جست‌وجوی استاد و پژوهشگر

Lukás Malýمشاهده پروفایل

دانشیار

• differential equations
• measure theory
• integration
• +۱۵ مورد دیگر

Lukás Malý is an Associate Professor at Linköping University working within the Department of Science and Technology (ITN) in Norrköping since 2019. His research focuses on differential equations and their generalizations, with specific expertise in measure theory, integration, function spaces, first-order analysis on metric spaces, and non-linear potential theory. His research spans several key areas: nonlinear potential theory associated with p-harmonic functions and quasiminimizers in metric spaces; inverse problems with applications in machine learning (particularly neural ordinary differential equations) and medicine (brain tumor modeling); and computational science including harmonic analysis and integrable systems. He is particularly known for his work on Newtonian spaces, Sobolev capacities, and boundary value problems in metric measure spaces. Malý's recent publications (2023-2024) show a growing emphasis on applications of differential equations to machine learning and medical imaging, particularly through neural ordinary differential equations and reaction-diffusion tumor models, while maintaining strong theoretical foundations in metric space analysis and Sobolev space theory. His educational innovation work focuses on digitalization within STEM instruction, developing interactive visualizations and sonifications of mathematical concepts using GeoGebra and JSXGraph, and creating digital learning platforms with immediate feedback systems for students. Co-supervised Rym Jaroudi's PhD thesis: 'Inverse Problems for Tumour Growth Models and Neural ODEs' (2023) Supervised Kristos Qiqi's Master's thesis: 'Image-driven simulation of brain tumors using a reaction-diffusion mathematical model' (2023) Developed fully digital summer course 'Preparatory Mathematics' bridging high school and university mathematics Malý is part of the Physics, Electronics and Mathematics (FEM) research group at ITN, contributing to mathematics research that intersects with communication electronics, engineering didactics, and physical electronics. His work connects theoretical mathematics with practical applications in machine learning and medical science.

Krishnakumar Balasubramanianمشاهده پروفایل

دانشیار

• Deep Learning Theory
• Sampling and Stochastic Optimization
• Geometric Statistics
• +۵ مورد دیگر

Krishnakumar Balasubramanian is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on deep learning theory, sampling and stochastic optimization, geometric and topological statistics, and nonparametric methods. He has contributed to advancements in high-dimensional statistical inference, optimization algorithms, and theoretical foundations of machine learning. He holds a Ph.D. in Statistics and has published extensively in top-tier journals and conferences. His work bridges statistical theory with practical machine learning challenges, addressing topics such as sampling algorithms, non-smooth optimization, and the analysis of complex data structures like manifolds and networks. Notably, he won the Grad Advising Award, reflecting his dedication to student mentorship. His research spans diverse areas including Stein's method applications, Langevin Monte Carlo analysis, and the theoretical properties of gradient descent dynamics. He also explores nonparametric modeling, geometric statistics, and the interplay between optimization and statistical guarantees in high-dimensional settings. Key Areas: Deep Learning Theory, Stochastic Optimization, Geometric Statistics, Topological Data Analysis, Nonparametric Methods, High-Dimensional Statistics Awards: Grad Advising Award Advising: Actively involved in mentoring graduate students, as highlighted by his award.

Andre Laestadiusمشاهده پروفایل

پژوهشگر

• Coupled-Cluster Theory
• Density-Functional Theory
• Quantum Chemistry
• +۳ مورد دیگر

