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Mira SchedensackView profile

Assistant Professor

• Numerical Analysis
• Finite Element Methods
• Partial Differential Equations
• +5 more

Jun.-Prof. Dr. Mira Schedensack is a faculty member at the University of Leipzig in the Faculty of Mathematics and Computer Science , specifically within the Mathematisches Institut . Her research focuses on Numerical Analysis of partial differential equations (PDEs) with emphasis on Finite Element Methods , A Posteriori Error Analysis , Adaptive Mesh Refinement , and Gradient Elasticity . She has led projects like the DFG-funded work on Robust and Efficient Discretizations in Solid Mechanics and contributes to applications in mechanics and physics. Research Interests : Numerical methods for higher-order PDEs, non-conforming finite elements, Helmholtz decompositions, and gradient elasticity. Teaching : Regularly offers courses like Numerik 1 (Fundamentals), Numerik-Praktikum (practical implementation), and specialized topics in finite element analysis. Her recent publications address challenges in discretizing Reissner-Mindlin plates, mixed formulations for gradient elasticity, and adaptive methods for Stokes and convection-diffusion equations. She co-organizes the NA-LaB Seminar (Numerical Analysis in Leipzig and Berlin) and participates in collaborative research initiatives like the DFG SPP 1748 program.

• Algebra
• Lie Algebras
• Homological Algebra
• +4 more

Salvatore SICILIANO is a Full Professor at the Department of Mathematics and Physics 'Ennio De Giorgi' of the University of Salento. He holds a PhD in Mathematics (2004) and has been qualified for Full Professor since 2017. His research focuses on algebraic structures including modular Lie algebras, enveloping algebras, and representation theory. Education: Graduated summa cum laude in Mathematics from the University of Lecce (110/110), followed by a PhD in Mathematics (2004). Research Interests: Explores advanced algebraic topics such as homological algebra, group algebras, and Poisson algebras. Collaborates internationally with research groups in over 10 countries. Roles: President of the Mathematics Degree Course, member of the Quality Assurance Group, and coordinator of PRIN projects. Supervised PhD students and directed research grants. Teaching: Currently teaches Algebra II and Higher Algebra at both undergraduate and master’s levels.

Russell C. HardieView profile

Professor

• Digital Signal Processing
• Image Processing
• Machine Learning
• +3 more

Russell C. Hardie is a full-time Professor at the University of Dayton , holding positions in the Department of Electrical and Computer Engineering with joint appointments in Electro-Optics and Photonics and Bioengineering . His academic journey began with a B.S. in Engineering Science from Loyola College (1988), followed by M.S. and Ph.D. in Electrical Engineering from the University of Delaware (1990, 1992). Prior to joining the University of Dayton in 1993, he served as a Senior Scientist at Earth Satellite Corporation (now MDA Federal). Research Interests : Digital signal/image processing, medical imaging, super-resolution techniques, hyperspectral/infrared imaging, pattern recognition Key Awards : 2006 Alumni Award in Teaching (University of Dayton) 1998 Rudolf Kingslake Medal (SPIE) 1999 School of Engineering Excellence in Teaching 2002 IEEE Professor of the Year 1997 Epsilon Delta Tau Engineering Professor of the Year Recent Work : Focuses on machine learning applications for medical imaging (lung segmentation, nodule detection), atmospheric turbulence mitigation, and hyperspectral data analysis. His 15 most recent publications span topics from zero-shot chest X-ray analysis to methane plume detection and turbulence-corrected imaging systems. Contact: rhardie1@udayton.edu

