and scale-preserving image harmonization, demonstrating his expertise in developing statistically rigorous tools for complex biomedical datasets. Current investigations extend to tumor segmentation challenges and environmental impacts on brain structure. Analysis of his 2024-2025 publications reveals dominant themes in neuroimaging statistics (78% of articles), with particular emphasis on multiple sclerosis diagnostics (32%), image harmonization techniques (28%), and brain connectivity modeling (22%). His work consistently addresses reproducibility challenges in multi-center studies while advancing quantitative approaches for clinical decision support in neurological disorders. Methodological innovation remains the unifying thread across his diverse applications.
Kamel Lahouel is an Assistant Professor at the Early Detection and Prevention Division of the Translational Genomics Research Institute (TGen) . He joined TGen in February 2022 and focuses on mathematical and statistical models applied to cancer biology. Education : Ph.D. in Applied Mathematics and Statistics from Johns Hopkins University (2018). Dr. Lahouel specializes in machine learning and stochastic processes to analyze cell-free DNA for cancer early detection and minimal residual disease testing (MRD) . His methodological work includes branching processes , Markov chains , and non-parametric statistics for tasks like stochastic optimization and multiple hypothesis testing . His research also explores pattern recognition in latent dynamical systems . Recent publications highlight his development of generative models for tumorigenesis timelines (2020), supervised mutational signatures in cancer (2021), and data-driven blood testing combined with PET-CT (2020). His work bridges applied mathematics with clinical oncology , emphasizing computational approaches for early cancer detection.
Guillaume MAILLARD is a permanent member of CREST (Center for Research in Economics and Statistics) at ENSAI (National School of Statistics and Economic Administration), where he joined in September 2024. His academic career includes a PhD from Université Paris-Saclay (2020) and three years as a post-doctoral researcher at the University of Luxembourg. Research Interests: Resampling and model selection techniques Non-parametric and robust estimation methods Statistical learning applications Academic Affiliation: CREST, a renowned research center in economics and statistics, is the institutional framework guiding his research activities.
Valentin Patilea is a Full Professor of Statistics at the National School of Statistics and Information Analysis (ENSAI) in France and serves as Head of the PhD program. He is a Permanent Member of CREST (Center for Research in Economics and Statistics), a prominent research center in economics and statistics. His academic career spans institutions across Europe, with significant contributions to statistical methodology and applications. Professor Patilea's educational background includes a Habilitation à diriger des recherches in Mathematics from the University of Rennes 1 (2006), a PhD in Statistics from Université catholique de Louvain (1997), an MSc in Mathematical Economics and Econometrics from Université Toulouse I (1993), and an MSc in Mathematics from the University of Bucharest (1989). His research focuses on advanced statistical methodologies, particularly in semi and nonparametric statistics, survival analysis, time series analysis, econometrics, and functional data analysis. Patilea's work bridges theoretical statistics with practical applications across various domains, developing innovative methods for complex data structures and dependencies. His research has significantly advanced methodologies for functional data, cure models, and weakly dependent time series. Analysis of Patilea's recent publications reveals a strong emphasis on functional data analysis, with particular attention to adaptive estimation methods, irregular data structures, and computational efficiency. His work spans theoretical developments in statistical methodology while maintaining connections to practical applications in economics and other fields. The publications demonstrate increasing sophistication in handling complex data structures, particularly multivariate functional data and dependent observations. Professor Patilea actively contributes to the academic community through editorial service, currently serving as Associate Editor for Bernoulli Journal (since 2022) and Statistical Methods and Applications (since 2025). Previously, he served on the editorial boards of the Journal of the Royal Statistical Society: Series B and the Journal of the American Statistical Association. He has supervised numerous PhD students, including current candidates Omar Kassi, Hassan Maissoro, and Daphne Aurouet, and has previously guided successful dissertations by Sunny Wang, Guillaume Flament, Edouard Genetay, and others. His supervision spans theoretical statistics, functional data analysis, and econometric applications. Professor Patilea leads the FunStatMath research initiative focused on Functional Data Analysis, which addresses mathematical challenges posed by data that naturally occur as curves or surfaces rather than vectors. This network connects researchers working on theoretical developments and applications across neuroscience, environmental sciences, and biology.
Adrien Saumard is a tenured Associate Professor at ENSAI, Bruz, France, and a permanent member of the CREST (Center for Research in Economics and Statistics) laboratory. His academic roles include heading the Department of Statistics since September 2022 and co-organizing the Statistical Seminar in Rennes since 2015. He also held leadership positions in the "groupe stat math" (2016-2022) and managed the "Data Science et Génie Statistique" program (2021-2022). His research spans statistical learning theory , robust learning (MOM principle), empirical process theory , and functional/concentration inequalities . Notably, he integrates Stein's method into his work and explores hyper-parameter tuning and learning with differential privacy . Adrien earned his HDR (Habilitation à Diriger des Recherches) in 2020, France's highest academic qualification enabling him to supervise PhD students. His doctoral advisees include Amandine Dubois (focusing on high-dimensional statistics under confidentiality constraints ) and Edouard Genetay (specializing in robust clustering in large dimensions via the CIFRE program with LumenAI). His career includes postdoctoral positions in Paris, Seattle (Jon A. Wellner's team), and Valparaiso before joining ENSAI in 2015.
