Find Academic Supervisors

• Mathematical Physics
• Interacting Stochastic Systems
• Gibbs Measures
• +4 more

Prof. Dr. Christof Külske is a faculty member at the Faculty of Mathematics , Ruhr University Bochum, specializing in Stochastics . His research focuses on interacting stochastic systems, Gibbs measures, phase transitions, and disordered systems in mathematical physics and statistical mechanics. Academic Affiliation: Ruhr University Bochum (Faculty of Mathematics, Stochastics Group) Research Interests: Mathematical Physics, Interacting Stochastic Systems, Gibbs Measures, Phase Transitions, Gradient Models, Disordered Systems Students: Niklas Schubert (PhD student, co-authored works on clock models and Widom-Rowlinson systems) Scientific Awards: Humboldt Research Award Recent publications analyze Ising models, Widom-Rowlinson systems, SOS models, and gradient Gibbs measures on trees, emphasizing extremal decomposition, non-Gibbsian behavior, and symmetry breaking. His work combines rigorous mathematical analysis with applications in physics, often collaborating with Benedikt Jahnel, Florian Henning, and Utkir Rozikov. He has organized major conferences including the Bernoulli-IMS 11th World Congress in Probability and Statistics (2024) and workshops on transformations and phase transitions. His editorial roles include associate editor for Stochastic Processes and their Applications .

Grigory PishchulovView profile

Senior Lecturer

• Supply Chain Management
• Sustainable Supply Chain Management
• Supply Chain Coordination
• +4 more

Grigory Pishchulov is a Senior Lecturer in Information Systems and Supply Chain Management at Alliance Manchester Business School, The University of Manchester , where he also serves as Programme Director for the BSc in Information Technology Management for Business. He holds a PhD in Operations Management from European University Viadrina Frankfurt (Oder), Germany. Roles: Senior Lecturer, Programme Director, Research Fellow (Smart Manufacturing, 2017–2019) External Roles: Part-Time Professor at St Petersburg State University (2017–2021) Research Focus: Sustainable supply chains, supply chain coordination, digital platforms, and SME collaboration in Industry 4.0 His work contributes to UN Sustainable Development Goals, particularly in sustainable practices and innovation. He has secured funding from the German Federal Government, Leverhulme Trust, and ESRC, including a Leverhulme Visiting Fellowship (2010). Research outputs include 38 publications in top journals like European Journal of Operational Research and Annals of Operations Research , plus datasets on supply chain collaboration models and journal rankings. Awards: Best Reviewer Award (2021) Activities: Peer review for conferences, organizing the 2nd International Conference on Business Management in the Digital Economy (2019) Pishchulov advises on PhD projects and contributes to initiatives like the Supply Chain Team Formation Data Model . His work bridges academia and industry through digital innovation and sustainability in global supply networks.

• Statistics
• Fuzzy Sets
• Operations Research
• +4 more

Dr. Gil Gonzalez Rodriguez is a Professor in the Department of Statistics and Operations Research at the School of Sciences, University of Oviedo. His research primarily focuses on statistical analysis of fuzzy and imprecise data, with significant contributions to the theory and application of random fuzzy sets, interval data analysis, and statistical methods for uncertain data. His educational background includes a doctorate from the University of Oviedo with a thesis on "Strict Monotonicity Tests for the Regression Function" supervised by Dr. José Santos Domínguez Menchero. His research interests span Statistics, Fuzzy Sets, Operations Research, Data Analysis, Interval Data Analysis, Random Sets, and Imprecise Data. Gonzalez Rodriguez has published 47 papers since 2004, with his work primarily appearing in top statistics journals including Computational Statistics and Data Analysis, International Journal of Approximate Reasoning, and Information Sciences. His research shows a consistent focus on developing statistical methodologies for handling imprecise and fuzzy data, with applications across various domains. His most cited works include "A new family of metrics for compact, convex (fuzzy) sets based on a generalized concept of mid and spread" (70 citations) and "Fuzzy data treated as functional data: a one-way ANOVA test approach" (57 citations). His scientific contributions have been recognized through extensive collaboration, with 35 co-authors and 51 joint publications. Notable collaborators include Ana Colubi, María Ángeles Gil, Manuel Montenegro, and Ana Belén Ramos-Guajardo. Dr. Gonzalez Rodriguez has been actively involved in the research group "GRINAT Grupo de Investigación en Riesgos Naturales" and previously participated in the group "SMIRE+CoDIRE Estadística con Elementos Imprecisos Aleatorios & Comparación de Distribuciones entre Elementos Aleatorios." His work bridges theoretical statistical development with practical applications, particularly in environmental contexts such as tidal fluctuation analysis.

