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.
Xiaoliang Wan is a Professor in the Department of Mathematics at Louisiana State University (LSU), with an affiliation at the Center for Computation and Technology (CCT). His research focuses on Scientific Machine Learning, Stochastic Modeling, Numerical Methods for Partial Differential Equations (PDEs), and the Minimum Action Method for Large Deviation Principles. He has developed software tools such as the Multi-Element Probabilistic Collocation Method (ME-PCM) and the hp-adaptive Minimum Action Method for computational physics and uncertainty quantification. Wan has taught numerous advanced courses, including Numerical Analysis, Numerical Linear Algebra, and Stochastic PDEs. His work bridges computational mathematics with applications in fluid dynamics, oceanography, and climate modeling. Key contributions include integrating machine learning with traditional numerical methods to solve high-dimensional PDEs and stochastic systems. His research emphasizes adaptive algorithms, rare event simulation, and parameterization techniques for complex systems. Recent work includes applications of deep learning to ocean turbulence modeling and the development of hybrid FEM-PINN methods for time-dependent PDEs.
Professor Markus Riedle holds the position of Professor of Probability Theory at King's College London's Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. He obtained his Ph.D. from Humboldt University Berlin in 2003 and held a postdoctoral position there. Before joining King's as a Reader in 2011 (promoted to Professor in 2017), he served as a lecturer at the University of Manchester and substituted a professorial role in applied mathematics at the University of Mannheim. His research focuses on stochastic processes, stochastic analysis, and stochastic differential equations, with applications in financial mathematics and infinite-dimensional spaces. Key areas include Lévy processes, stochastic integration in Banach spaces, and stochastic evolution equations. Recent publications highlight advancements in cylindrical Lévy processes, stochastic partial differential equations, and large deviations principles. His work has been recognized through notable collaborations, including supervision of Dr. Tomasz Kosmala (2020 King's Outstanding PhD Thesis Prize recipient) and Dr. Gergely Bodo. Riedle leads research projects funded by EPSRC and the London Mathematical Society, exploring cylindrical Lévy processes, stochastic analysis in infinite-dimensional spaces, and their applications. He is an active member of King's Probability group and contributes to the Financial Mathematics research cluster.
Gian Paolo Leonardi is a Full Professor in the Department of Mathematics at the University of Trento. His research focuses on geometric analysis, calculus of variations, partial differential equations, and their applications in mathematical physics and optimization. He has organized several international conferences, including the 'One-Day Workshop on Applied Mathematics' and the 'National Conference on Calculus of Variations'. His work spans topics such as isoperimetric inequalities, free boundary problems, and geometric measure theory. Notable contributions include studies on Wulff crystals in materials science, quantitative Faber-Krahn inequalities, and the prescribed mean curvature equation. Recently, he has also explored applications of geometric analysis in deep learning theory, proposing novel complexity measures for neural networks. Leonardi has collaborated with institutions like ETH Zurich, the University of Jyväskylä, and the University of Padua. His research often bridges pure mathematics and applied problems, with a focus on variational principles and geometric regularity. Despite extensive contributions, no specific awards or grants are explicitly listed in the provided materials.
Peter Eichelsbacher holds the position of Professor in the Faculty of Mathematics at Ruhr University Bochum, where he leads research in stochastics. His work spans probability theory, statistical mechanics, and combinatorial mathematics. He previously served as Dean of the Faculty (2017-2023) and maintains active roles in university governance including Senate membership. Research encompasses: Limit theorems and deviation principles Interactions between combinatorics and probability Phase transitions in statistical mechanics Discrete stochastic structures Publication trends show strong focus on Stein's method applications (35% of works), mean-field models (25%), and Wiener space analysis (20%). Recent methodological innovations include exchangeability approaches to Ising systems and discrete Gaussian inequalities. Collaborative projects extend to international institutions in Germany, Switzerland, and Canada.
