جست‌وجوی استاد و پژوهشگر

• Operator Theory
• Functions of a Complex Variable
• Functional Analysis
• +۵ مورد دیگر

Alejandro Mas Mas is an Assistant Professor in the Department of Mathematical Analysis at the Faculty of Mathematics, Universidad Autónoma de Madrid. He obtained his Ph.D. in 2021 with a thesis on weighted composition operators and conformal invariance, supervised by Dr. Dragan Vukotić. His research lies at the intersection of operator theory , complex analysis , and functional analysis , with a focus on weighted composition operators, Banach and Hilbert spaces of analytic functions, and Dirichlet spaces. He is a member of the AHF (Harmonic and Functional Analysis) research group. His recent publications (2020–2023) demonstrate a strong trend in analyzing operator-theoretic properties such as invertibility, isometry, and co-isometry in spaces of analytic functions, often involving radial measures and conformal invariance. These works appear in respected journals like Journal of Functional Analysis , Results in Mathematics , and Journal of Mathematical Analysis and Applications . Ph.D., Universidad Autónoma de Madrid, 2021 Thesis: Weighted composition operators and weighted conformal invariance Advisor: Dr. Dragan Vukotić There are no scientific awards mentioned in the available text. Alejandro Mas Mas collaborates with prominent researchers in complex analysis, including Dragan Vukotić, Petros Galanopoulos, and Daniel Girela. His work has been supported by research groups and publications, though specific grants are not detailed. He has not published a large volume of work, but his contributions are focused and technically deep within his niche. He is affiliated with the AHF (Harmonic and Functional Analysis) research group, which likely provides a collaborative environment for his work in functional and harmonic analysis.

Qiang Liuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Probabilistic Graphical Models
• +۸ مورد دیگر

Qiang Liu is an Associate Professor in the Department of Computer Science at the University of Texas at Austin. He leads the Statistical Learning & AI Group, focusing on probabilistic graphical models, variational and Monte Carlo inference, deep reinforcement learning, and kernel methods with applications in crowdsourcing, computer vision, and bioinformatics. NSF CAREER Award recipient Co-organized workshops at ICML 2019 and NIPS 2013 Specializes in rectified flow for generative modeling and optimal transport His recent work includes fast inference via straight trajectories in ODE models and certifiable robustness in neural architectures. Publications span conferences like NeurIPS, ICML, ICLR, and JMLR.

Hajime Kanekoمشاهده پروفایل

دانشیار

• Uniform Distribution Theory
• Diophantine Approximation
• Transcendental Number Theory
• +۴ مورد دیگر

Hajime Kaneko is an Associate Professor at the Department of Mathematics, Faculty of Pure and Applied Sciences, University of Tsukuba . His research focuses on Uniform Distribution Theory , Diophantine Approximation , and Transcendental Number Theory , with a particular emphasis on the behavior of number sequences in beta expansions and geometric progressions involving algebraic numbers. Key research topics include Markoff-Lagrange Spectrum , Sturmian Type Numbers , and Digit Exchange Properties in numerical systems. He has actively participated in and organized seminars such as DARF (Diophantine Analysis and Related Fields) and the 2024 Workshop on Number Theory and Ergodic Theory at Kanazawa University. His recent publications highlight collaborations with prominent mathematicians like Shigeki Akiyama , Thomas Stoll , and Wolfgang Steiner . Notably, his work on the Markoff-Lagrange Spectrum (2024) and binary digits of algebraic numbers (2023) has advanced the understanding of digit patterns and their connections to Diophantine equations. He also contributes to the study of rotational beta expansions and irrationality exponents through symbolic dynamics and Padé approximations.

• Analytic Number Theory
• Elementary Number Theory
• Combinatorics
• +۷ مورد دیگر

Yuta Suzuki is an Assistant Professor at the Department of Mathematics, Rikkyo University, specializing in Analytic Number Theory, Elementary Number Theory, and Combinatorics. He earned his Doctor of Science (D.Sc) from Nagoya University in 2019 under the supervision of Professor Kohji Matsumoto. His research focuses on additive problems involving primes, zeta functions, and combinatorial structures. Doctoral Degree: Nagoya University (2019) Advisor: Kohji Matsumoto His work spans topics like Goldbach-type problems , zeta function zeros , amicable tuples , and combinatorial number theory . He has published extensively in journals such as Mathematische Zeitschrift, Journal of Number Theory, and Acta Mathematica Hungarica, with recent research on short intervals in prime sums and Hurwitz zeta function behavior. His publications highlight trends in analytic approaches to additive number theory , including sieve methods, L-function zeros, and combinatorial aspects like Ramsey numbers. He has presented at international conferences in Lithuania, Japan, India, France, and Belgium, focusing on error term estimates and prime distribution. Teaching : Suzuki has taught courses at Tsuda University, Rikkyo University, Nagoya University, and Nagoya City University, including Linear Algebra , Exponential Sums , and Sieve Methods . He is also involved in organizing seminars such as the Hachioji Number Theory Seminar and the Young Mathematicians Conference on Zeta Functions .