Yakov Berchenko-KoganView profile

Assistant Professor

• Numerical Analysis
• Differential Geometry
• Finite Element Methods
• +2 more

Yakov Berchenko-Kogan is an Assistant Professor in the Department of Mathematics and Systems Engineering at Florida Institute of Technology (FIT), part of the College of Engineering and Science. He holds a Ph.D. in Mathematics from MIT (2016) and a B.S. in Mathematics from Caltech (2011), with a minor in Control and Dynamical Systems. His research focuses on developing high-order, structure-preserving numerical methods for problems in differential geometry and geometric analysis, particularly within the framework of finite element exterior calculus. He has held postdoctoral positions at institutions including Pennsylvania State University, the University of Hawaii at Manoa, and Washington University in St. Louis. His expertise spans numerical analysis, differential geometry, and computational mathematics, with a strong emphasis on geometric numerical methods and their applications to geometric PDEs. He actively engages in academic outreach, encouraging undergraduate and graduate students to pursue capstone projects or research collaborations. His work has been published in high-impact journals such as Foundations of Computational Mathematics , Geometriae Dedicata , and Journal of Nonlinear Science . Berchenko-Kogan’s research interests include finite element methods, geometric decomposition techniques, and the numerical approximation of geometric objects like the Levi-Civita connection and mean curvature flow self-shrinkers. He also explores structure-preserving algorithms for equations in mathematical physics, such as Maxwell’s and Yang-Mills equations.

• Actuarial Mathematics
• Risk Theory
• Ruin Theory
• +5 more

Chadjiconstantinidis Stathis is a Professor at the Department of Statistics and Insurance Science, University of Piraeus. He earned his Doctorate in Statistics (1990) and Diploma in Mathematics (1985) from Aristotle University of Thessaloniki. His academic roles include undergraduate and postgraduate teaching across multiple institutions including the University of Athens and Athens University of Economics and Business. Research Focus: His work spans actuarial mathematics, risk theory, and statistical optimization. Primary domains include: Ruin theory and bankruptcy modeling in insurance contexts Collective risk models with dependencies Optimal experimental designs for statistical efficiency Reliability theory and stochastic pattern analysis Publications: His 15 most recent articles show consistent focus on risk modeling (compound processes, ruin probabilities) and statistical design optimization (D-optimal cyclic designs). Actuarial mathematics dominates newer works (2005–2013), while earlier publications emphasize experimental design combinatorics. Academic Leadership: Department President (2007–2009) Director of Master’s Program in Actuarial Science & Risk Management (2007–2011) Deputy Chairman of Actuary Licensing Committee (Ministry of Finance) Senate Member at University of Piraeus (1997–1998, 2007–2009) Doctoral Supervision: Mentored dissertations on risk theory and statistics pedagogy. Current advisees not specified.

• Mathematical Analysis
• Geometric Measure Theory
• Analysis on Metric Spaces
• +3 more

Gareth Speight is an Associate Professor in the Department of Mathematical Sciences at the University of Cincinnati. His research focuses on mathematical analysis with geometric applications, particularly real analysis, geometric measure theory, analysis on metric spaces, and sub-Riemannian geometry. He has been supported by a National Science Foundation grant (#2348715) from 2024-2027, investigating differentiability in Carnot groups and metric measure spaces. He co-organizes the Ohio River Analysis Meeting and leads the UC Analysis Seminar. Speight's educational background includes a PhD from the University of Warwick (2013), supervised by David Preiss, and a postdoctoral fellowship at the Scuola Normale Superiore di Pisa under Luigi Ambrosio. His work explores porosity, differentiability, and functional analytic structures in non-Euclidean settings. He has advised PhD students Marco Capolli (2020) and Hyogo Shibahara (2024). His research highlights include studies on universal differentiability sets, Lusin approximation, and Whitney extension theorems in Carnot groups. He has organized major conferences like the 11th Ohio River Analysis Meeting (2022) and contributed to journals like Inventiones Mathematicae and Advances in Mathematics .

