Find Academic Supervisors

Maxim L. YattselevView profile

Professor

• Meromorphic Approximation
• Orthogonal Polynomials
• Integrable Systems
• +3 more

Maxim L. Yattselev is a Professor in the Department of Mathematical Sciences at Indiana University–Purdue University Indianapolis (IUPUI) . He has held prior positions as a Visiting Assistant Professor at the University of Oregon and a Visiting Scholar at Vanderbilt University. Education: Ph.D. and M.S. in Mathematics from Vanderbilt University (2007, 2004), M.S. and B.S. from Dnepropetrovsk National University (2001, 2000). Research Interests focus on meromorphic approximation, orthogonal polynomials, integrable systems, random polynomials, and spectral theory. He explores connections between these areas and applications to Painlevé equations, random matrix theory, and convergence of rational approximants. Publications highlight advancements in strong asymptotics for Angelesco systems, spectral theory of Jacobi matrices on trees, universality in random polynomial roots, and convergence properties of Padé and Hermite–Padé approximants. His work often employs Riemann–Hilbert techniques and potential theory. Advising: Mentored Ph.D. students Hanan Aljubran and Ahmad Barhoumi on random polynomials and multiple orthogonal polynomials. Teaching includes advanced courses in complex analysis, real analysis, orthogonal polynomials, and differential equations at IUPUI, University of Oregon, and Vanderbilt University.

José E. ChacónView profile

Professor

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +3 more

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.

• Bayesian Networks
• Artificial Intelligence
• Risk Analysis
• +3 more

Steven Mascaro is a Senior Research Fellow in the Department of Data Science & AI at Monash University. His research focuses on advancing Bayesian network methodologies and their application in complex domains such as public health, biosecurity, and policy analysis. He collaborates on projects like 'Fitting AI technology to complex policy problems' and 'The Development of Novel Bayesian Network and Multi-Criteria Decision Analysis Techniques.' Key research areas include causal modeling for medical decision support, risk assessment frameworks for invasive species, and expert elicitation techniques. Mascaro's work bridges theoretical AI advancements with practical implementations in healthcare and environmental management. His contributions span over 27 peer-reviewed outputs, emphasizing interdisciplinary collaboration. Projects: 2 major research initiatives funded through 2022 Publications: Over 25 articles since 2001, with recent focus on Bayesian networks in pandemic response and clinical diagnostics Expertise: Bayesian network parameterization, causal inference, and decision-support tool development His current efforts prioritize scalable AI solutions for policy challenges and improving diagnostic accuracy through causal modeling.

Richard A. DavisView profile

Professor

• Applied Probability
• Time Series Analysis
• Stochastic Processes
• +4 more

Richard A. Davis is a Professor of Statistics at Columbia University and a Hans Fischer Senior Fellow at the Technical University of Munich's Institute for Advanced Study (TUM-IAS). He holds the Howard Levene Professorship in Statistics. His research focuses on applied probability, time series analysis, stochastic processes, and extreme value theory. Davis has contributed to nonlinear time series models, spatial modeling, and financial econometrics. He has held academic positions at Colorado State University and MIT, and has been recognized with awards including the Koopmans Prize, IBM Faculty Award, and fellowships from the Institute of Mathematical Statistics and American Statistical Association. His education includes a B.A. and Ph.D. in Mathematics from the University of California, San Diego. He co-authored influential textbooks like Time Series: Theory and Methods and has advised numerous papers on topics ranging from count time series to spatial extremes. Current research explores machine learning applications in time series and environmental risk modeling through collaborations like the STARMAP program.

• Distributed Computing
• Parallel Algorithms
• Graph Theory
• +3 more

Christoph Grunau is a Researcher at ETH Zürich's Theoretical Computer Science department, affiliated with the Professorship for Computer Science. His work focuses on distributed computing, parallel algorithms, graph theory, and network decomposition. He has contributed to advancements in scalable MPC (Massively Parallel Computing) algorithms, efficient parallel derandomization techniques, and deterministic network decomposition methods. His research emphasizes algorithmic efficiency, theoretical guarantees, and applications in distributed systems, quantum computing, and dynamic graph problems. Key contributions include work on graph orientation, dynamic coloring algorithms, and clustering techniques such as k-center and k-means++. His publications span topics like shortest path algorithms with negative edge weights, probabilistic methods for algorithm analysis, and distributed symmetry breaking in sparse graphs. Grunau's research bridges foundational theory with practical distributed computing challenges, addressing scalability and efficiency in both classical and emerging computational frameworks.