• Artificial Intelligence
• Machine Learning
• Business Analytics
• +6 more

Yifan Yu is an Assistant Professor at the Department of Information, Risk, and Operations Management, McCombs School of Business, The University of Texas at Austin. He holds a Ph.D. in Information Systems from the University of Washington and bachelor's/master's degrees in Management Science and Engineering from Tsinghua University. Research Focus: Economics of AI/ML, unstructured data analytics (text/images/video), and data-driven sustainable operations. Methodologies: Machine/deep learning, network analysis, econometric/game-theoretical modeling, and experiments. Publications: Appeared in top journals like Management Science , MIS Quarterly , and Information Systems Research . Awards: Best paper recognitions at INFORMS, CIST, WITS, and teaching awards from University of Washington.

Nataliya ChukhrovaView profile

Researcher

• Data Science
• Business Analytics
• Soft Computing
• +9 more

Dr. Nataliya Chukhrova is a Researcher at the Institut für Mathematik und Statistik within the University of Hamburg Business School . Her research focuses on Data Science, Business Analytics, Fuzzy Statistics, and Quantitative Risk Management, with applications in Finance, Health, and Industrial Engineering. She has received notable awards including the Hamburger Lehrpreis 2015 for innovative teaching and the Jahrgangsbestenpreis 2011 for academic excellence. Her teaching portfolio includes courses such as Angewandte Statistik für Fortgeschrittene , Statistik mit SPSS , and Statistische Qualitätskontrolle . Her publications emphasize statistical methods in quality control, fuzzy hypothesis testing, and stochastic modeling, with a focus on practical applications in healthcare and finance. In recent years, her work has explored the intersection of fuzzy logic with traditional statistical techniques, particularly in addressing challenges posed by the COVID-19 pandemic. She collaborates extensively, often with co-author Arne Johannssen, on projects that bridge theoretical statistics and real-world operational problems.

Dan RalescuView profile

Professor

• Probability Theory
• Uncertainty Theory
• Fuzzy Sets
• +5 more

Dan Ralescu is a Professor of Mathematics at the University of Cincinnati since 1980, affiliated with the Department of Mathematical Sciences in the College of Arts and Sciences. He holds editorial roles in 10 journals and has delivered plenary lectures globally. His research focuses on probability theory, fuzzy sets, and uncertainty modeling, with applications in finance, engineering, and decision theory. Education: PhD in Statistics (Indiana University, 1980), M.A. Mathematics with Honors (University of Bucharest, 1972), and additional M.A. in Mathematics/Statistics (Indiana University, 1979). Research interests include limit theorems for random sets, mixed uncertainty models, Bayesian robustness, and large data set management. His work bridges statistical methodologies with fuzzy logic systems, particularly in financial modeling and risk analysis. Key contributions include the Liu Process in uncertainty theory, portfolio optimization models using fuzzy time series, and risk index frameworks for uncertain systems. Awards include the 2014 Lifetime Achievement Award from China’s Society for Uncertainty and IFSA Fellowship. Active in grants since 1985, including NSF-funded projects on decision-making under uncertainty and collaborative efforts with international institutions like Tokyo Institute of Technology. He has supervised numerous research collaborations and publishes extensively in top-tier journals.

