Ryan Browne is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a BMath (2004), MMath (2006), and PhD (2009) from the same institution. His research focuses on model-based clustering , classification , and measurement system quality assessment , with applications in multivariate analysis and statistical inference. He is particularly known for contributions to mixture models and their computational optimization. Education: BMath in Statistics, University of Waterloo (2004) MMath in Statistics, University of Waterloo (2006) PhD in Statistics, University of Waterloo (2009) Ryan’s work emphasizes flexible statistical methodologies , including advancements in skewed distributions, high-dimensional data analysis, and robust algorithms for clustering and classification. He has received the prestigious 2011 W.J. Youden Award from the American Statistical Association for his PhD research on measurement system evaluation. His research trends span computational statistics (e.g., sketching algorithms for big data) and model-based clustering innovations (e.g., mixtures of generalized hyperbolic distributions). Recent work explores parsimonious models, nested Gaussian structures, and efficient parameter estimation for complex datasets. Key Awards: 2011 W.J. Youden Award (American Statistical Association) Ryan collaborates on applied projects, including industrial ecology and sensory data analysis. He has developed R packages like mixture and MixGHD , which implement his methodological contributions.
Dr. Mawuli Kouami Segnon is a researcher at the Chair of Empirical Economics, School of Business and Economics, University of Münster. His work focuses on econometric modeling, financial time series analysis, and volatility forecasting across various domains including cryptocurrencies, energy markets, and macroeconomic indicators. Research interests include: Development of advanced volatility models (GARCH, multifractal, regime-switching) Applications to financial markets, energy economics, and macroeconomic policy High-frequency data analysis and mixed-frequency forecasting Count data modeling with conditional heteroscedasticity Portfolio risk management using copula and multifractal approaches Recent publications demonstrate expertise in: Geopolitical risk impacts on stock volatility Comparative analysis of realized variance measures Inflation uncertainty modeling in G7 countries Electricity price volatility in Australian markets Bitcoin market forecasting Historical economic data analysis Current projects (since 2020) involve: Innovative economic/financial time series forecasting Financial market volatility modeling Applications of multifractal structures in econometrics
Lutz Warnke is a Professor of Mathematics at the University of California, San Diego, with prior affiliations at Georgia Institute of Technology (where he received tenure in 2021) and Peterhouse, Cambridge University (Junior Research Fellow until 2016). His research focuses on probabilistic combinatorics, random graphs, phase transitions, and combinatorial probability, with applications to extremal combinatorics and Ramsey theory. Education : Ph.D. in Mathematics from the University of Oxford (2012), supervised by Oliver Riordan. Dr. Warnke's research explores the structure and evolution of random graphs and processes, including Achlioptas processes, Ramsey numbers, and extremal problems. His work often bridges probabilistic methods with algorithmic applications and theoretical computer science. His publications from 2022–2025 reveal trends in random graph isomorphisms, clique coloring thresholds, extremal subgraph counts, and hardness of online algorithms. Key subfields include percolation, phase transitions, and probabilistic methods applied to combinatorial structures. Scientific Awards : Dénes König Prize (2016), Alfred P. Sloan Research Fellowship (2018), NSF CAREER Award (2020), Richard Rado Prize (2014). Dr. Warnke actively supervises PhD students and postdocs, including Matthew Cho (PhD ongoing), Erlang Surya (PhD 2025), Emily Zhu (PhD 2025), and He Guo (PhD 2021). He has received teaching accolades at Georgia Tech and contributes to graduate courses in probabilistic combinatorics, random graph theory, and stochastic processes.
Florian Naef is an Assistant Professor in the School of Mathematics at Trinity College Dublin. His research spans topological and algebraic structures with applications to mathematical physics, including string topology, Poisson geometry, and homotopy theory. Publications emphasize formality theorems, torsion invariants, and quantization methods. Recurring themes include loop spaces, deformation quantization, and connections between differential geometry and algebraic topology.
Scott Armstrong is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on partial differential equations, calculus of variations, and probability theory, with a specialization in stochastic homogenization of PDEs in random media and related statistical mechanical systems. He holds a Ph.D. from UC Berkeley (2009) and a B.S. from Texas A&M University (2002). Education: Ph.D. in Mathematics, University of California, Berkeley, USA (2009) B.S. in Mathematics, Texas A&M University, USA (2002) Research Interests: Scott's work addresses fundamental questions in homogenization theory, including quantitative estimates for elliptic and parabolic equations in random media, renormalization group methods, and applications to statistical mechanics. His contributions bridge analysis, probability, and mathematical physics, with a focus on rigorous mathematical frameworks for understanding macroscopic behavior from microscopic models. Publications: His recent work includes studies on anomalous diffusion, renormalization group techniques, and quantitative homogenization in high-contrast media. Over 50 peer-reviewed articles highlight his expertise in stochastic PDEs, elliptic regularity, and variational methods. Awards: No specific awards listed in the provided text. Advising & Grants: No student advisees or grant details explicitly mentioned in the text. Labs/Teams: No dedicated labs or collaborative teams explicitly noted, though his research likely involves interdisciplinary collaborations within the Courant Institute.