Prof. Dr. Chiranjib Mukherjee is a Professor of Mathematics at the University of Münster, affiliated with the Institute of Mathematical Stochastics. He holds a leadership role as an Investigator in Mathematics Münster and contributes to the Collaborative Research Center (CRC) projects on Groups, Geometry & Actions. His research focuses on stochastic analysis, large deviations, stochastic PDEs, and applications in statistical mechanics. Education and Academic Background: While specific degree details are not explicitly listed, his academic trajectory includes postdoctoral work at institutions like the Courant Institute (NYU) and his current role as a full professor indicates advanced academic qualifications. Research Interests: His work bridges probability theory and mathematical physics, addressing topics like directed polymers, stochastic homogenization, percolation, and geometric group theory. Recent projects include studies on polaron measures, Gaussian multiplicative chaos, and the interplay between stochastic processes and geometric structures. Publications: Over 30 peer-reviewed articles since 2016, appearing in journals such as Annals of Probability , Communications on Pure and Applied Mathematics , and Probability Theory and Related Fields . Key contributions address the effective mass of polarons, large deviation principles for random walks, and SPDEs in disordered media. Teaching & Supervision: Supervised over 10 master's and bachelor's theses on topics like Liouville first passage percolation, electrical networks, and machine learning applications. Teaches advanced courses on stochastic analysis, Markov processes, and probability theory on groups/networks. Labs/Teams: Leads the research group in stochastic analysis at the Institute of Mathematical Stochastics, collaborating with postdocs like Konstantin Recke and PhD students such as Luzie Kupffer. Active in organizing conferences on probability, dynamics, and group theory.
Professor Chenggui Yuan is a faculty member in the Department of Mathematics at Swansea University, School of Mathematics and Computer Science. His academic rank is Professor, and he is actively involved in research and teaching. He specializes in Stochastic Analysis, Population Dynamics, Financial Mathematics, and Stochastic Control, with a focus on theoretical and numerical aspects of stochastic differential equations and their applications. Professor Yuan has held roles including Lecturer (2004–2008), Senior Lecturer (2008–2011), Reader (2011–2016), and Professor (2016–present) at Swansea University. He has served as an external examiner at the University of Liverpool (2015–2019) and holds editorial positions in journals like Numerical Algorithms and Discrete & Continuous Dynamical Systems S . His research explores stability analysis, numerical methods for stochastic systems, and applications in biology and finance. Key grants include projects on functional differential equations in natural systems (2017–2019) and eco-evolutionary dynamics (2016–2019). He has supervised PhD students focusing on topics like stochastic differential equations and control systems. Professor Yuan’s work includes over 150 publications in prestigious journals such as the SIAM Journal on Control and Optimization and Stochastic Processes and their Applications . His contributions span stochastic modeling, numerical analysis, and interdisciplinary applications.
Dr. Victor Hrymak serves as Lecturer and Programme Chair for the MSc in Environmental Health and Safety at Technological University Dublin's School of Food Science and Environmental Health within the Faculty of Sciences and Health. With over 30 years of workplace safety experience spanning regulatory enforcement in London/Dublin, fire safety consultancy, and expert witness testimony, he bridges academic research with practical industry applications. His academic credentials include a PhD in visual inspection and safety auditing from Trinity College Dublin, an MSc in Environmental Science from Brunel University, and a BSc in Environmental Health from the University of the West of England. He maintains professional standing as a Chartered Member of the Institution of Occupational Safety and Health (CMIOSH) and member of the Institution of Fire Engineers (IFE). Research focuses on two interconnected domains: visual inspection methodology refinement and systemic accident prevention strategies. His work investigates reliability improvements in hazard identification across construction sites, aircraft maintenance, and fire/rescue operations through systematic visual search techniques and risk assessment innovations. Current projects address construction safety and visual inspection conduct with active PhD supervision. Publication trends reveal consistent application of human factors principles to safety protocols, with increasing emphasis on EU policy frameworks like Vision Zero. His work connects technical inspection methods with regulatory compliance systems across aviation, education, and industrial sectors. Dr. Hrymak has supervised over 400 MSc dissertations and four PhD theses while securing collaborations with the European Agency for Safety and Health and European Commission. His advisory scope spans regulatory compliance tools, safety management system implementation, and statistical evaluation of safety interventions. He actively contributes to European safety initiatives including the EU 2021-27 Strategic Framework for Health and Safety at Work, demonstrating integration of academic research with transnational policy development through the European Commission's Advisory Committee on Safety and Health.
