Professor Vitali Wachtel of Bielefeld University's Faculty of Mathematics specializes in advanced stochastic processes, probability theory, and their applications in mathematical modeling. Since 2021, he holds a W3 Professorship and serves as Principal Investigator in CRC 1283 'Taming uncertainty and profiting from randomness and low regularity in analysis, stochastics and their applications' since 2023. Chaired Examination Boards for Bachelor & Master Business Mathematics Member, Bielefeld Graduate School in Theoretical Sciences Research focus: Markov processes, random walks in cones, branching processes Research Trends: His recent work spans critical multitype branching in random environments (2025), asymptotic expansions for conditioned random walks (2024), and invariance principles for integrated processes. He explores connections between stochastic processes, combinatorial structures, and risk modeling with level-dependent premiums. Awards: Feodor Lynen Research Fellowship (2017), Alexander von Humboldt Foundation Teaching: Coordinates modules including 'Stochastic Processes' (24-M-PT-STP) and 'Introduction to Probability Theory' (24-B-EW-5). Active in curriculum development and academic governance through multiple university committees.
Samuel Herrmann is a Professor of Applied Mathematics at the University of Burgundy, France. He is a member of the Statistics, Probability, Optimization and Control team and an external member of the TOSCA project team at INRIA. His research focuses on stochastic processes, particularly asymptotic analysis of non-linear stochastic processes, large deviations, and stochastic resonance phenomena, with applications in climatology, biology, and financial modeling. Education: PhD in Mathematics (2001) - University of Burgundy Habilitation (2009) - Asymptotic analysis related to stochastic processes Research Interests: Stochastic differential equations and their numerical simulation Large deviation phenomena in stochastic processes Self-stabilizing diffusions and stochastic resonance First-passage and exit time problems for diffusions Applications in climatology, biology, and finance Scientific Contributions: Professor Herrmann has published extensively on stochastic processes, with over 50 peer-reviewed articles and a monograph on stochastic resonance. His work includes exact simulation methods for diffusion processes, studies on self-stabilizing systems, and theoretical contributions to large deviations theory. He has collaborated with leading researchers such as Peter Imkeller and David Peithmann. Awards and Recognition: Contributed to the encyclopedia of mathematical physics Co-authored the book "Stochastic Resonance: A Mathematical Approach in the Small Noise Limit" (2014) Teaching and Supervision: He teaches courses on stochastic processes and their simulation at both undergraduate and master's levels, including the Master in Turin program. He has supervised numerous PhD and master's students in stochastic processes and related fields.
Dr. Elliot Carr is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT), Faculty of Science. He holds a PhD in Mathematics from QUT and has been a faculty member since 2015, progressing from Lecturer to his current rank. His research and teaching focus on applied and computational mathematics, with strong interdisciplinary applications. Education: PhD in Mathematics, Queensland University of Technology, 2009–2012 Bachelor of Applied Science (Honours) in Mathematics, QUT, 2008 Bachelor of Mathematics, QUT, 2005–2007 Elliot Carr's research lies at the intersection of applied mathematics and real-world physical systems. His work centers on developing and analyzing mathematical models of advection, diffusion, and reaction processes, particularly in heterogeneous media. He employs both deterministic (PDE-based) and stochastic (random walk) frameworks, contributing to analytical solutions, multiscale modeling, surrogate models, and numerical methods such as finite volume and Newton-Krylov techniques. His research has been applied to diverse fields including groundwater contamination, drug delivery, heat transfer, and tumor spheroid modeling. The latest publications reflect a consistent focus on transport phenomena in complex geometries and heterogeneous environments. Key themes include dual-grid mapping for contaminant transport, analytical modeling of drug release from spherical capsules, thermal diffusivity in shell geometries, and stochastic models of biological systems. His methodological contributions span analytical, numerical, and statistical approaches, demonstrating versatility across applied mathematics. Scientific Awards and Recognitions: JH Michell Medal, ANZIAM (2022) ARC DECRA Fellowship (2015) QUT Outstanding Doctoral Thesis Award (2012) University Medal, QUT (2008) Dean’s Award for top graduate in both Honours and Bachelor programs Keynote and plenary speaker at major conferences including ANZIAM and Forum “Math-for-Industry” Dr. Carr actively supervises PhD and Masters students, with completed and ongoing projects on diffusive transport, tumor modeling, and sports analytics. He has secured competitive research funding, including an ARC Discovery Project on multiscale modeling. His teaching includes computational mathematics, linear algebra, and differential equations, with a focus on MATLAB-based implementation. He is a member of the Australian Mathematical Society (AustMS) and ANZIAM. Research Labs and Teams: While not explicitly tied to a named lab, Carr is part of the broader Applied Modelling and Computation research environment at QUT. He collaborates extensively with researchers such as Ian Turner, Matthew Simpson, and Chris Drovandi, contributing to interdisciplinary teams in mathematical biology, environmental modeling, and statistical computation.