Frédéric BERTRANDView profile

Professor

• Statistical Modelling
• Statistical Learning
• Artificial Intelligence
• +5 more

Frédéric BERTRAND is a Full Professor at the Université de Technologie de Troyes (UTT), part of the Computer Laboratory and Digital Society (LIST3N). He previously held positions at the Université de Strasbourg and Institut de Recherche Mathématique Avancée (IRMA). His academic qualifications include a habilitation to direct research (HDR) from the Université de Strasbourg, a Ph.D. from Université Louis Pasteur, and membership in the École Normale Supérieure de Lyon. He specializes in statistical modeling, machine learning, and artificial intelligence, with applications in bioinformatics, biostatistics, and medical data analysis. Roles and Affiliations: Full Professor at UTT Director of the Mastère Spécialisé in Big Data Analytical and Decisional Aspects Responsible for the OSS ED361 SPI specialty Responsible for the PEA Impact program Member of the LIST3N laboratory Research Interests: His work focuses on statistical and machine learning methods applied to complex systems, including bioinformatics, medical data analysis, and process mining. Key areas include Bayesian networks, partial least squares regression (PLS), variable selection algorithms, and handling missing data in large-scale datasets. He has developed multiple R packages, such as selectBoost , bootPLS , and Patterns , which are widely used in statistical modeling and data analysis. Publications and Contributions: He has authored/co-authored over 38 scientific articles, 14 books, and 14 R packages. His research emphasizes methodological advancements in statistics and their practical applications in biology, healthcare, and industry. Notable contributions include developing algorithms for high-dimensional data analysis and collaborative projects on cancer genomics and proteomics. Teaching: He teaches advanced courses in statistics, mathematics, and data analysis at the graduate and postgraduate levels, including modules on uncertainty analysis and complex systems at UTT and the Université de Strasbourg.

Gabriele SicuroView profile

Researcher

• Statistical Mechanics
• Theoretical Physics
• Random Graphs
• +4 more

Gabriele Sicuro holds a Researcher position, with a focus on theoretical physics and statistical mechanics. He earned a Doctor of Science in Physics from Università di Pisa (2012–2015), a Master of Science from University of Salento (2011), and a Bachelor of Science from the same institution (2009). His research interests include high-dimensional systems, random graphs, and machine learning applications. Notable contributions address superstatistical features, dimer models, and hypergraph matching problems. He collaborates internationally, with work published in journals like Physical Review E and Journal of Statistical Mechanics . His publications explore topics such as regularization in high dimensions, graph alignment algorithms, and asymptotic analysis of Gaussian mixtures. With 220 citations, his work bridges theoretical frameworks and computational methods, contributing to advancements in statistical physics and data science.

Lap Chi LauView profile

Professor

• Algorithms
• Optimization
• Spectral Graph Theory

Lap Chi Lau is a Professor at the University of Waterloo's Cheriton School of Computer Science. His primary research explores algorithms, optimization, and spectral graph theory. His recent publications focus on graph algorithms, spectral methods, and combinatorial optimization, with consistent applications to network design and experimental methodologies. Publications demonstrate advanced techniques in graph sparsification, eigenvalue methods, and efficient algorithm design for complex computational problems. His work frequently intersects theoretical computer science and applied mathematics, contributing to foundational improvements in graph partitioning, spectral clustering, and randomized algorithms.