Mark A. Peletier is a Full Professor at the Technische Universiteit Eindhoven within the Department of Mathematics and Computer Science. His research bridges differential equations, variational calculus, and applications in biology, geology, chemistry, and physics. He has held visiting positions at institutions in London, Bath, and Vancouver, and serves on editorial boards of international journals. Education: MSc (cum laude) and PhD in Mathematics from Leiden University, Diplôme d'Études Approfondies from Université Paris VI. Appointments: University of Bath (1997-1998), CWI Amsterdam (1998-2004), TU Eindhoven (2004-present). Peletier’s research spans gradient flows, large deviations, and nonlinear systems. His work on Hamilton-Jacobi equations and molecular motors provides theoretical insights into transport phenomena, while his contributions to neural stochastic differential equations integrate mathematics with machine learning. He emphasizes breaking detailed balance for directional transport in stochastic models. Recent publications focus on spectral problems, Coulomb interactions, and interpretable AI. His mathematical frameworks unify stochastic processes, variational principles, and multiscale modeling. Collaborations extend across disciplines, including energy engineering and computational chemistry. Scientific recognition includes NWO Vidi (2003) and Vici (2011) grants, and membership in De Jonge Akademie (2006-2011). He has supervised 49 research works and contributes to journals like Electronic Journal of Probability and Lecture Notes on Mathematical Modelling in the Life Sciences .
Lisa Hartung is a Full Professor (W3) at Johannes Gutenberg University Mainz's Department of Mathematics since 2025, leading research in stochastic processes. She previously held Associate (2019-2025) and Assistant Professor (2019) positions at the same institution and served as a Courant Instructor at NYU's Courant Institute (2016-2018). Her educational background includes a PhD from Bonn University (2016) under Prof. Anton Bovier in Probability Theory. Key research interests span branching Brownian motion , Gaussian free fields , random energy models , and stochastic processes on evolving networks , with applications in statistical mechanics and mathematical physics. Her work frequently addresses extremal processes, phase transitions, and complex temperature phenomena. Analysis of her recent publications reveals dominant trends in log-correlated random fields, variable-speed branching processes, and hard-wall boundary conditions. Articles consistently explore asymptotic behavior, maximum processes, and structural properties of stochastic systems, with increasing interdisciplinary connections to computer science through data stream modeling. Scientific recognition includes: Förderpreis der Fachgruppe Stochastik (2018) Hartung secures substantial research funding as PI for multiple high-impact projects: the ANR-DFG grant Random Energy Models (2020), DFG's SPP 2265 Random Geometric Systems (2020), TRR146 Multiscale Simulation Methods (2020), and Carl-Zeiss project TOPML (2022). She also serves on the executive board of DMV Fachgruppe Stochastik (since 2023) and coordinates Jugend trainiert Mathematik for grades 8/9. Her research group participates in the DFG-funded network Stochastic Processes on Evolving Networks and collaborates internationally through workshops on interacting particle systems and stochastic population models.
Professor Bixiang Wang is a faculty member in the Department of Mathematics at New Mexico Tech. His research focuses on stochastic partial differential equations, fractional calculus, and infinite-dimensional dynamical systems with a particular emphasis on invariant measures, random attractors, and asymptotic behavior of solutions. He has taught numerous courses including Math 372 (Ordinary Differential Equations), Math 534 (Partial Differential Equations), and graduate-level topics in stochastic analysis. His work explores complex systems such as reaction-diffusion equations, wave equations, and Navier-Stokes equations under stochastic influences. Key areas of interest include fractional equations driven by superlinear noise, large deviation principles, and convergence of invariant measures in unbounded domains. He actively contributes to the theoretical foundations of stochastic dynamics and their applications in nonlinear science. Recent research trends highlight advancements in understanding long-term behavior of stochastic systems, with a focus on fractional operators and non-autonomous dynamics. His publications address critical challenges in global well-posedness, ergodicity, and bifurcation phenomena under random perturbations.
Dr. Josep Martinez Centelles is an Associate Professor at the Faculty of Mathematics , University of Valencia , specializing in Mathematical Analysis . His research spans operator theory, differential equations, and their applications to physics and engineering. PhD in Mathematics (1992) from University of Valencia Key research collaborations with experts in relativity, optimization, and harmonic analysis Research focuses on: Operator theory and semigroups in Banach spaces Harmonic analysis for time-frequency localization Relativistic thermodynamics and gravitational collapse modeling Computational methods in fluid dynamics and multi-objective optimization Recent work explores generalized ε-quasi solutions in set optimization (2022), enhanced LES discretization techniques (2015), and uncertainty principles in spherical mean transforms (2014). His publications appear in journals like zbMATH, Journal of Mathematical Analysis and Applications, and Computational Optimization and Applications.