Hannes FeilhauerView profile

Professor

• Remote Sensing
• Biodiversity Mapping
• Ecosystem Research
• +5 more

Hannes Feilhauer is a full professor at Leipzig University since 2020, where he heads the Remote Sensing in Geo- and Ecosystem Research group. He maintains dual affiliation with the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. His previous academic positions include visiting professor at the Free University of Berlin (2018) and senior scientist at the Institute of Geography, Friedrich-Alexander Universität Erlangen-Nürnberg (2010). His educational background comprises a PhD in Geography from Bonn University (2009) and a Diploma in Geoecology from Bayreuth University (2006). Professor Feilhauer's research centers on spatio-temporal patterns of species distributions and functional properties in terrestrial ecosystems. He pioneers methodologies combining high-resolution remote sensing data with in-situ observations for data-driven ecological analysis. His signature contribution involves mapping fuzzy ecosystem characteristics that defy conventional crisp mapping approaches, requiring innovative methodological development. This work bridges remote sensing technology with fundamental ecological questions about biodiversity patterns and ecosystem functioning. Analysis of his publication record reveals a consistent focus on advancing vegetation mapping techniques beyond traditional categorical approaches. His work increasingly emphasizes continuous gradient mapping, fuzzy classification, and trait-based remote sensing to capture ecological complexity. Key methodological innovations include brightness-normalized PLSR, ensemble spectral band selection, and integration of ordination techniques with imaging spectroscopy. Science award for Physical Geography of the Prof. Dr. Frithjof Voss Stiftung für Geographie Professor Feilhauer maintains active collaborations with researchers across European institutions, particularly with Daniel Doktor (UFZ), Sebastian Schmidtlein, and Angela Lausch. His research has significant applications in biodiversity conservation, habitat monitoring under Natura 2000, and understanding climate change impacts on ecosystems. His leadership of the Remote Sensing in Geo- and Ecosystem Research group drives methodological innovation while addressing pressing environmental challenges through advanced remote sensing approaches. The research group operates at the intersection of remote sensing technology and ecological theory, developing tools that capture ecological complexity more faithfully than conventional approaches. Their work demonstrates how continuous representation of ecological gradients provides superior insights compared to traditional discrete classification methods, particularly for conservation applications where transitional zones and subtle compositional changes carry significant ecological meaning.

Brian BaetzView profile

Professor

• Sustainable Communities
• Resilient Systems
• Environmental Systems Analysis
• +7 more

Brian Baetz serves as the Director of the Walter G. Booth School of Engineering Practice and Technology at McMaster University. His academic career spans several decades with a focus on engineering systems, sustainable communities, and environmental modeling. Dr. Baetz's research interests lie in the synthesis of a range of modeling approaches (optimization, simulation, decision analysis) to problems in sustainable and resilient communities and hydrological and environmental systems analysis. He has developed prototype decision support systems for improved decision-making in these areas. As a systems engineer, his work focuses on the development of more resilient and integrated communities. His scholarly activity demonstrates expertise across multiple domains including waste management systems, water resources planning, climate change impact analysis, and sustainable community development. Dr. Baetz has published extensively in high-impact journals across environmental engineering, hydrology, and sustainability science, with his most recent work (2021-2024) showing advanced applications of machine learning and sophisticated uncertainty analysis methods. Dr. Baetz has received significant scholarly attention, with his work being referenced in policy sources, picked up by news outlets, and shared across academic networks. His research has practical applications in municipal planning, environmental management, and sustainable infrastructure development. Extensive publication record spanning from 1988 to present Research cited by policy makers and featured in media outlets Active contributor to sustainable engineering practices and education Teaching experience across multiple engineering and sustainability courses His work bridges theoretical modeling approaches with practical applications for community resilience and environmental sustainability.

Shu ShenView profile

Associate Professor

• Econometric Theory
• Applied Econometrics
• Applied Microeconometrics
• +6 more

Shu Shen is an Associate Professor and Graduate Program Chair in the Department of Economics at the University of California, Davis, specializing in econometric theory and its applications in microeconometrics. His academic credentials include: Ph.D. in Economics, University of Texas at Austin, 2011 M.S. in Economics, University of Texas at Austin, 2008 B.A. in Economics, Fudan University, Shanghai, 2006 Shen's research centers on advancing econometric methodologies for causal inference, with emphasis on regression discontinuity designs, GMM identification, multiple testing frameworks, and distributional/quantile analysis. His work bridges theoretical rigor with practical applications in program evaluation and treatment effect heterogeneity. His publications reveal consistent contributions to methodological econometrics, particularly in developing robust techniques for regression discontinuity, treatment effect analysis, and multiple hypothesis testing. These works frequently employ nonparametric and semiparametric approaches to address distributional questions in empirical economics, appearing in leading journals like the Review of Economics and Statistics and Journal of Econometrics . Shen's research impact is recognized through prestigious awards: Hellman Fellow, University of California, Davis (2015) Individual Research Grant, UC Davis Institute for Social Sciences (2014) Small research grant, University of California, Davis (2012–2014, 2019) Hale Fellowship, University of Texas at Austin (2008) As Graduate Program Chair, he oversees doctoral training in Economics while securing research funding from institutional sources. His teaching portfolio spans undergraduate econometrics (ECN 102, 140) and graduate-level courses (ECN 240D, 240F), emphasizing both theoretical foundations and empirical applications.