Massimiliano Tamborrino is an Associate Professor in the Department of Statistics at the University of Warwick since August 2024. He holds a WIHEA Fellowship (2023-2026) and has organized the One World ABC Seminar (2020–present), focusing on approximate Bayesian computation (ABC) and simulation-based inference. He co-organizes the BioInference conference series, which explores mathematical modeling in biological systems. His research integrates stochastic processes, numerical methods, and statistical inference, with applications in neuroscience, biology, and parallel-in-time algorithms. Education: PhD in Probability Theory and Statistics from the University of Copenhagen (2013), supervised by Prof. Susanne Ditlevsen. Previously held roles include University Research Assistant at JKU Linz (2014–2019) and Postdoc at the University of Copenhagen (2012–2014). Research interests include stochastic processes (diffusions, point processes), parallel-in-time numerical schemes (PinT), and ABC methods. His work bridges stochastic numerics with computational statistics, particularly in neuroscience and biological systems. He has developed R packages for exact simulation of non-Gaussian processes (e.g., shot noise, OU processes). Awards: WIHEA Fellow (2023–2026). Active in grants, including leadership of the EPSRC-funded project on AI-informed decision-making using Decision Field Theory. Teaching responsibilities include ST232/ST233: Introduction to Mathematical Statistics for undergraduate students.
Dr. Tung-Lung Wu is Associate Professor of Statistics at Mississippi State University, specializing in applied probability and statistical methodology. His research develops analytical frameworks for boundary crossing problems, pattern distributions, and high-dimensional data. Research interests include first passage time calculations for stochastic processes, sequential analysis techniques in clinical trials, quality control applications of pattern distributions, and hypothesis testing for high-dimensional covariance structures. His work has applications in finance, manufacturing quality, and spatial epidemiology. Recent publications advance methods for scan statistics of Poisson processes, distribution-free control charts, and projected tests for covariance matrices. Analytical approaches include Markov chain imbedding techniques, random matrix projections, and adaptive scanning procedures. No awards, student advising, or laboratory affiliations are detailed in the source materials.