Ana Cannas da Silva is a Lecturer in the Department of Mathematics at ETH Zurich (Switzerland). She specializes in Symplectic Geometry , Geometric Topology , and Geometric Analysis . Her academic work includes research on symplectic toric manifolds, folded symplectic structures, and geometric quantization, with notable publications in journals like Pure and Applied Mathematics Quarterly and Mathematical Research Letters . Research Interests Symplectic Geometry Geometric Topology Geometric Analysis Hamiltonian Group Actions Toric Manifolds Recent Academic Activities Co-organized Symplectic Geometry Seminar (2021-2023) Supervised student theses on topics like contact toric manifolds, Hamiltonian actions, and symplectic linear algebra Authored research on Dedekind sums via Atiyah-Bott-Lefschetz theory (2023) and symplectic origami (2011) Teaching Lecturer for Mathematics I (2024), covering differential calculus and linear algebra Lecturer for Mathematics II (2024), focusing on multivariable calculus and partial differential equations Co-taught seminars on symplectic/contact geometry with Bahar Acu Academic Contributions Advised 20+ MSc/BSc theses at ETH Zurich since 2012 Co-organized conferences like D-Days (2013) and LP-60 (2023) Authored outreach book: Step by Step Symmetry (2016)
Ali Maalaoui is a Professor of Mathematics at Clark University, specializing in geometric analysis and calculus of variations, with a focus on conformal and CR geometries. He holds a Ph.D. from Rutgers University (2013) and a prior Ph.D. from the University of Tunis (2010). Before Clark, he was an Associate Professor at the American University of Ras Al Khaimah in the UAE and a postdoctoral fellow at the University of Basel, Switzerland. His research explores critical geometric partial differential equations (PDEs) involving energy concentration and bubbling phenomena, particularly in contexts like Dirac-Einstein equations, fractional Yamabe problems, and CR manifolds. Key contributions include studies on Q’-curvature flows, singular solutions in geometric PDEs, and functional inequalities in non-Euclidean settings. Maalaoui’s work combines analytical techniques from functional analysis, geometric measure theory, and Morse-Floer homology. Recent trends in his publications focus on fractional operators, spin geometry, and applications of conformal invariance principles. His articles span high-impact journals such as Mathematische Nachrichten , Journal of Differential Equations , and Calculus of Variations and Partial Differential Equations . No scientific awards or grants are explicitly listed in the provided information. He has advised no listed students but has contributed to collaborative projects with institutions worldwide. His research often involves international co-authors, reflecting a global network in geometric analysis.
Patrick T. Brandt is a Professor of Political Science, Public Policy, and Political Economy at the University of Texas at Dallas , affiliated with the School of Economic, Political and Policy Sciences . His work integrates advanced statistical methods with political science, focusing on time series analysis, machine learning, and Bayesian modeling to study political dynamics. His research spans international relations , political economy , terrorist targeting , and conflict forecasting . He specializes in developing novel models for event count time series, including the Bayesian Poisson Vector Autoregression and MS-BVAR packages for R. His NSF-funded projects focus on event data generation and real-time conflict forecasting. Recent publications emphasize domain-specific language models (ConfliBERT variants), graph neural networks for conflict prediction, and machine translation challenges in political text analysis. He maintains the OpenEvent Data Repository and develops software like MSBVAR and PESTS for academic use. Scientific Awards : Robert H. Durr Award for Best Methodology Paper, Midwest Political Science Association (2006)
Andreas Groll is a Professor at the Technical University of Dortmund, affiliated with the Department of Statistical Methods for Big Data under the Faculty of Statistics. His research focuses on variable selection, regularization techniques in generalized linear models, categorical data analysis, and sports statistics, particularly predicting international soccer and tennis tournaments. He leads a working group including researchers like Dr. Daniel Horn and Dr. Rouven Michels. Key research areas include semiparametric regression and event data analysis. Recent work explores machine learning applications in sports analytics and healthcare, such as predicting hospital readmissions and modeling environmental data. Groll has published extensively in journals like Journal of Quantitative Analysis in Sports and Statistical Modelling .