• Computational Geometry
• Discrete Geometry
• Convex Optimization
• +6 more

Francisco Criado is a researcher with significant contributions to computational geometry, discrete mathematics, and applied algorithm design. His work spans theoretical and applied domains, including convex optimization, Voronoi diagrams, and geometric modeling in gas dynamics and earthquake decision-making frameworks. Collaborators : Francisco Santos, Sebastian Pokutta, Michael Joswig Key Venues : Discrete & Computational Geometry , Foundations of Computational Mathematics , NeurIPS Research Interests focus on geometric algorithms, polyhedral complexes, and applications of fuzzy logic. His 2025 papers explore convex hulls, zonotopes, and tropical geometry, while earlier works address gas dynamics and decision-making models. Article Trends reveal expertise in high-dimensional geometry, combinatorial optimization, and numerical methods. He frequently employs randomized and linear convergence algorithms in 2022–2025. Advising and Grants : No explicit data provided, but his extensive co-authorship network suggests collaborative research leadership.

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.

AHMED HATİPView profile

Assistant Professor

• Neutrosophic Mathematics
• Algebra
• Mathematics Education
• +3 more

Ahmed Hatip serves as an Assistant Professor in the Department of Mathematics and Science Education at Gaziantep University's Faculty of Education since 2023, following his appointment as Teaching Staff from 2019-2023. His academic work bridges neutrosophic mathematics with primary mathematics education, particularly for Arabic-speaking students at the Afrin campus. Education: PhD in Mathematics, Gaziantep University, Turkey (2019-2023) Master's in Mathematics (with thesis), Gaziantep University, Turkey (2016-2018) Bachelor's in Mathematics, Aleppo University, Syria (2004-2009) His research pioneers neutrosophic algebraic structures, plithogenic sets, and refined neutrosophic calculus, with significant contributions to neutrosophic modules, rings, and vector spaces. He actively develops applications in educational contexts, focusing on integrating non-classical mathematics into primary curricula through Arabic-language pedagogy. Analysis of his 15 most recent publications (2023-2025) reveals three dominant trends: (1) Theoretical expansion of symbolic n-plithogenic structures (n=2 to 6), (2) Cross-disciplinary applications in IoT security and homomorphic encryption, and (3) Educational implementations of neutrosophic logic in primary mathematics teaching. His work shows increasing collaboration with international researchers across Turkey, Syria, and the United States. Scientific Awards: 2021 University Best Paper Award Dr. Hatip supervises graduate research in neutrosophic algebra, currently guiding PhD candidate Nader Taffash's work on neutrosophic logic in algebraic structures and having completed Talib Altalib's 2025 Master's thesis on Symbolic Plithogenic Rings. His editorial roles include serving on the boards of Neutrosophic Knowledge (2020) and Plithogenic Algebraic Structures (2023), reflecting his leadership in this specialized mathematical domain.

Mehrdad NaderiView profile

Lecturer

• Classification
• Cluster Analysis
• Finite Mixture Models
• +5 more

Mehrdad Naderi is a Lecturer in Statistics at the Department of Mathematics, Physics, and Electrical Engineering , Northumbria University . His academic journey includes a PhD in Mathematical Statistics from Shahid Bahonar University of Kerman (2017) and postdoctoral research at National Chung Hsing University (Taiwan), Ferdowsi University of Mashhad (Iran), and University of Pretoria (South Africa). Education: PhD in Mathematical Statistics, Shahid Bahonar University of Kerman (2017) His research focuses on applied statistical inference with emphasis on classification , cluster analysis , factor analysis , finite mixture models , and EM algorithm for robust estimation. He has contributed to multivariate and matrix-variate analysis, particularly in handling outliers and asymmetrical data structures. Recent work includes three-way data clustering using matrix-variate normal distributions and robust Bayesian inference for censored mixture models. His publications demonstrate expertise in distribution theory, statistical computation, and applications to financial data, environmental modeling, and astrophysics. Current collaborations span multiple institutions, focusing on heavy-tailed distributions and computational methods for complex data structures.

