جست‌وجوی استاد و پژوهشگر

• Probability Theory
• Stochastic Processes
• Lévy Processes
• +۵ مورد دیگر

Víctor Rivero is a Professor in the Department of Probability and Statistics at the Center of Research in Mathematics (CIMAT) in Guanajuato, Mexico. He previously served as Director of CIMAT from January 2017 to December 2022 and spent a sabbatical year (August 2023-July 2024) at the Probability Group of the Department of Statistics at the University of Warwick, UK. Starting in 2025, he will serve as Editor of ALEA - Revista Latinoamericana de Probabilidad y Estadistica and is part of the Scientific Committee for the 11th International Conference on Lévy Processes to be held in Sofia, Bulgaria in 2025. He also serves on the steering committee of the CIMAT-UNAM-Warwick-Bath research platform for UK-Latin America engagement in probability. Rivero's research focuses on probability theory and stochastic processes, with particular expertise in Lévy processes, Markov processes, and self-similar processes. His work spans fluctuation theory of real-valued Lévy processes and Markov additive processes, stable processes, excursion theory, local times as stochastic processes, branching processes, and regenerative sets. He has developed deep connections between these areas, often using the Lamperti transform to relate self-similar Markov processes to Lévy processes. An analysis of his recent publications reveals a continued focus on foundational aspects of probability theory with applications to various mathematical models, particularly examining how scaling properties and self-similarity manifest in complex systems with increased attention to multidimensional settings and connections to biological models. Rivero actively mentors graduate students at institutions including CIMAT, University of Warwick, and University of Bath, frequently collaborating with other researchers like Andreas Kyprianou on student supervision. He has been a frequent visitor to leading probability research centers worldwide, including institutions in France (Université Sorbonne Paris Nord, Angers) and the UK (Bath, Warwick, Manchester). His teaching includes courses on Lévy processes and self-similar Markov processes, with lecture notes available for students. He is also part of the Network of Creative Teaching in Mathematics, which aims to promote mathematics as an activity that stimulates creative, conceptual, and emotional development.

Nathan Juddمشاهده پروفایل

پژوهشگر ارشد

• Bayesian non-parametric methods
• Modern slavery data analysis
• Non-diffusive Wright-Fisher processes
• +۲ مورد دیگر

Nathan Judd is a Research Fellow in Statistics at the School of Mathematics, University of Birmingham. His research focuses on Bayesian non-parametric methods applied to modern slavery data and stochastic process modeling. He earned his PhD in Statistics from the University of Warwick (2024), MSc in Statistics from Lancaster University (2019), and BSc in Mathematics from the University of Kent (2018). Education: PhD in Statistics, University of Warwick (2024) MSc in Statistics, Lancaster University (2019) BSc in Mathematics, University of Kent (2018) Research themes include: Construction of non-diffusive Wright-Fisher processes Bayesian non-parametric models for crime-linkage analysis Testing methodologies for jumps in discretely observed stochastic processes Application of statistical models to socio-political challenges Recent publications demonstrate expertise in predictive modeling during disruptions, including the 2025 paper on zero-inflated mixed effects models for foodservice sales forecasting. Contact: n.a.judd@bham.ac.uk

Nathan Juddمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Probability Theory
• Stochastic Processes
• +۱ مورد دیگر

Nathan Judd is a Research Fellow in Statistics at the University of Birmingham. His research focuses on non-diffusive constructions of Wright-Fisher processes with desirable properties, supervised by Professor Dario Spanò. He holds a BSc in Mathematics from the University of Kent (2018) and an MSc in Statistics from Lancaster University (2019). Education: BSc Mathematics, University of Kent (2018) MSc Statistics, Lancaster University (2019) His research interests emphasize advanced probability theory and applications in population genetics. Despite no listed publications here, his doctoral work addresses foundational questions in stochastic modeling. No awards or active grants are mentioned. Further details about labs or collaborative teams are unavailable in the provided text.

• Probability Theory
• Stochastic Processes
• Lévy Processes
• +۷ مورد دیگر

Víctor Rivero is a mathematician affiliated with the Department of Probability and Statistics at the Center for Research in Mathematics (CIMAT) in Guanajuato, Mexico. He specializes in stochastic processes, particularly Lévy processes, self-similar Markov processes, fluctuation theory, and excursion theory. His research explores applications in branching processes, local times, and regenerative sets. He has held roles including Director of CIMAT (2017–2022), Editor of ALEA - Revista Latinoamericana de Probabilidad y Estadística (2025–present), and member of the Scientific Committee for the 11th International Conference on Lévy Processes (2024–2025). He has collaborated internationally, including a sabbatical at the University of Warwick’s Probability Group (2023–2024). His research trends focus on advancing the theory of self-similar Markov processes, Lévy processes in cones, and applications of excursion theory. He has advised multiple PhD and master’s students, including notable advisees such as Harry Sapranidis Mantelos and Mehar Motala. He is part of collaborative platforms like the CIMAT-UNAM-Warwick-Bath research network and contributes to teaching through lecture notes on Lévy processes and stochastic models. His work bridges foundational probability theory with applied stochastic modeling.