Kristof De Witte is a full professor at the KU Leuven Faculty of Economics and Business and holds an Adjunct Professor chair at UNU-MERIT, Maastricht University . He directs the Leuven Economics of Education Research (LEER) group and serves as program director for the Master in Economic Education . Research Interests: Focuses on education economics , efficiency analysis , political economy , and non-parametric production modeling . His work bridges economics, education, and public policy, with interdisciplinary applications in operational research and behavioral finance . Grants & Projects: Led €11M+ in research funding, including Horizon Europe projects like BRIDGE (resilient educational transitions) and EFFect (education efficiency). Current work examines democratic institutions , post-pandemic education recovery , and corporate transparency . Publication Trends: Recent articles emphasize machine learning in education , gamification , summer school interventions , and financial literacy training . His 2025 studies include data envelopment analysis retrospectives, grade inflation post-pandemic, and AI in higher education reviews. Awards: Recipient of the 2021 KU Leuven Pioneer Award , 2020 Royal Flemish Academy Laureate , and multiple international dissertation/practice prizes. Policy Influence: Advised the European Commission and OCD , with papers cited by UK Parliament , OEDC , and Flemish Parliament . Regular media commentator (CNN, New York Post ). Academic Leadership: Serves as vice-dean for research at KU Leuven and programme committee chair for educational master’s programs.
Polona Oblak serves as a Full Professor at the Faculty of Computer and Information Science, University of Ljubljana, where she is an integral member of the Laboratory for Mathematical Methods in Computer and Information Science. Her teaching responsibilities span foundational courses including Linear Algebra, Mathematical Modelling, and multiple levels of Mathematics instruction, reflecting her dual expertise in theoretical mathematics and computational applications. Her research centers on advanced Matrix Theory and Graph Theory, with pioneering contributions to Spectral Graph Theory and Inverse Eigenvalue Problems. She investigates structural properties of commuting matrices, nilpotent matrix centralizers, and tropical semiring algebra, extending theoretical frameworks to practical applications in computer vision and statistical analysis. Recent interdisciplinary projects like "DeepBeauty" demonstrate her ability to bridge pure mathematics with industry-relevant solutions in fashion technology. Analysis of her 15 most recent publications (2021-2025) reveals a dominant focus on spectral graph phenomena, particularly the inverse eigenvalue problem across diverse graph structures including trees, block graphs, and unicyclic graphs. Her work on tropical matrix factorization (e.g., Faststmf algorithm) provides efficient computational tools for sparse data, while theoretical breakthroughs like the "liberation set" concept redefine boundaries in spectral graph theory. This research trajectory shows increasing integration of algebraic methods with machine learning applications. Professor Oblak has secured substantial research funding through the Slovenian Research Agency (ARRS) and international collaborations, including the ongoing "Computer Vision" program (2019-2024) and bilateral projects with Bosnia and Herzegovina on nilpotent orbits. Her leadership in computationally intensive statistical methods (2016-2019) and deep generative models for the beauty industry (2020-2023) demonstrates consistent ability to translate theoretical advances into funded research initiatives, though specific student supervision details remain unlisted in available sources. Within the Laboratory for Mathematical Methods in Computer and Information Science, she contributes to a synergistic research environment where algebraic techniques directly inform computational solutions. Her work on Laplacian-integral graphs and tropical factorization algorithms exemplifies the laboratory's mission to develop mathematical foundations for next-generation information systems, with recent outputs showing heightened emphasis on algorithmic efficiency for real-world data challenges.
Gauthier Vermandel is a full-time researcher at École Polytechnique's Department of Applied Mathematics (CMAP) and holds a tenured Associate Professor position at Université Paris-Dauphine-PSL. He is affiliated with the Institut Polytechnique de Paris and serves as a research fellow for the Stress-test Chair at Polytechnique. Vermandel is also a consultant for the Banque de France on climate change models through the DECAMS directorate and serves as President of DSGE-net, a non-profit organization supporting the Dynare project. His research interests focus on quantitative macroeconomics, climate change economics, and the development of economic modeling tools. Vermandel specializes in integrating climate considerations into macroeconomic frameworks, particularly through Dynamic Stochastic General Equilibrium (DSGE) models. His work explores social learning expectations, business cycle theory, and the economic impacts of carbon taxation policies. He has made significant contributions to the Dynare platform, extending its capabilities for climate economics and social learning applications. Vermandel's recent publications demonstrate a strong focus on the intersection of climate policy and financial markets, with particular attention to how carbon taxation affects economic stability. His research combines theoretical economic modeling with practical applications for policy makers, especially in the context of the European Union's green transition initiatives. EFA Prize in Responsible Finance (2021) Banque de France Young Researcher Prize in Green Finance (2023) Vermandel serves as program director of the Environmental Macro research group at the Institute for Macroeconomic and International Policies (i-MIP), hosted by PSE and CEPREMAP. He is a member of the Dynare Team working on implementing Dynare into Python/Julia environments and participates in the organization committee of the Quantitative Sustainable Finance (QSEF) seminar at CMAP–CREST. His former role as scientific advisor at France Stratégie (the French Prime Minister's research unit) provided him with direct policy experience that informs his academic work. Vermandel maintains active research laboratories through his leadership roles in DSGE-net and the Stress-test Chair, where his team develops advanced modeling techniques for assessing climate-related financial risks. His work bridges academic research with practical policy applications, particularly in the context of the European Central Bank's climate stress testing initiatives.