Chen Greif is a Professor in the Department of Computer Science at the University of British Columbia (UBC). His research focuses on numerical linear algebra, iterative solvers, preconditioning techniques, and scientific computing. He has held editorial roles in SIAM journals and book series, and served as Head of the Department of Computer Science from 2016 to 2020. Greif co-authored the SIAM bestselling textbook A First Course in Numerical Methods and has published extensively in top journals. Education: Ph.D., Mathematics, University of British Columbia, 1998 M.Sc., Mathematics, Tel Aviv University, 1994 B.Sc., Mathematics, Tel Aviv University, 1991 Research Interests: Scientific computing, numerical linear algebra, iterative methods for sparse linear systems, saddle-point systems, and elliptic PDEs. His work emphasizes preconditioning techniques and numerical stability. Publications Trends: Recent work includes multigrid methods for complex systems, preconditioners for saddle-point matrices, and eigenvalue bounds analysis. His contributions bridge theory and applications in computational science and engineering. Awards: SIAM Fellow (2022) CAIMS Research Prize (2023) Multiple teaching awards (2025, 2024, 2017, 2006, 2004) Advising & Grants: Greif has advised numerous students and contributed to grants in numerical methods and computational science. He has led major conferences (e.g., International Conference on Preconditioning Techniques, 2017) and served on SIAM committees. Labs/Teams: Engaged in interdisciplinary research groups at UBC, focusing on numerical algorithms and their applications in fluid dynamics, image processing, and computational geometry.
Naratip Santitissadeekorn is a Senior Lecturer in Data Assimilation at the School of Mathematics and Physics, University of Surrey, where he is affiliated with the Mathematics at the Interface Group. His work bridges mathematics, data science, and real-world applications in urban planning, crime analysis, and geophysical fluid dynamics. Dr. Santitissadeekorn received his PhD from Clarkson University in 2008, with a dissertation titled "Transport Analysis and Motion Estimation of Dynamical Systems of Time-Series data." His doctoral research was supervised by Professor Erik Bollt. Following his PhD, he completed two significant postdoctoral positions: from 2008-2011 at the University of New South Wales, Sydney, Australia, working with Professor Gary Froyland on numerical techniques for finite-time Lagrangian coherent set identification, with applications to delimiting the polar vortex and Agulhas rings; and from 2011-2014 at the University of North Carolina-Chapel Hill, working with Professor Chris Jones on data assimilation projects. Dr. Santitissadeekorn's research focuses on inverse problems and data assimilation in geophysical fluid dynamics, the applications of Lagrangian Coherent Structures (LCS), and computational ergodic theory. His work combines theoretical mathematics with practical applications, particularly in urban growth modeling and crime analysis. He has developed innovative methods for identifying coherent structures in fluid flows, estimating transition probabilities from spatiotemporal data, and creating data-driven frameworks for urban expansion scenarios. His research demonstrates how mathematical techniques can be applied to solve real-world problems in environmental science, urban planning, and public safety. An analysis of Dr. Santitissadeekorn's recent publications (2020-2023) reveals a strong focus on urban expansion modeling and network analysis. His work on urban growth has evolved from basic cellular automata models to sophisticated frameworks that manage uncertainty through parameter clustering and growth mode identification. His research on Hawkes processes has advanced ensemble-based filtering techniques for analyzing count data in large networks. These publications demonstrate a consistent pattern of applying mathematical rigor to complex spatiotemporal phenomena, with increasing emphasis on data-driven approaches and practical applications. Dr. Santitissadeekorn has made significant contributions to data assimilation methods, particularly through the development of the extended Poisson-Kalman filter (ExPKF) for urban crime modeling. His teaching includes courses in Algebra and Bayesian Statistics, reflecting his expertise in both theoretical and applied mathematics. While specific awards are not mentioned in the available information, his extensive publication record in high-impact journals demonstrates recognition within his field. Dr. Santitissadeekorn's research has practical implications for urban planning and law enforcement. His work on urban expansion models helps planners understand different growth trajectories, while his crime modeling research contributes to improved police patrolling strategies. His interdisciplinary approach, combining mathematics, computer science, and domain-specific knowledge, positions him at the forefront of applying data science to societal challenges.
Alexandros Kontogiannis is a research fellow at the University of Cambridge, Department of Engineering, specializing in fluid dynamics and applied mathematics. His work combines Bayesian inference, machine learning, and physics-informed algorithms to solve inverse problems in magnetic resonance velocimetry (MRV) and fluid-structure interaction. EPSRC National Fellow in Fluid Dynamics Member of Energy, Fluids and Turbomachinery Division Research Focus: Development of digital twin frameworks that integrate MRV data with Navier-Stokes equations to reconstruct flowfields, infer rheological parameters in non-Newtonian fluids, and estimate hidden quantities like pressure and wall shear stress. Key innovations include: Physics-informed compressed sensing for sparse MRV data Simultaneous boundary shape and flowfield estimation Bayesian turbulence model parameter learning Scientific Awards: ASME Fluids Engineering Division Graduate Student Scholar (2021) Technical Chamber of Greece (TEE) Award (2018) Limmat Foundation Academic Excellence (2017) Mentzelopoulos Scholarship for international studies (2017) Greek State Scholarships Foundation Award (2012) Key Contributions: Algorithms for 3D flow reconstruction with adaptive discretization, viscous signed distance field regularization, and multi-objective aerodynamic shape optimization. His methodologies enable 27x reductions in MRI scanning time while maintaining diagnostic accuracy.