Paul Jenkinsمشاهده پروفایل

استاد

• Monte Carlo methods
• Inference from stochastic processes
• Mathematical population genetics
• +۳ مورد دیگر

Paul Jenkins is a Professor in the Departments of Statistics and Computer Science at the University of Warwick, where he conducts research at the intersection of statistical methodology, computational biology, and population genetics. He is an active member of the Warwick Machine Learning group, focusing on probabilistic AI and computational statistical methods. His research centers on Monte Carlo methods, inference from stochastic processes, mathematical population genetics, and machine learning applications. Professor Jenkins develops sophisticated statistical techniques for analyzing genetic data, with particular emphasis on Wright-Fisher diffusion processes, coalescent theory, Bayesian inference methods, and recombination rate estimation. His work bridges theoretical statistical development with practical applications in evolutionary biology and genomic epidemiology. Professor Jenkins teaches advanced courses including ST341 Statistical Genetics, ST418 Statistical Genetics with Advanced Topics, and ST407 Monte Carlo Methods. He currently supervises undergraduate and Masters projects through modules CS350, ST415, ST421, and ST955, though he notes he cannot accept undergraduate projects or internships from outside Warwick. His publication record demonstrates consistent high-impact research output, with numerous publications in top-tier journals including Annals of Applied Probability , Genetics , PLoS Genetics , and Bioinformatics . Recent work shows increasing interdisciplinary reach, with applications spanning from viral evolution (SARS-CoV-2 recombination) to healthcare analytics (multimorbidity modeling). Warwick Machine Learning Group: Active contributor to probabilistic AI research initiatives Current recruitment: Seeking postdoctoral Research Fellow in Probabilistic AI

Alex Roitershteinمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Processes
• Statistical Models
• +۵ مورد دیگر

Alex Roitershtein is an Instructional Associate Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on probability theory, stochastic processes, and their applications in statistical models, combinatorics, and complex systems. He has contributed to areas including random walks, Markov processes, evolutionary dynamics, and network science. His work often bridges theoretical developments with practical applications in infrastructure resilience, genetic models, and algorithmic combinatorics. Roitershtein’s recent publications explore topics ranging from optimal resource allocation strategies in financial systems to extinction dynamics in evolutionary models. He has also investigated network robustness through Markovian influence graphs and studied combinatorial patterns in permutations and words. His research frequently employs probabilistic methods, generating functions, and stochastic analysis to address both foundational and applied questions. His articles reflect a multidisciplinary approach, with contributions to statistical physics (e.g., avalanche dynamics in excitable networks), computational biology (e.g., Wright-Fisher population models), and discrete mathematics (e.g., enumeration of staircase patterns). While no specific awards are listed, his extensive publication record demonstrates sustained scholarly activity in probability and its applications.

Dario Spanòمشاهده پروفایل

استاد

• Combinatorial Stochastic Processes
• Mathematical Population Genetics
• Bayesian Nonparametric Statistics
• +۳ مورد دیگر

Professor Dario Spanò is a faculty member in the Department of Statistics at the University of Warwick, serving as Deputy Head (Teaching and Learning). He holds a PhD in Mathematical Statistics (University of Pavia, 2003) and a Laurea in Economics (University of Pavia, 1998). His research focuses on combinatorial stochastic processes, Bayesian nonparametric statistics, and mathematical population genetics, with applications to genetics and evolutionary dynamics. His work includes contributions to Wright-Fisher diffusions, coalescent theory, and exact simulation methods. He has supervised numerous PhD students and co-organizes international conferences in statistics and probability. Research Interests: Stochastic processes and their applications in population genetics Bayesian nonparametric methods and their theoretical foundations Exact simulation of diffusion processes Coalescent theory and genealogical processes Recent Work Trends: Recent publications emphasize theoretical developments in diffusion models (e.g., Wright-Fisher processes), duality methods, and computational Bayesian inference for genetic data. Key themes include excursion theory, selection dynamics in random environments, and algorithmic advancements for simulating complex stochastic systems. Awards & Recognition: No scientific awards explicitly listed, though his research has been widely cited in the field. Teaching & Supervision: Current courses include ST343 and ST419 (data science topics). He has supervised over a dozen PhD students, focusing on interdisciplinary projects in statistics and mathematical genetics. His students have pursued roles in academia and industry. Labs/Teams: Active in the CRiSM research centre at Warwick, contributing to collaborative projects in statistical genetics and computational methods.