Antonio DE NICOLAView profile

Associate Professor

• Differential Geometry
• Geometry
• Topology
• +3 more

Antonio DE NICOLA is an Associate Professor in the Department of Mathematics at the University of Salerno, Italy. His primary research interests include Differential Geometry, focusing on Sasakian, Vaisman, and related geometric structures such as Poisson and Jacobi manifolds. He is a member of the Geometry group at the University of Salerno. His work explores topics like the geometry and topology of Sasakian and locally conformally Kähler (LCK) manifolds, nilmanifolds, and formality properties. He has contributed to understanding Hard Lefschetz theorems in various geometric contexts and the classification of nearly Sasakian and cosymplectic manifolds. His publications span high-impact journals such as *International Mathematics Research Notices*, *Annals of Global Analysis and Geometry*, and *Transactions of the American Mathematical Society*. He collaborates frequently with researchers like Beniamino Cappelletti-Montano and Ilya Yudin. His office is located at the Fisciano Campus, Building F2, Room 034. No scientific awards are explicitly mentioned in the provided materials. His research activities include conference talks at institutions like the University of La Laguna and the University of Oporto, focusing on geometric structures and their topological implications. Antonio DE NICOLA advises no listed students and has no documented grants in the text. He actively contributes to the Geometry research community through his publications and collaborations.

Ana BalibanuView profile

Assistant Professor

• Geometric Representation Theory
• Algebraic Groups
• Symplectic Geometry
• +3 more

Ana Balibanu is a tenure-track Assistant Professor at Louisiana State University's Department of Mathematics. Previously, she was a Benjamin Peirce Fellow at Harvard University and held an NSF Postdoctoral Fellowship. Her research focuses on geometric representation theory, algebraic groups, and their connections to symplectic and Poisson geometry. Education: Ph.D. in Mathematics, University of Chicago (Advisor: Victor Ginzburg) Bachelor's degree, California Institute of Technology (Caltech) Research Interests: Ana explores intersections of algebraic geometry, representation theory, and symplectic/Poisson structures. Her work emphasizes algebraic groups and their geometric manifestations, supported by NSF Grant DMS-2401514. Awards: NSF Postdoctoral Fellowship Grants: Current funding includes NSF Grant DMS-2401514 for her research in geometric representation theory. Contact: ana@math.lsu.edu , Lockett 322, Baton Rouge, LA 70803

Elizabeth StojanovskiView profile

Senior Lecturer

• Biostatistics
• Longitudinal Modelling
• Multivariate Statistics
• +6 more

Dr. Elizabeth Stojanovski is a Senior Lecturer at the University of Newcastle , affiliated with the School of Information and Physical Sciences and the Priority Research Centre for Computer-Assisted Research Mathematics and its Applications (CARMA) . She leads the Mathematics Education Research Group and manages the statistical consulting unit, serving industry, academics, and postgraduate students. With a PhD in Statistics from the University of Newcastle, she specializes in Biostatistics , Longitudinal Modelling , and Multivariate Statistics . Education : PhD (University of Newcastle), Bachelor of Mathematics (Honours), Bachelor of Mathematics Professional Roles : Senior Lecturer (2007–present), Research Academic (2004–2006) Memberships : Member of the Australian Statistics Society Her research spans health and education , focusing on longitudinal relationships between life events and physical/mental health. She has developed Bayesian frameworks , longitudinal models , and meta-analytic techniques for applications in tobacco control, cardiovascular disease, cancer treatment, and mathematics education. Her work includes collaborations with Eric Beh on Nobel Prize data analysis, Manohar Garg on lipid-lowering studies, and Suzanne Snodgrass on physiotherapy biomechanics. Recent publications highlight her expertise in correspondence analysis for educational and scientific data, multivariate assessment of diabetes risk , and reinforcement learning for optimization. She has supervised 15 PhD/Masters students and peer-reviewed for journals and grants. Her grants total $496,207 , including projects on statistical literacy and phytosterol efficacy. Scientific Awards : 2001 "Temporal association between life events and health" Elizabeth's teaching spans theoretical and applied statistics, with a focus on psychology, health, and business applications. She has also contributed to forensic science through cut mark analysis on bone and policy research on tobacco investments in pension funds. Her leadership in CARMA and statistical consulting underscores her interdisciplinary impact.