Basile de Loynes is a Lecturer at the French National School of Statistics and Information Analysis (ENSAI), holding a permanent academic position since at least 2016. He maintains a dual affiliation as a CREST (Center for Research in Economics and Statistics) Affiliated Member, contributing to interdisciplinary economic-statistical research. His academic trajectory includes a postdoctoral position at the University of Neuchâtel (2012), followed by temporary lecturer roles at the University of Burgundy (2013-2014) and University of Strasbourg (2014-2016). His research centers on advanced probability theory with specific expertise in stochastic processes on non-Euclidean structures. Key areas include: Random walks on algebraic structures (groups, groupoids, tilings, graphs) Poisson-Martin boundary theory and potential analysis Long memory processes and invariance principles Graph signal processing with Fourier/wavelet methods His publication record shows consistent output in top-tier journals since 2012, with recent work (2021-2023) focusing on graph-based signal denoising and differential privacy applications. Analysis of his 10 most recent publications reveals a strong methodological thread connecting classical probability theory with modern graph-based signal processing. Approximately 60% of his work since 2016 involves graph-structured stochastic models, demonstrating an evolving research trajectory from theoretical random walk properties toward applied graph signal analysis. The recurring subfields across publications include Markov additive processes, spectral graph theory, and wavelet transforms on non-Euclidean domains. His academic service includes developing comprehensive teaching materials for core probability and measure theory courses at ENSAI, with publicly available lecture notes and examinations dating back to 2016.
James Martin is a Lecturer at the Department of Statistics, University of Oxford . He is affiliated with St Hugh's College and has been actively involved in organizing probability seminars since 2018. Research Interests Probability theory Random graphs and percolation Interacting particle systems Models of random growth and coagulation-fragmentation Queueing networks Combinatorial games Teaching Courses: Prelims Probability , Part A Probability , Part B Statistical Lifetime Models , Part C Probabilistic Combinatorics His publications focus on probability theory , statistical physics , and combinatorial structures . Recent work includes studies on last-passage percolation, multispecies exclusion processes, and integrable probability models. James Martin collaborates with researchers from institutions such as Uppsala University, University of Cambridge, Imperial College London, and Kyoto University. He has been a key organizer for the Oxford Probability Seminar since 2018.
David F. Anderson is the Vilas Distinguished Achievement Professor of Mathematics at the Department of Mathematics, University of Wisconsin-Madison. He has maintained an active research and teaching career spanning over two decades with significant contributions to mathematical biology and stochastic modeling. Dr. Anderson's research focuses on the interface of mathematics and biology, specifically in mathematical systems biology and algorithm design for stochastic models in biological systems. His work has fundamentally advanced chemical reaction network theory, stochastic processes in biochemical systems, and computational methods for analyzing complex biological phenomena. He has developed numerous numerical techniques for simulating and analyzing reaction networks with applications across systems biology. An analysis of his recent publications reveals a sustained focus on mathematical properties of stochastic reaction networks, with increasing emphasis on connections between chemical systems and computational frameworks. His later work explores reaction networks as computing devices, implementing arithmetic operations and neural network functionalities through biochemical processes, while maintaining rigorous mathematical analysis of network properties like ergodicity, mixing times, and solution structures. Simons Fellow (2022) Vilas Associates Award (2016) IMA Prize in Mathematics (2014) Dr. Anderson has successfully guided nine PhD students to completion, with recent graduates including Aidan Howells (2024), Tung Nguyen (2021), Chaojie Yuan (2020), Kurt Ehlert (2019), and Jinsu Kim (2018). His current graduate student is Jingyi Ma. His research has been supported by prestigious fellowships including the Simons Fellowship, indicating substantial research funding, though specific grant details aren't provided in the source material. While specific laboratory facilities aren't described in the text, Dr. Anderson maintains an active research group evidenced by continuous publications, regular PhD student completions, and collaborations with numerous researchers including Daniele Cappelletti, Jinsu Kim, and Tung Nguyen. His research program demonstrates sustained productivity with publications spanning from 2005 to the present.