Andre Laestadius is a Researcher at the University of Oslo's Department of Chemistry, affiliated with the Hylleraas Centre for Quantum Molecular Sciences. His academic roles include leading the Young Research Talent project CCerror (2019–present) and the ERC-funded REGAL project (2022–). He holds a PhD in applied mathematics from the Royal Institute of Technology (KTH), Sweden, and has held positions at Uppsala University and the University of Oslo. His research focuses on coupled-cluster theory and density-functional theory, with applications to molecular electronic structure and quantum chemistry. Education: PhD in Applied Mathematics, Royal Institute of Technology (KTH), 2014 Master of Science in Biotechnology, Royal Institute of Technology (KTH), 2008 Research Interests: His work bridges mathematics and theoretical chemistry, emphasizing error analysis in coupled-cluster methods, regularization in density-functional theory, and molecular systems under extreme conditions. Key projects include CCerror (error estimates for excited states) and REGAL (regularized density-functional analysis). Publications: Recent work explores exchange energies in DFT, coupled-cluster theory reanalysis, and paramagnetic current-density functionals. These contributions advance computational methods and theoretical frameworks in quantum chemistry. Awards: ERC Starting Grant (2022) Peder Sather Grant (2021–2023) Young Research Talent Grant (2019–present) Grants & Projects: Leads CCerror (RCN-funded) and REGAL (ERC-funded). Collaborates internationally via grants like the Peder Sather Grant with UC Berkeley. Labs/Teams: Part of the Hylleraas Centre, a hub for quantum molecular sciences, and collaborates with global researchers in computational chemistry and theoretical physics.

Serena Dipierroمشاهده پروفایل

استاد

• Partial Differential Equations
• Free Boundary Problems
• Nonlocal Equations
• +۳ مورد دیگر

Serena Dipierro is a Professor at the University of Western Australia , affiliated with the School of Physics, Maths and Computing within the Mathematics and Statistics department. Her academic journey includes a PhD from the International School for Advanced Studies (SISSA, Trieste) in 2012, followed by postdoctoral positions at Universidad de Chile and the University of Edinburgh, as well as a Humboldt Fellowship. She previously held permanent positions at the University of Melbourne and Università di Milano before joining UWA in 2018. Her research focuses on Partial Differential Equations (PDEs) , Free Boundary Problems , Nonlocal Equations , and Calculus of Variations , with applications in nonlinear analysis. Key areas of investigation include the study of fractional Laplacians, minimal surfaces, and symmetry properties of solutions to PDEs. In 2023, she was awarded the School of Physics, Mathematics & Computing Mid-Career Research Award . She leads multiple ARC-funded research projects, including Non-local PDE approach to moving fronts and bushfires and Partial Differential Equations, free boundaries and applications . Grants : Non-local PDE approach to moving fronts and bushfires (2025–2028) Nonlocal Equations at Work (2017–2022) Partial Differential Equations, free boundaries and applications (2018–2022) Labs/Teams : Active in interdisciplinary research groups within the School of Physics, Maths and Computing, focusing on PDEs and nonlocal operators.

Marcello Carioniمشاهده پروفایل

استادیار

• Inverse Problems
• Optimal Transport
• Regularization Techniques
• +۳ مورد دیگر

Marcello Carioni is an Assistant Professor affiliated with the Digital Society Institute, TechMed Centre, and Mathematics of Imaging & AI. His research focuses on inverse problems, optimal transport, regularization techniques, and mathematical analysis with applications in machine learning and imaging. He has published extensively, including 35 research outputs since 2016, with a notable h-index of 23 and 228 citations. His work bridges theoretical mathematics and practical applications, addressing challenges in sparsity recovery, dynamic systems, and convex optimization. Recent contributions include advancements in optimal transport regularization, sparse optimization frameworks, and neural network representations in infinitely wide regimes. Collaborations span international teams, reflecting his interdisciplinary approach to mathematical challenges. Carioni has delivered invited talks on topics like dynamic inverse problems and optimal transport methods, showcasing his engagement with both academic and applied communities. His research emphasizes methodological innovation, with a focus on solving complex problems in imaging and data science through rigorous mathematical foundations.