Alperen Ergürمشاهده پروفایل

استادیار

• Real algebraic geometry
• Convex geometry
• Randomized numerical algorithms
• +۵ مورد دیگر

Alperen Ergür is an Assistant Professor at the University of Texas at San Antonio , affiliated with the College of Sciences and Department of Mathematics. His teaching integrates modern mathematical and computational skills, including courses on probability theory, optimization, algorithmic foundations of data science, reinforcement learning, and computational abstract algebra. Ph.D. in Mathematics from Texas A&M University M.S. in Mathematics from TOBB University of Science and Technology B.S. in Mathematics from Bilkent University Research spans real algebraic geometry , convex geometry , randomized numerical algorithms , and reinforcement learning . Key trends in his publications include: Polynomial system analysis Tropical geometry applications Tensor rank approximation Algorithm complexity in computational geometry Probabilistic methods in optimization Homotopy algorithms for real zeros He organizes the UTSA Algorithms Seminar and co-founded a student-friendly research seminar on Geometry, Probability, and Computing with Grigoris Paouris and Petros Valettas. With Suleyman Tek and Rodrigo Velez, he established a creative/competitive math circle for middle schoolers.

Huijing Chenمشاهده پروفایل

مدرس ارشد

• Time series modelling
• Predictive data analytics
• Machine Learning
• +۵ مورد دیگر

Dr. Huijing Chen is a Senior Lecturer in Operations Management at the University of Portsmouth, affiliated with the School of Organisations, Systems and People within the Faculty of Business and Law. She holds a PhD in short-term seasonal demand forecasting and is Deputy Director of NATCOR. Her research focuses on time series modeling, machine learning for predictive analytics, and supply chain learning theories. Previously, she held academic positions at London Metropolitan University and the University of Salford. Her research interests include seasonal demand forecasting, reliability in healthcare systems, and operational research methodologies. She has secured grants from EPSRC, Innovate UK, and the British Academy, and is part of the Centre for Operational Research and Logistics. Her work spans over a decade, with notable contributions to forecasting techniques, inventory management, and Bayesian approaches to spare parts optimization. She collaborates with institutions like the European Journal of Operational Research and the International Journal of Forecasting.

• Operations Management
• Supply Chain Management
• Statistics
• +۴ مورد دیگر

Dr. Avi H. Giloni serves as Associate Dean, Chair of the Information and Decision Sciences Department, and Associate Professor of Operations Management and Statistics at Yeshiva University's Sy Syms School of Business. He holds a PhD in Statistics and Operations Research from New York University's Stern School of Business (2000), along with prior degrees from NYU. His research focuses on robust forecasting, optimization, stochastic systems, and their applications to supply chain management. He teaches courses emphasizing randomness modeling and risk analysis in Operations Management and Statistics. His work bridges theoretical advancements in robust statistical methods (e.g., LAD regression) with practical challenges in supply chain coordination, inventory management, and demand forecasting. Notable contributions include analyzing ARMA demand models, information sharing mechanisms, and inventory policy optimization. He has published extensively on topics like cluster analysis for aggregated demand forecasting and the impact of exponential smoothing techniques. Dr. Giloni's recent research trends highlight advancements in supply chain transparency through information sharing, minimizing forecasting errors via clustering methodologies, and evaluating robust statistical techniques under varying conditions. His articles often address the interplay between operational decisions and systemic risks in dynamic environments. He advises on graduate studies as the school's area coordinator for Information and Decision Sciences, fostering academic-industry collaboration through Yeshiva University's multi-campus presence (Wilf and Beren). His offices are located at both campuses, reflecting his active role in campus administration and pedagogy.

Kristian Debrabantمشاهده پروفایل

دانشیار

• Numerical Analysis
• Stochastic Models
• Numerical Methods and Simulation
• +۷ مورد دیگر

Kristian Debrabant is an Associate Professor at the Department of Mathematics and Computer Science, Faculty of Science and Engineering, University of Southern Denmark. He is also affiliated with the STEM Center for Educational Research – FNUG, highlighting his dual engagement in research and pedagogical development. His academic credentials include a Dr.rer.nat. degree, reflecting a strong foundation in mathematical sciences. Department: Department of Mathematics and Computer Science Position: Associate Professor in Computational Science Additional Affiliation: STEM Center for Educational Research – FNUG Email: debrabant@imada.sdu.dk ORCID: https://orcid.org/0000-0003-2901-162X His research lies at the intersection of numerical analysis and stochastic modeling, with a focus on developing and analyzing numerical methods for stochastic differential equations (SDEs). Key areas include Runge-Kutta methods, exponential integrators, weak and strong convergence, and applications in computational science. He also contributes to interdisciplinary work, such as urban traffic modeling and environmental systems. Recent publications show a consistent trend in advancing numerical schemes for SDEs, particularly those with non-standard noise (e.g., fractional Brownian motion), stiff systems, and non-globally Lipschitz coefficients. His work spans theoretical developments in convergence and order conditions to practical implementations in finance, ecology, and engineering. The integration of B-series and tamed schemes reflects a deep engagement with both classical and modern numerical frameworks. Kristian Debrabant supervises PhD students, as indicated by the 'PhD supervision (2)' note in his profile. While specific grant details are not listed, his publication record and active research suggest ongoing funding support. His involvement with the STEM Center implies leadership or significant contribution to educational innovation in STEM disciplines.