Professor Anatoly Zhigljavsky serves as Chair in Statistics and Honorary Professor at Cardiff University's School of Mathematics. He holds multiple administrative positions including membership in the Senior Management Committee, School Research Committee, School Management Board, School Learning and Teaching Committee, Board of Studies, and Subject panel. University: Cardiff University School: School of Mathematics Position: Chair in Statistics, Honorary Professor Professor Zhigljavsky earned his MSc from the University of St.Petersburg, Russia in 1976, followed by his PhD in 1981 and Habilitation in 1987, all from the same institution. His academic credentials reflect a strong foundation in mathematical statistics and theoretical probability. His research spans several interconnected domains in statistics and optimization. He is particularly renowned for his contributions to Time Series Analysis, where he has advanced Singular Spectrum Analysis (SSA) into a powerful technique for time series analysis, forecasting, and change-point detection. His work in Statistical Modelling in Market Research has resulted in numerous industry collaborations, while his research in Stochastic Global Optimization has provided theoretical insights into random search algorithms, especially in high-dimensional spaces. His investigations into Probabilistic Methods in Search and Number Theory have yielded novel approaches to discrete search problems including group testing with lies. Professor Zhigljavsky has also pioneered Dynamical system approaches for studying convergence of search algorithms, bridging continuous and discrete optimization methodologies. Analysis of Professor Zhigljavsky's recent publications (2021-2025) reveals an evolving research trajectory with increasing focus on high-dimensional statistical challenges, quantization theory, and the intersection of optimization with time series analysis. His work consistently demonstrates mathematical rigor combined with practical relevance, addressing computational challenges in large-scale data analysis. His collaborations span multiple institutions with researchers including Luc Pronzato, Jack Noonan, and Anatoly Pepelyshev. Scientific recognition includes: Constantin Caratheodory Prize in France (2019) Professor Zhigljavsky has secured substantial external funding including projects with Procter and Gamble on statistical modelling in Market Research (totaling approximately £200,000), projects with AcNielsen/BASES on consumer behaviour modeling (£40,000), and projects with GlaxoSmithKline on biopharmaceutical studies (£15,000) and environmental science (£10,000). His research has consistently demonstrated practical applications across multiple industries. As an active member of Cardiff University's Statistics research group, Centre for Optimisation and Its Applications, and Statistical Modelling Unit, Professor Zhigljavsky continues to influence both theoretical developments and practical applications in statistics and optimization.
Prof. Dr. Benedikt Jahnel is a Professor at the Institute of Mathematical Stochastics, Carl Friedrich Gauss Faculty, Technical University of Braunschweig, and Head of the Leibniz Junior Research Group on Probabilistic Methods for Dynamic Communication Networks. His research focuses on the modeling and analysis of spatially embedded systems with interacting random components, with applications in physics, epidemiology, and telecommunications. Education: PhD in Organismic and Evolutionary Biology from Ruhr University Bochum (2011), Diploma from Technical University of Berlin. Research interests: Uses tools from statistical mechanics and stochastic geometry to study phase transitions, percolation theory, and spatial random processes. Current projects investigate continuum percolation, interacting particle systems, and probabilistic methods for communication networks. Recent publications demonstrate strong emphasis on spatial stochastic processes and percolation theory, with applications to network connectivity and epidemic modeling. Trends include mathematical analysis of phase transitions in random environments and dynamics of communication networks. Scientific awards include leadership of Leibniz Junior Research Group, EURANDOM Ambassador appointment, and election to the board of the Probability and Statistics Group of the German Mathematical Society. Leads a research team including 3 postdocs and 3 predoctoral researchers. Current projects funded by DFG, ERC, and Math+ Cluster of Excellence. Heads the DYCOMNET research group at Weierstrass Institute Berlin, focusing on dynamic spatial random systems.
Charlie Smith is an Associate Professor in the Department of Statistics at North Carolina State University, affiliated with the Biomathematics Program. He holds a Ph.D. in Biophysics from the University of Chicago and B.S. in Physics from MIT. His research focuses on neurobiological stochastic processes, physiological models, and first passage time approximations. Smith has organized major conferences like the Gordon Research Conference on Theoretical Biology and Biomathematics (2000-2006). He teaches courses such as ST370 (Statistics for Engineers) and ST746 (Stochastic Processes), and has supervised numerous graduate students. His work spans interdisciplinary collaborations in toxicology, pharmacokinetics, and biomathematical modeling. Professional service includes directing the Biomathematics Graduate Program (2003-2005) and editorial roles in journals. Education: Ph.D. Biophysics (University of Chicago, 1979), M.S. Biophysics & Theoretical Biology (University of Chicago), B.S. Physics (MIT) Research: Stochastic modeling in neuroscience, neural diffusion models, and interdisciplinary applications in toxicology and pharmacokinetics Teaching: ST370, ST507, ST746, and graduate-level stochastic processes courses Professional Contributions: Conference chair (Gordon Research Conferences), program director roles, and advisory work in biomathematics