Christopher GreenView profile

Assistant Professor

• Applied Mathematics
• Complex Analysis
• Fluid Mechanics
• +3 more

Dr. Christopher Green is an Assistant Professor of Applied Mathematics at Wichita State University's Fairmount College of Liberal Arts and Sciences, Department of Mathematics, Statistics & Physics. He holds a PhD from Imperial College London (supervised by Prof. Darren Crowdy) and has held postdoctoral positions at the University of California San Diego and Queensland University of Technology. Previously, he served as a Lecturer in Applied Mathematics and ARC DECRA Fellow at Macquarie University in Sydney, Australia. His research focuses on computational and applied complex analysis, particularly in multiply connected domains. Key interests include the Schottky-Klein prime function, ideal fluid mechanics, and free boundary problems. His work bridges classical function theory with modern computational methods. Recent publications emphasize fluid dynamics applications such as Hele-Shaw bubble dynamics, harmonic measure computations, and airfoil potential flow. His research has been published in high-impact journals across applied mathematics and fluid mechanics. Christopher Green has received the ARC DECRA Fellowship, a prestigious early-career award. His advising and grants include contributions to computational methods in complex analysis and fluid dynamics. He maintains active collaborations internationally, leveraging both theoretical and numerical approaches to geometrically complex problems.

Selin AhipasaogluView profile

Professor

• Operational Research
• Convex Optimization
• Robust Optimization
• +5 more

Prof. Selin Damla Ahipasaoglu is a Professor in Operational Research at the University of Southampton's School of Mathematical Sciences . She serves on the management team of the UKRI CDT SustAI (Artificial Intelligence for Sustainability) as Senior Tutor and Co-Lead for the Transportation and Logistics Theme . Her work bridges mathematical optimization with practical applications in sustainability, finance, and transportation systems. Research Interests : Convex Optimization Robust Optimization Discrete Choice Theory Experimental Design Machine Learning Current Research : Focused on robust optimization and its applications in discrete choice modeling, portfolio optimization, and transportation systems. She explores theoretical frameworks alongside real-world implementations, particularly through interdisciplinary projects like the UKRI CDT SustAI. Teaching : In the 2025/2026 academic year, she teaches MATH3017: Mathematical Programming and MATH2013: Operational Research II . She supervises PhD students in Mathematical Sciences, including Kexin Lai, Samuel Jericho Ward, and others.

• Mathematical Optimization
• Game Theory
• Mechanism Design
• +7 more

Max Klimm is an Assistant Professor for Discrete Optimization at Technische Universität Berlin, affiliated with Faculty II – Mathematics and Natural Sciences and the Department of Mathematics. He leads the research group in Discrete Optimization and holds editorial roles at journals like the International Journal of Game Theory and Operations Research Forum . His academic journey includes a PhD in Mathematics from TU Berlin (2012), followed by roles as an Assistant Professor at Humboldt-Universität zu Berlin and Head of the Junior Research Group at the Einstein-Center for Mathematics. His research focuses on mathematical optimization, game theory, and mechanism design applied to multi-agent systems in traffic, telecommunications, and economics. Recent work addresses equilibrium computation in congestion games, stochastic optimization, and algorithmic challenges in network design. Key projects include Combinatorial Network Flow Methods for Gas Markets and the Math+ projects on mechanism design and evolutionary models for networks. Teaching responsibilities include courses on Discrete Optimization, Algorithmic Game Theory, and introductory mathematical courses. His research has been funded by DFG, Einstein Center, and Math+ initiatives. Notable contributions include advancements in parametric flow algorithms, impartial selection mechanisms, and reconstructing historical road networks using cost-benefit models.

• von Neumann algebras
• C*-algebras
• CSL-algebras
• +2 more

Roger Smith is a Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. His research specializes in operator algebras, including von Neumann algebras, C*-algebras, CSL-algebras, and functional analysis. He earned his Ph.D. from Oxford University in 1975 after completing his S.M. at MIT and B.A. at Jesus College, Oxford. Smith's current work focuses on computational methods for partial differential equations and numerical homogenization, with recent publications exploring multiscale techniques for elliptic equations. His articles demonstrate strong engagement with finite element approximations and convergence analysis for complex mathematical systems. While no awards are documented, Smith maintains active collaborations internationally and contributes to seminar organization. His teaching includes courses like Math 172 and 407, with office hours available for student consultation.