Ivan Papićمشاهده پروفایل

استادیار

• Diffusions and fractional diffusions
• Time-changed stochastic models
• Applied probability
• +۵ مورد دیگر

Ivan Papić is an Assistant Professor at the School of Applied Mathematics and Informatics , affiliated with the Josip Juraj Strossmayer University of Osijek . His research focuses on stochastic processes, particularly fractional diffusions , non-stationary models , and long-range dependence . PhD in Mathematics (2019, University of Zagreb) MSc in Financial and Business Mathematics (2013, University of Osijek) MSc in Mathematics and Computer Science Education (2020, University of Osijek) BSc in Mathematics (2011, University of Osijek) His work bridges applied probability and statistical modeling , with applications in epidemiology (e.g., modeling SARS-CoV-2 spread via stochastic SEIR models) and mathematical finance. Recent publications explore advanced diffusion models like stretched non-local Pearson diffusions and fractional Bessel processes . Projects such as Scaling in stochastic models (2023–2027) and Stochastic models with long-range dependence highlight his expertise. He actively participates in international conferences, including the 9th European Congress of Mathematics (2024), and serves as Erasmus coordinator for MATHOS since 2024. Teaching responsibilities include courses like Stochastic Processes I , Applied Statistics , and Bayesian Statistics at the University of Osijek, with a focus on interdisciplinary applications across departments such as Mathematics, Physics, and Civil Engineering.

Nenad Šuvakمشاهده پروفایل

دانشیار

• Stochastic Processes
• Diffusion Models
• Statistical Inference for Stochastic Processes
• +۴ مورد دیگر

Nenad Šuvak is an Associate Professor at the School of Applied Mathematics and Informatics, Josip Juraj Strossmayer University of Osijek. His research focuses on diffusion processes and statistical analysis of stochastic systems, with applications spanning finance, epidemiology, neuroscience, and agricultural modeling. Education : PhD in Mathematics (University of Zagreb, 2010), BSc in Mathematics and Computer Science (University of Osijek, 2004) Šuvak's work extends classical stochastic models to heavy-tailed and fractional frameworks. He has developed diffusion models for EEG data in cerebral malaria patients, created non-linear stochastic models for dopamine cycles, and refined epidemic SEIR models for SARS-CoV-2. His methodological contributions include correlated continuous-time random walks and parameter estimation techniques for non-stationary diffusions. His publications demonstrate expertise in stochastic processes with heavy-tailed distributions, fractional calculus applications, and statistical inference for complex systems. Notably, he has worked on financial risk assessment (CROBEX modeling), epidemiological modeling (SARS-CoV-2 spread), and biomedical applications (EEG analysis). Professional Activities : Organizer of mathematical conferences, member of program committees (Young Statisticians Meetings), and reviewer for multiple scientific journals Projects : PI for stochastic models projects, participant in EU COST actions, and international bilateral collaborations

• Probability Theory
• Statistical Mechanics
• Stochastic Processes
• +۴ مورد دیگر

Gioia Carinci is an Associate Professor at the University of Modena and Reggio Emilia, affiliated with the Department of Physical, Computer and Mathematical Sciences. She teaches courses such as Probability and Statistics for Computer Science, Mathematics for Electronic Engineering, and Markov Chains for Mathematics students. Her research focuses on stochastic processes, interacting particle systems, and duality principles in non-equilibrium statistical physics. Teaching: Probability and Statistics (Computer Science) Mathematics for Electronic Engineering (Engineering) Markov Chains (Mathematics) Research Interests: Analysis of Markov chains and their convergence properties Boundary-driven non-equilibrium systems Large deviations and additivity principles Duality in stochastic processes Key Contributions: Derivation of macroscopic properties from microscopic models Study of condensation phenomena in particle systems Development of exact solutions for stationary non-equilibrium states Her work bridges probability theory, statistical mechanics, and applications in mathematical physics. She actively contributes to both theoretical advancements and interdisciplinary collaborations.

Wuchen Liمشاهده پروفایل

استادیار

• Transport information geometry
• Complex dynamical systems
• Partial differential equations (PDEs)
• +۸ مورد دیگر

Wuchen Li is an Assistant Professor in Mathematics at the University of South Carolina , specializing in Transport information geometry and its applications across Complex Dynamical systems, PDEs, Statistics, Optimization, Control and Games, Mathematical Data science, Graphs and Neural networks , and Scientific Computations . His work bridges theoretical mathematics with practical algorithms for machine learning, Bayesian inference, and optimal transport problems. His research explores geometric frameworks for probability spaces, including Wasserstein-2 metrics , Onsager gradient flows , and primal-dual hybrid gradient algorithms . Recent publications focus on accelerated sampling methods, mean field control systems, and novel applications of optimal transport in high-dimensional settings. 2022 : Air Force Office of Scientific Research YIP award for Transport Information Geometric Computations Key article trends include stochastic differential equations (37%), Wasserstein gradient flows (42%), Markov chain Monte Carlo (28%), and Hamilton-Jacobi-Bellman equations (33%). Subfields span accelerated optimization , nonlinear mobility metrics , generative modeling , and reaction-diffusion systems .