Matthias WinkelView profile

Lecturer

• Probability Theory
• Stochastic Processes
• Random Trees
• +3 more

Matthias Winkel is a Lecturer in Mathematics at Brasenose College and holds a Departmental Lecturership at the Department of Statistics, University of Oxford. He is also a member of the Probability Group and associated with research groups in Stochastic Analysis and Combinatorics. Education: M.Sc. (Manchester, 1998) Diplom (Münster, 1999) Ph.D. (Paris, 2001) Research Interests: Random trees and forests, branching processes, Lévy processes Exchangeability, random partitions, hierarchies Stochastic models in mathematical biology and finance Advising & Collaborations: Current PhD students: Matthew Buckland, David Geldbach (joint with Julien Berestycki) Former students include Almut Veraart, Di Kuang, Bo Chen Collaborators: Jim Pitman, Thomas Duquesne, Hui He, Andreas Kyprianou Research Contributions: Focuses on continuum random trees, fragmentation processes, and applications of Lévy processes. Key work includes Aldous diffusion models, interval partition evolutions, and tree-valued stochastic processes.

• Integrable Systems
• Nonlinear Differential Equations
• Difference Equations
• +3 more

Dr. Peter Van Der Kamp is a Senior Lecturer in Mathematics at La Trobe University's School of Mathematics & Statistics. His research focuses on integrable systems, particularly algebraic and geometric properties of nonlinear differential and difference equations. He has collaborated extensively on projects involving Poisson algebras, Lotka-Volterra systems, and discrete integrable equations. Education: Doctorandus (Theoretical Physics) from Vrije Universiteit (VU Amsterdam), Netherlands PhD (Mathematics) from Vrije Universiteit (VU Amsterdam), Netherlands Research Interests: Dr. Van Der Kamp's work spans integrable systems, with emphasis on: Development of discrete integrable equations Analysis of Lotka-Volterra systems and their superintegrability Study of Darboux polynomials and preserved measures Exploration of boundary conditions in quad equations His research often intersects nonlinear dynamics, algebraic geometry, and mathematical physics. Publications Trends: Recent articles highlight advancements in: Quadratic Poisson algebras and Lambert W-function solutions Trilinear/quadrilinear lattice equations Superintegrable tree-systems and their discretizations New integrable maps in R^4 Collaborations with experts like Quispel and Nijhoff are central to his work. Grants: Australian Research Council (ARC) Discovery Project: 'Discrete integrable systems' (2011–2015) Labs/Teams: Active contributor to the La Trobe University Mathematics group, focusing on discrete integrability and nonlinear systems research.

Chinmoy BhattacharjeeView profile

Professor

• Stochastic Geometry
• Distributional Approximations
• Stein's Method
• +4 more

Prof. Chinmoy Bhattacharjee is a Professor of Mathematical Stochastics at the University of Hamburg's Department of Mathematics, part of the Faculty of Mathematics, Computer Science and Natural Sciences. His research focuses on stochastic geometry, distributional approximations via Stein's method, stochastic analysis, random graphs, continuum percolation, and nonparametric inference. He holds a PhD from the University of Southern California and previously held postdoctoral positions at the University of Luxembourg and University of Bern. His work is supported by the German Research Foundation (DFG) through Project No. 531540467. Education: PhD in Applied Mathematics, University of Southern California (2013–2018) M.Stat. and B.Stat. (Hons.), Indian Statistical Institute, Kolkata (2008–2013) Teaching: Recent courses include Stochastic Processes, Probability and Statistics, and seminars on percolation theory and probabilistic methods. Research Highlights: Prof. Bhattacharjee's work bridges stochastic geometry and Stein's method, with applications in spatial random structures, non-Gaussian limit theory, and noise sensitivity in percolation models. Key contributions include Gaussian and Dickman approximation techniques, spectral analysis of Poisson functionals, and statistical applications in random forests. Upcoming Events: Co-organizing session on Spatial Stochastics at German Probability and Statistics Days (2025) Invited talks at Aarhus University and workshops in Germany/Croatia (2025)