Sandra Pieraccini is a Full Professor in the Department of Mathematical Sciences "GL Lagrange" (DISMA) at the Politecnico di Torino, where she also serves as Deputy Director of the department, Contact Person for student orientation, and Coordinator of the basic subjects for first-year engineering programs. She is a member of the University Open Access Commission and actively contributes to academic governance. Her research interests include machine learning, numerical analysis, scientific computing, uncertainty quantification, and numerical optimization . She is a key member of the research group Numerical Analysis and Scientific Computing and leads the national research project FaReX (2023–2025) on reduced-order modeling and automatic learning. Her work integrates advanced numerical methods with AI techniques, particularly in modeling discrete fracture networks and fluid dynamics. The most recent publications reflect a strong trend in combining graph-informed neural networks , explainable AI , and meshless computational methods to solve complex problems in geophysics, fluid mechanics, and data science. Her research bridges applied mathematics with real-world engineering and environmental challenges. She is an active member of the scientific community, serving on the editorial boards of Journal of Machine Learning for Modeling and Computing and GEM , and participating in steering committees of UMI groups on AI and machine learning. She has also contributed to organizing major workshops and conferences. Sandra Pieraccini teaches across multiple programs, including doctoral courses in Mathematical Sciences and Aerospace Engineering, master’s courses in Mathematical Engineering and Data Science, and bachelor’s courses such as Linear Algebra and Problem Solving Lab. She is deeply involved in curriculum development and academic leadership.
Dr. Stasys Steišūnas serves as an Affiliated Scientist at the Smart Technologies Research Group within Vilnius University Institute of Data Science and Digital Technologies (VU DMSTI), formerly known as the Institute of Mathematics and Informatics (MII), located at Akademijos St. 4, Vilnius. His academic profile centers on theoretical research in probability and stochastic modeling without indication of teaching responsibilities. His primary research domains include Queueing Theory , Stochastic Processes , and Mathematical Modeling , with specialized contributions to open/multiphase queueing networks, Brownian motion dynamics, and message switching systems. Collaborative work with Saulius Minkevičius constitutes the majority of his publications, demonstrating sustained focus on deriving limit theorems and performance metrics for complex stochastic systems. Publication analysis reveals concentrated output between 1999-2012 across journals like Nonlinear Analysis: Modelling and Control and International Journal of Pure and Applied Mathematics , with recurring themes in heavy traffic approximations, sojourn time analysis, and departure process modeling. The absence of recent publications suggests potential shift in professional focus while maintaining institutional affiliation. As part of VU DMSTI's research infrastructure, Steišūnas contributes to Lithuania's mathematical research ecosystem through rigorous theoretical work in stochastic network analysis, though no laboratory-specific affiliations or grant details are documented in available sources.
University of Illinois Urbana-ChampaignUnited States
Partha Dey is an Associate Professor in the Department of Mathematics at the University of Illinois at Urbana-Champaign (UIUC), where he also serves as Director of the NetMath Program. He holds affiliations in both Mathematics and Statistics departments. His research focuses on Probability Theory and its intersections with Statistical Physics, emphasizing First/Last Passage Percolation, Random Growth Models, Stein’s Method, Spin Glasses, and Random Matrix Theory. Education: Ph.D. in Statistics from UC Berkeley (2010), supervised by Sourav Chatterjee and Steve Evans. Prior to UIUC, he was a Courant Instructor/Simons Fellow at NYU (2010-2013) and a Harrison Early-Career Assistant Professor at the University of Warwick (2013-2014). Undergraduate and Master’s studies at Indian Statistical Institute Kolkata (Mathematical Statistics & Probability). Research interests include analyzing stochastic processes in complex systems, with recent work on fluctuation phenomena in percolation models, spin glasses under external fields, and Stein’s method applications. His publications span high-impact journals like ALEA , Annals of Probability , and Communications in Mathematical Physics . Awards/Funding: No explicitly listed honors, though his positions suggest sustained academic recognition. Grants/Advising: No detailed grant info provided; no advisee names listed in texts. Labs/Teams: Leads NetMath Program, a distance-learning initiative in mathematics education. Collaborates widely on interdisciplinary projects in probability and statistical physics.