Christopher HughesView profile

Associate Professor

• Number Theory
• Random Matrix Theory
• Analytic Number Theory
• +6 more

Christopher Hughes is an Associate Professor in the Department of Mathematics at the University of York, where he has served since 2007. He holds the position of Chair of the Graduate Research School Committee and is recognized as a Senior Fellow of the Higher Education Academy, reflecting his contributions to academic leadership and pedagogy. His academic journey began with a PhD from the University of Bristol in 2001 under Jon Keating and Neil O'Connell, followed by teaching roles at the University of Michigan and postdoctoral positions at Tel Aviv University, the American Institute of Mathematics, and Cambridge's Newton Institute. This diverse background informs his interdisciplinary research approach. Professor Hughes specializes in the profound connections between number theory and random matrix theory, particularly using characteristic polynomials of random matrices to model L-function distributions. His work explores the Riemann zeta function's value distribution, extreme values, and moments, with applications spanning mathematical physics, combinatorics, and probability. As an active member of York's Number Theory Research Group, he bridges theoretical mathematics with real-world phenomena through innovative probabilistic frameworks. Analysis of his 15 most recent publications (2008-2025) reveals a sustained focus on zeta function derivatives, discrete mean-value theorems, and Gram's law probabilistic models. While predominantly centered on number theory, his 2018 educational research on cyber-university learning demonstrates versatility. The trajectory shows increasing sophistication in hybrid analytical-random matrix techniques for tackling classical number theory problems. His scientific recognition includes: Senior Fellowship of the Higher Education Academy (HEA) Christopher Hughes currently supervises PhD candidates Solomon Lugmayer and Ben Durkan on projects involving zeta function extrema and random matrix applications. His research is supported by collaborative grants reflected in 29 recent activities including 8 international invited talks. He actively shapes graduate education through committee leadership while maintaining a robust publication pipeline, with his 2025 work on local extrema moments representing cutting-edge advances. As a core member of York's Number Theory Research Group, Hughes collaborates globally on projects connecting random matrices to L-functions. His current work focuses on extreme value distributions and derivative behaviors, with future research poised to deepen understanding of the Riemann hypothesis through novel matrix-theoretic approaches and interdisciplinary applications in quantum chaos and statistical mechanics.

Jane RyngaertView profile

Assistant Professor

• Macroeconomics
• Monetary Policy
• Inflation Expectations
• +3 more

Jane Ryngaert is an Assistant Professor of Economics at the University of Notre Dame, affiliated with the Department of Economics. Her research focuses on macroeconomic expectations, particularly inflation expectations and their implications for monetary policy, labor markets, and consumer behavior. She holds a Ph.D. from the University of Texas at Austin (2018), an M.S. from the same institution (2015), and a B.A. from the College of William and Mary (2013). Her key research areas include understanding how inflation expectations are formed, how they respond to monetary policy interventions, and their effects on firm and consumer decision-making. Notable contributions address partisan divides in inflation expectations during the pandemic, the role of higher-order beliefs among managers, and the impact of inflation expectations on labor market dynamics such as job search behavior and wage adjustments. She employs novel survey methodologies and structural models to analyze these phenomena. Ryngaert’s work has been published in top journals like the Quarterly Journal of Economics and Journal of Monetary Economics . Her teaching includes advanced macroeconomic theory and specialized courses on macroeconomic expectations. She actively contributes to the field through collaborative research with scholars like Olivier Coibion and Carola Binder, exploring topics such as information frictions among forecasters and the macroeconomic consequences of inflation disasters.

Dmitri FinkelshteinView profile

Associate Professor

• Analysis
• Probability Theory
• Mathematical Physics
• +7 more

Dr. Dmitri Finkelshtein is an Associate Professor in the Department of Mathematics at Swansea University, within the Faculty of Science and Engineering's School of Mathematics and Computer Science. He holds a PhD (2004) and DSc (2014) from the Institute of Mathematics, Kyiv, Ukraine, and a BSc/MSc from the National University of Kyiv. His research focuses on mathematical analysis, probability, and their applications to complex systems, including infinite-dimensional analysis, stochastic dynamics, and non-local evolution equations. Key areas include mathematical physics, point processes, and population ecology. He has contributed to frameworks for analyzing individual-based ecological models and spatial stochastic systems. Recent work includes studies on entropy behavior in Poisson distributions, spatial cumulant models in cancer systems, and the dynamics of non-local Fisher-KPP equations. He has secured grants, such as the £265k Horizon-Infra-2021 Biodiversity Digital Twin Project. Dr. Finkelshtein advises multiple PhD students on topics like reaction-diffusion models, umbral calculus, and tumor growth dynamics. His teaching spans mathematical analysis, dynamical systems, and probability theory.