Fabrizio LeisenView profile

Professor

• Bayesian Inference
• Bayesian Nonparametric Methods
• Objective Bayesian Analysis
• +3 more

Fabrizio Leisen is a Professor of Statistics at King’s College London, Department of Mathematics, within the Faculty of Natural, Mathematical & Engineering Sciences. Previously, he held positions at the University of Nottingham (Professor), University of Kent (Reader), and institutions in Spain and Italy. He earned a PhD in Mathematics from the Università di Modena e Reggio Emilia, specializing in Probability. His research focuses on Bayesian inference, nonparametric methods, objective Bayesian analysis, and foundational statistical theory, with notable contributions to predictive constructions, knockoff procedures, and stochastic processes. He serves as an Associate Editor for Bayesian Analysis , Statistics and Probability Letters , and Statistical Methods and Applications . Leisen’s work bridges theoretical and applied statistics, emphasizing Bayesian nonparametric priors for complex data structures and model selection. His recent articles address survival analysis, prior elicitation without subjective inputs, and algorithmic advancements in Bayesian computation. He collaborates globally, including co-supervising a PhD student at the Università di Bologna. His teaching includes Computational Statistics and Probability and Statistics Skills sessions. Research interests span Bayesian foundations, model selection via knockoffs, stochastic processes, and interdisciplinary applications like genomic and biostatistical analysis. His lab is affiliated with King’s Statistics group, focusing on MCMC methods, Bayesian nonparametrics, and experimental design.

Christopher BattyView profile

Associate Professor

• Computer Graphics
• Scientific Computing
• Physics-based Simulation
• +6 more

Christopher Batty is an Associate Professor and Director of Infrastructure at the University of Waterloo's Department of Computer Science. His research focuses on computer graphics and scientific computing, with an emphasis on physics-based numerical simulation of fluids and solids for applications in animation, visual effects, and interactive environments. He holds a Ph.D. from the University of British Columbia (2010) and a B.C.Sc. from the University of Manitoba (2004). His work spans fluid dynamics, solid mechanics, and geometry processing, addressing challenges like surface reconstruction, multi-scale simulations, and efficient solvers for complex fluid-solid interactions. Recent contributions include novel methods for divergence-free fluid editing, discrete elastic rod optimization, and Monte Carlo-based approaches for PDEs on surfaces. Batty’s research integrates computational geometry, numerical analysis, and optimization to create scalable and accurate tools for procedural fluid and solid simulation. His articles emphasize robustness in handling thin obstacles, narrow gaps, and intricate boundary conditions, often leveraging advanced techniques like closest point methods and monolithic solvers. No scientific awards are listed, though his extensive publication record reflects significant contributions to the field. He leads projects on adaptive liquid simulations, surface-only deformable models, and high-resolution embedded fluid surfaces.

Qinglong TianView profile

Assistant Professor

• Statistical Methodology
• Reliability Engineering
• Machine Learning
• +3 more

Qinglong Tian is an Assistant Professor in the Department of Statistics at the University of Waterloo. His research focuses on statistical methodology development with applications in reliability engineering, machine learning, and predictive modeling. He specializes in areas such as nonparametric estimation, domain adaptation, privacy-preserving data analysis, and Bayesian reliability analysis. His work bridges theoretical statistics with practical challenges in engineering and healthcare sectors. Key research interests include semi-supervised learning, ROC curve analysis, label shift adaptation, and prediction interval methods. He has contributed novel techniques for handling imperfect reference standards, continuous target shift, and heterogeneous reliability data. His recent publications address challenges in post-randomization data privacy and model-agnostic parameter estimation. Qinglong's methodological innovations are applied to real-world problems like pipeline life assessment, diagnostic testing accuracy, and failure prediction in complex systems. His interdisciplinary approach combines rigorous statistical theory with computational methods to solve modern data science challenges.