Amos Lawlessمشاهده پروفایل

استاد

• Data Assimilation
• Numerical Weather Prediction
• Meteorology
• +۴ مورد دیگر

Professor Amos Lawless is a leading academic in the Department of Meteorology at the University of Reading, specializing in data assimilation and inverse problems. His research focuses on advancing numerical methods for meteorological and oceanographic modeling, particularly in variational data assimilation, error covariance analysis, and coupled atmosphere-ocean systems. He holds a Professorship position and contributes to the development of algorithms for improving forecasting accuracy in numerical weather prediction (NWP) systems. His work integrates computational mathematics, environmental science, and applied statistics to address challenges in climate modeling and predictive systems. Key research themes include the conditioning of data assimilation problems, model bias correction, and the application of optimization techniques to enhance forecast precision. His publications span prestigious journals like the Quarterly Journal of the Royal Meteorological Society and SIAM Journal on Applied Dynamical Systems, reflecting his expertise in both theoretical and applied aspects of data assimilation. Collaborations involve institutions such as the Met Office and international research groups, aiming to bridge the gap between computational methods and real-world environmental modeling challenges. Recent work emphasizes hybrid data assimilation approaches, cross-domain error correlations in coupled systems, and the use of randomized preconditioning to tackle large-scale computational problems. His contributions are pivotal in advancing the mathematical foundations of environmental forecasting systems, ensuring robustness and efficiency in modern NWP frameworks.

Ibrahim Ekrenمشاهده پروفایل

دانشیار

• Stochastic Control Theory
• Stochastic Partial Differential Equations
• Mathematical Finance
• +۳ مورد دیگر

Ibrahim Ekren is an Associate Professor of Mathematics at the University of Michigan, specializing in stochastic control, PDEs, and mathematical finance. He holds a Diplôme d'Ingénieur from École Polytechnique (2009), an M.Sc. from Université Paris VI (2010), and a Ph.D. from the University of Southern California (2014). His research integrates stochastic analysis with applications in finance and machine learning, including market microstructure modeling, online learning algorithms, and high-dimensional optimization. Recent publications demonstrate a focus on quantitative finance (Kyle-Back models, liquidity effects) and machine learning (regret minimization, adversarial prediction). He actively advises doctoral students (including Lu Vy and Liwei Huang) and postdoctoral researchers. His NSF grants include DMS-2406240 (2024-2027) and DMS-2007826 (2020-2024). He serves as associate editor for Finance and Stochastics, Applied Mathematics and Optimization, and Advances in Continuous and Discrete Models.

Pavan Aduriمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Science
• +۴ مورد دیگر

Pavan Aduri is a Professor and Interim Department Chair at Iowa State University's Department of Computer Science. His research focuses on theoretical computer science, computational complexity, algorithms, and machine learning. Key interests include statistical similarity estimation, submodular optimization, and probabilistic inference. He has contributed significantly to topics like #P-completeness, data stream processing, and fairness in optimization. His work spans algorithm design, computational hardness analysis, and applications in data science. Notable publications address total variation distance estimation, forgetting mechanisms in data streams, and monotone k-submodular maximization. He has explored connections between model counting and distinct element estimation in streams. Aduri's recent work (2023-2025) emphasizes geometric rounding techniques, Sperner's lemma variants, and algorithmic approaches to probabilistic inference. His research often bridges foundational theory with practical algorithmic challenges in machine learning and big data analysis. No scientific awards are explicitly listed, but his extensive publication record reflects sustained academic contributions.

• Uncertainty Quantification
• Ensemble Kalman Methods
• Bayesian Inverse Problems
• +۳ مورد دیگر

Xin T. Tong is Associate Professor in the Department of Mathematics at the National University of Singapore, specializing in uncertainty quantification, machine learning, and operations research. His research develops theoretical foundations and methodologies for structured problem-solving in high-dimensional settings. Current investigations focus on ensemble Kalman methods, Bayesian inverse problems, sampling algorithms, and stochastic optimization with non-i.i.d. data. Research emphasizes mathematical analysis of algorithm efficiency and structure-aware computational methods. Professional background includes postdoctoral work at NYU's Courant Institute and Ph.D. from Princeton University.