National Research Institute for Mathematics and Computer ScienceNetherlands
Udo Böhm is a researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI) in Amsterdam. His work bridges Bayesian statistics, computational psychology, and numerical mathematics through advanced modeling techniques. Research Focus : Bayesian inference, diffusion models, first-passage time analysis Technical Expertise : Numerical approximation of partial differential equations Collaborations : Extensive collaborations with psychologists and mathematicians His publications demonstrate interdisciplinary applications of machine learning in psychological modeling and mathematical problem-solving. Current work involves developing anytime-valid confidence sequences and improving computational methods for diffusion processes. Notable contributions include: Advancements in hierarchical diffusion decision models Efficient numerical algorithms for non-regular Fokker–Planck equations Foundational guidelines for Bayesian analysis in JASP
Dr. Libo Li is a Senior Lecturer at the School of Mathematics & Statistics, University of New South Wales. His research focuses on probability theory, stochastic processes, and mathematical finance. He holds a PhD from the University of Sydney and has undertaken postdoctoral positions at Ritsumeikan University and Universite d’Evry Val d’Essonne. Education: PhD, University of Sydney, Australia (2011) Postdoc, Ritsumeikan University, Japan (2013-2014) Postdoc, Universite d’Evry Val d’Essonne, France (2012) Research Interests: Dr. Li's work investigates mathematical finance applications, including backward stochastic differential equations (BSDEs), optimal stopping problems, default times, and numerical schemes for Lévy-driven stochastic differential equations. His research emphasizes theoretical rigor and computational methods. Recent Trends: His 2024-2025 publications analyze BSDEs with generalized drivers, positivity-preserving numerical methods for CEV processes, and defaultable American options. Earlier works (2022-2019) focus on honest times, parametrix methods, and Lévy-driven SDEs. Contact: Email: libo.li@unsw.edu.au
Martin Rainer Bladt is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen, focusing on Applied Probability and Insurance Mathematics. He works on statistical and stochastic modeling, particularly in Markov processes and risk theory. Research trends include Markov jump processes, phase-type distributions, survival analysis, and applications in insurance and financial risk modeling. His recent publications emphasize methodological developments in actuarial science, extreme value theory, and homogeneous approximation techniques for inhomogeneous processes.
Michael V. Boutsikas is an Associate Professor in the Department of Statistics and Insurance Science at the University of Piraeus , Greece. Since 2000 he has held progressively senior academic posts, beginning as Lecturer and becoming tenured Assistant Professor before his promotion to Associate Professor in 2016. He earned his Diploma (1995), M.Sc. (1998) and Ph.D. (2000) in Mathematics from the University of Athens, all awarded with distinction. His research lies at the intersection of applied probability, actuarial science and reliability theory . Core interests include risk models, stochastic dependence, probability metrics, extreme value theory, scan statistics, urn models and compound Poisson approximation . These themes are unified by the aim of quantifying and bounding the behaviour of complex stochastic systems. Across more than twenty-five peer-reviewed articles published in leading journals such as The Annals of Applied Probability , Journal of Applied Probability , Bernoulli , Insurance: Mathematics and Economics and Naval Research Logistics , a clear trajectory emerges: developing sharp approximations and limit theorems for rare events, system reliability, and aggregate claims in risk processes. While the text does not list formal awards, it notes that his work has attracted at least 150 citations from international journals and monographs and that he maintains an h-index of 9 . He is an active referee for more than twenty journals and a reviewer for Mathematical Reviews . Teaching duties span undergraduate courses ( Risk Management, Stochastic Processes, Simulation ) and postgraduate offerings ( Simulation Methods, Extreme Price Theory, Stochastic Financial Models ). He has also authored sixteen sets of detailed teaching notes and lecture notes covering reliability, risk management and statistical software. No specific doctoral students or grant details are provided, but his long-standing presence and prolific output indicate sustained supervisory and research funding activity within the Department of Statistics and Insurance Science.