• Bayesian analysis
• Data mining
• Machine learning
• +3 more

Dr. Tri M. Le serves as Associate Professor of Mathematics and Computer Science and Program Coordinator for the M.S. in Data Science at Mercer University's College of Professional Advancement, Department of Informatics and Mathematics. He joined Mercer in 2017 after working as a Predictive/Computational Statistician at the University of Nebraska-Lincoln, bringing expertise in statistical software (SAS, SPSS, R) and extensive teaching experience at undergraduate and graduate levels. His educational background includes: PhD and MA in Statistics, University of Missouri-Columbia (2014) MS in Probability and Statistics, Ho Chi Minh City University of Natural Sciences (2002) BS in Mathematics and Informatics, Ho Chi Minh City University of Natural Sciences (1999) Dr. Le's research spans Bayesian analysis, decision theory, spatio-temporal modeling, and machine learning. His work addresses fundamental questions in model uncertainty, prediction reliability, and the interpretability-performance trade-off in statistical learning. He has published in leading journals including Journal of Machine Learning Research and Bayesian Analysis, with recent focus on model averaging superiority over selection and theoretical foundations of ensemble methods. Analysis of his 2016-2022 publications reveals consistent focus on Bayesian predictive modeling, with increasing emphasis on interpretable machine learning. His work bridges theoretical statistics and practical applications in healthcare analytics and environmental systems, demonstrating interdisciplinary relevance through collaborations in geoscience and criminal justice research. Dr. Le actively contributes to the academic community as reviewer for Bayesian Analysis and International Conference on Fuzzy Systems and Data Mining, and as Session Chair for the Joint Statistical Meetings. His leadership extends to Mercer's Tenure and Promotion committee and the M.S. in Data Science program coordination. While no dedicated research lab is mentioned, Dr. Le's role as Program Coordinator provides structured opportunities for students through the M.S. in Data Science curriculum. His courses in Data Analytics and Healthcare Data Analytics offer practical training grounded in his research on predictive modeling and statistical inference.

• Fuzzy Data Analysis
• Statistical Analysis of Imprecise Data
• Random Sets
• +3 more

Ana Belén Ramos Guajardo is a Professor in the Department of Statistics and Operations Research and Mathematics Education at the University of Oviedo. She is affiliated with the GRINAT Research Group (Grupo de Investigación en Riesgos Naturales) and holds a doctorate from the University of Oviedo with her thesis titled "Contrastes de hipótesis tratamiento de la variabilidad y la imprecisión" (2011), supervised by Dr. Gil González Rodríguez, Dr. María Angeles Gil Alvarez, and Dr. Ana María Colubi Cervero. Her research focuses on statistical methodologies for imprecise data, particularly fuzzy data analysis, random sets, interval data analysis, and hypothesis testing. She has developed innovative approaches for distance-based statistical analysis, fuzzy clustering, and regression models for random intervals. Her work bridges theoretical statistics with practical applications in environmental sciences and other domains requiring analysis of imprecise measurements. Her publications reveal a strong focus on developing statistical methods for non-standard data types, with particular emphasis on random fuzzy sets and interval-valued data. She has contributed significantly to hypothesis testing frameworks for fuzzy data, distance metrics for imprecise data analysis, and computational methods for implementing these techniques. Her most recent work continues to advance the theoretical foundations while exploring practical applications. Ramos Guajardo has collaborated extensively with researchers in the fuzzy data analysis community, particularly with Ana Colubi, Gil González-Rodríguez, and María Asunción Lubiano. Her work has been published in top statistical journals including International Journal of Approximate Reasoning, Fuzzy Sets and Systems, and Information Sciences. She has advised or collaborated with numerous researchers in the field, contributing to the development of statistical fuzzy data analysis as a specialized subfield. Her methodological contributions have applications in environmental risk assessment, particularly through her affiliation with the Natural Risks Research Group at the University of Oviedo.