Yang Wangمشاهده پروفایل

استادیار

• Computational Phonology
• Morphophonology
• Language Structure
• +۳ مورد دیگر

Yang Wang is an Assistant Professor in the Linguistics Department at the University of Utah, specializing in computational phonology. She received her PhD in Linguistics from UCLA under the supervision of Tim Hunter and Bruce Hayes. Research Interests: Structural properties of natural languages, especially (morpho)phonology How linguistic structures are represented, computed and learned Converging evidence from traditional/formal linguistic analyses, mathematical investigations, computational modeling, and experimental studies Her research examines the learning and computational properties of morphophonological copying (i.e. reduplication). Her recent publications focus on the typology of reduplication and regular copying languages. Professional Activities: Presented a poster titled 'The emergent typology of reduplication: Universals and variations in learning biases.' at AMP in November 2024 Published 'On regular copying languages' in the Journal of Language Modelling in July 2023 (joint work with Tim Hunter)

Leon Bungertمشاهده پروفایل

استاد

• Machine Learning
• Applied Mathematics
• Partial Differential Equations
• +۵ مورد دیگر

Leon Bungert is a W2 Professor with Tenure Track to W3 Professorship for Mathematics III (Mathematics of Machine Learning) at the University of Würzburg, appointed since 2023. He leads the Mathematics of Machine Learning group within the Institute of Mathematics. His academic journey includes a Ph.D. (summa cum laude) from the University of Erlangen-Nürnberg in 2020, focusing on nonlinear spectral theory with variational methods, followed by postdoctoral research at TU Berlin, Bonn, and Erlangen-Nürnberg. Research Interests: Bungert's work bridges applied analysis and machine learning, emphasizing PDEs on graphs, adversarial robustness, inverse problems, optimization, and nonlinear eigenvalue problems. His research often employs variational methods and explores the intersection of geometry, probability, and numerical analysis. Notable areas include adversarial machine learning, Lipschitz learning on graphs, and regularization techniques for inverse problems. Professional Activities: He serves as a guest editor for the European Journal of Applied Mathematics , associate editor for Advances in Continuous and Discrete Models , and co-organizes major conferences like MIA'25 and SSVM 2025. His work has been published in top journals such as Annals of Applied Probability , Journal de Mathématiques Pures et Appliquées , and SIAM Journal on Imaging Sciences . Upcoming Engagements: Invited speaker and organizer at events including the IHP Paris conference (January 2025), Osaka workshop on machine learning and numerics (March 2025), and the BIRS workshop on adversarial machine learning (August 2025). His research team collaborates with institutions globally, including MIT, University of Cambridge, and University of Bonn. Lab/Team: The Mathematics of Machine Learning group at Würzburg focuses on theoretical and applied aspects of machine learning, with active projects in adversarial robustness, PDE-based optimization, and geometric data analysis. Bungert’s team includes researchers like Eloi Martinet and Tim Roith, advancing interdisciplinary work in mathematical foundations of AI.

Christian Kanzowمشاهده پروفایل

استاد

• Optimization
• Complementarity Problems
• Variational Inequalities
• +۶ مورد دیگر

Christian Kanzow is a Full Professor at the University of Würzburg , holding the Chair of Mathematics VII (Numerical Mathematics and Optimization) . His research focuses on optimization problems, complementarity, variational inequalities, generalized Nash equilibrium problems, mathematical programs with equilibrium constraints, sparse optimization, and cardinality constraints, with algorithmic developments for large-scale nonconvex optimization. Academic Affiliation: Faculty of Mathematics and Computer Science, University of Würzburg Research Interests: Optimization theory, numerical methods for equilibrium problems, sparse control, infinite-dimensional optimization His recent publications emphasize proximal gradient methods, augmented Lagrangian techniques, and nonsmooth DC programming, often analyzing convergence under the Kurdyka–Łojasiewicz property or C2-cone reducibility. He has received multiple best paper awards, including the Charles Broyden Prize (2008, 2022) and COAP Best Paper Award (2021) . He served as Dean of the Faculty of Mathematics and Computer Science (2017–2019) and Vice Dean (2015–2017), with extensive involvement in university governance and appointment committees.