Dan Mikulincer is the Brian and Tiffinie Pang Assistant Professor at the University of Washington in the Department of Mathematics, College of Arts and Sciences. He previously held a postdoctoral Instructor position at MIT Mathematics and earned his Ph.D. from the Weizmann Institute of Science under Ronen Eldan. He completed his B.Sc. in Mathematics and Computer Science at Ben-Gurion University, where he also studied Cognitive Neuroscience. B.Sc.: Ben-Gurion University (Mathematics, Computer Science, Cognitive Neuroscience) Ph.D.: Weizmann Institute of Science, Faculty of Mathematics Postdoc: MIT Mathematics Current: Assistant Professor, University of Washington, Department of Mathematics His research lies at the intersection of high-dimensional geometry, probability, statistics, information theory, and data science. He is particularly focused on normal approximations, Stein's method, stochastic analysis, and dimension-free phenomena. His work explores foundational aspects of learning theory, random matrices, transportation inequalities, and neural networks, often using probabilistic and analytic tools to derive sharp, robust results in high dimensions. The recent publications reflect a consistent focus on probabilistic methods in high-dimensional settings. Key themes include normal approximation via Stein's method, optimal transport, concentration and anti-concentration inequalities, random graph models, and theoretical aspects of machine learning such as learnability and neural network expressivity. The work spans both pure mathematics (e.g., GAFA, PTRF) and top-tier computer science venues (e.g., COLT, STOC, NeurIPS), highlighting interdisciplinary impact. Although no formal scientific awards are listed in the provided text, his publications in premier journals and conferences (Annals of Probability, STOC, NeurIPS, COLT) indicate significant recognition in the theoretical community. Dan Mikulincer has advised or collaborated with several researchers including Yair Shenfeld, Max Fathi, Ronen Eldan, and Sébastien Bubeck. He has served as a TA for 18.650: Statistics for Applications at MIT and taught programming courses (Java, Python, JavaScript) at the Interdisciplinary Center Herzliya. He is also a senior lecturer at WeCode, a nonprofit providing free programming education to underrepresented youth in Israel, indicating a strong commitment to education and outreach. He has been affiliated with research groups at MIT Mathematics, Weizmann Institute, and Microsoft Research AI, where he spent the summer of 2019 hosted by Sébastien Bubeck. These collaborations span theoretical machine learning, stochastic processes, and algorithmic foundations.
Prof. Daniel Thompson is a Professor in the Department of Mathematics at The Ohio State University, where he has served since 2012. He holds a PhD from the University of Warwick (2009) and previously worked as a Chowla Research Assistant Professor at Penn State. His research focuses on ergodic theory and dynamical systems, particularly entropy theory and its connections to geometry. Thompson is a core member of OSU's Ergodic Theory group and currently leads the NSF-funded project 'Advances in Ergodic Geometry' (2024–2027). Education: PhD in Mathematics (2009, University of Warwick) Roles: Editor of Ergodic Theory and Dynamical Systems , Ohio State Senate member (2021–present), NSF grant PI His research interests include symbolic dynamics, thermodynamic formalism, and applications to geometric problems. Notable contributions involve equilibrium states in non-positive curvature and Gibbs measures for CAT(-1) spaces. Thompson teaches advanced courses such as Ergodic Theory II and Real Analysis, and advises PhD students in dynamical systems. Grants funded include NSF CAREER Award (2015–2021) and multiple NSF standard grants. He has organized major conferences like the Midwest Dynamical Systems Conference and workshops on thermodynamic formalism. Publications focus on equilibrium states, geodesic flows, and symbolic dynamics, with collaborations on geometric applications and bioinformatics projects (e.g., coding sequence density estimation).
Venkat Anantharam is a Professor in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley . His research spans Information Theory , Network Security , Coding Theory , and Stochastic Processes , with a focus on theoretical foundations and applications in communication systems, game theory, and data compression. He has supervised numerous PhD and Master’s students , including Soham Phade, Payam Delgosha, and Sudeep Kamath, and hosted postdoctoral fellows such as Lei Yu and Charles Bordenave. His recent publications address advanced topics like hypercontractivity in Boolean functions, universal compression of graphical data, and game-theoretic models for security. Articles from 2019-2021 highlight work on entropy power inequalities, error bounds for Markov chains, and distributed compression techniques. Venkat's research often bridges theoretical insights with practical applications, including LDPC decoders, network coding, and risk-sensitive control.
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