Annalisa Massaccesiمشاهده پروفایل

دانشیار

• Geometric Measure Theory
• Calculus of Variations
• Partial Differential Equations
• +۳ مورد دیگر

Annalisa Massaccesi is an Associate Professor at the Department of Mathematics 'Tullio Levi-Civita' , University of Padova. Her research focuses on Geometric Measure Theory, Calculus of Variations, and Partial Differential Equations. She has contributed to topics such as mass-minimizing currents, optimal transport, nonlocal diffusion models, and Carnot-Carathéodory spaces. Her work often bridges geometric analysis with advanced analytical techniques. Key areas include: Geometric structures of currents in sub-Riemannian settings Optimal transport problems with multi-material systems Regularity of solutions to PDEs in non-smooth contexts Her articles often explore theoretical frameworks for variational problems, with recent trends emphasizing Frobenius theorem applications and unrectifiability in non-involutive distributions (2025). She has organized conferences on Geometric Measure Theory and collaborates on interdisciplinary projects, including numerical calibration of Steiner trees and biological population dynamics. Education includes a PhD in Mathematical Analysis (2015) with a thesis on modeling dislocations via 1-currents. Active in seminars and workshops, she maintains academic engagement through events like the 'School in Geometric Measure Theory' (2025) and the 'Vito Volterra Meeting in Calculus of Variations' (2023).

• Calculus of Variations
• Nonlinear Analysis
• Partial Differential Equations
• +۳ مورد دیگر

Giovanni Pisante is an Associate Professor at the Department of Mathematics and Physics (Università degli Studi della Campania 'Luigi Vanvitelli'). His research focuses on Calculus of Variations, Nonlinear Analysis, and Partial Differential Equations, with emphasis on regularity theory, optimal design problems, and free boundary phenomena. He has contributed to studies involving anisotropic functionals, geometric inequalities, and stability of functional inequalities. Education: Advanced degrees in Mathematics, likely from prestigious Italian institutions (not explicitly stated). His work bridges analytical techniques with geometric problems, addressing topics like degenerate elliptic equations, elasticity theory, and convex geometry. Recent publications explore multiphase optimal design and epsilon-regularity in anisotropic settings. He has participated in conferences on Nonlinear Analysis, PDEs, and Variational Problems, including roles as speaker and organizer of events such as 'START for Analysis' and workshops on L∞ constrained variational problems. No scientific awards are explicitly listed, though his active publication record and conference involvement suggest sustained research engagement. He advises no listed students but collaborates with researchers like N. Fusco, M. Barchiesi, and L. Esposito. His work has implications for both theoretical mathematics and applied fields like material science.

• Geometric Measure Theory
• Geometric Analysis
• Functional Inequalities
• +۳ مورد دیگر

Ivan Yuri Violo is a post-doctoral researcher at the Department of Mathematics of the University of Pisa, Italy. His research focuses on geometric measure theory, geometric analysis, functional inequalities, and metric spaces with curvature bounds. He holds a PhD in Mathematical Sciences from an unspecified institution (PhD thesis: Functional and geometric rigidities of RCD spaces and bi-Lipschitz Reifenberg's theorem in metric spaces , 2021). His work explores stability of Sobolev inequalities on Riemannian manifolds, topological regularity of isoperimetric sets, and the interplay between Ricci curvature bounds and geometric properties in non-smooth settings. He has contributed to understanding p-Laplacian equations in RCD spaces, Carleson-type lemmas for uniform rectifiability, and nodal domain theorems in non-smooth geometries. Violo's articles frequently intersect with geometric analysis, partial differential equations, and metric geometry, emphasizing applications to functional inequalities and curvature constraints. He maintains a homepage with further details and is reachable at ivan.violo@sns.it .