• Number Theory
• Analytic Number Theory
• Multiplicative Number Theory
• +۴ مورد دیگر

Cécile Dartyge is an HDR lecturer at the Faculty of Science and Technology, University of Lorraine, France, affiliated with the Institut Élie Cartan de Lorraine (IECL) and actively contributing to the Analysis and Number Theory research team. Her position reflects a permanent academic appointment with research supervision privileges granted through her Habilitation à Diriger des Recherches. Her research centers on analytic number theory with specialized focus areas including multiplicative properties of polynomial values, sums of ellipsephic digits and integers, partitions of integers, and pseudo-random sets. These investigations explore deep structural properties of integer sequences and their arithmetic behavior, often employing sophisticated analytic techniques to uncover distribution patterns and asymptotic properties. Analysis of her publication record since 2018 reveals consistent emphasis on friable (smooth) integers, prime factorization of polynomial values (particularly quartic cases), and exponential sums. Her work demonstrates strong interdisciplinary connections between combinatorial number theory, digital expansions, and cryptographic applications, frequently appearing in high-impact mathematics journals. No scientific awards are documented in available sources. Information regarding doctoral students or grant funding is not specified in current records, though her HDR qualification confirms extensive research supervision experience. Dr. Dartyge maintains active collaboration within the IECL's Analysis and Number Theory group, participating in seminars and research initiatives that advance theoretical frameworks in multiplicative and additive number theory.

• Geometric Group Theory
• Analysis on Metric Spaces
• Hyperbolic Geometry
• +۳ مورد دیگر

Dr. John M Mackay is an Associate Professor in Pure Mathematics at the School of Mathematics, University of Bristol. His research focuses on geometric group theory and analysis on metric spaces, particularly hyperbolic and relatively hyperbolic groups. Geometric Group Theory Analysis on Metric Spaces Hyperbolic Geometry Conformal Dimension Low-Dimensional Topology He has published extensively on topics such as asymptotic dimension, quasisymmetric maps, and Poincaré profiles, with recent works exploring connections between Lie groups and coarse geometry. His research projects include "Sublinear Geometry of Lie Groups" and "Optimal geometric structures for hyperbolic groups." Dr. Mackay has participated in conferences like the IPAM Workshop on Non-Smooth Geometry and Young Geometric Group Theory II as an invited speaker.

• Mathematical Physics
• Probability Theory
• Random Matrix Theory
• +۵ مورد دیگر

Gaultier Lambert is an Associate Professor at the Department of Probability, Mathematical Physics and Statistics, KTH Royal Institute of Technology. His research focuses on the intersection of probability theory, mathematical physics, and random matrix theory, with particular emphasis on determinantal processes, log-correlated fields, and quantum statistics. He holds a PhD and has an extensive publication record in high-impact journals. Key research areas include fluctuations in free fermionic systems, multiplicative chaos, and universality in random matrix ensembles. His work explores topics such as characteristic polynomials of random matrices, law of large numbers for Coulomb gas potentials, and quantum statistics transmutation via magnetic flux attachment. Recent publications (2021–2025) emphasize asymptotic analysis of eigenvalue fluctuations, universality in semiclassical systems, and stochastic processes in high-dimensional spaces. Lambert’s contributions bridge theoretical probability with applications in quantum physics and statistical mechanics. No scientific awards or grants are explicitly listed in the provided texts. He advises no listed students and is affiliated with the Lindstedtsvägen 25 research group at KTH.

• Integer-Valued Time Series Analysis
• Extreme Value Theory
• Financial Modelling
• +۳ مورد دیگر

Thomas Fung is an Associate Professor in the School of Mathematical and Physical Sciences at Macquarie University. He currently serves as Director of Education and Deputy Course Director for the BSc program. His academic roles include teaching and research supervision, alongside community engagement in statistical societies. Thomas holds a PhD in Statistics from the University of Sydney (2010) and a B.Sc (Advanced Mathematics) with First Class Honours from the same university (2006). His research focuses on advanced statistical methodologies, including: Integer-valued time series analysis Extreme value theory Financial modelling with a focus on complex data Flexible regression models and statistical distributions such as Conway-Maxwell-Poisson (CMP) and Variance-Gamma Thomas has been recognized for his contributions with several awards, including: Faculty of Science and Engineering Excellent Award for Educational Leader (2023) Teaching Development Grants in 2024 and 2022 Inter-School Collaboration Award (2023) In addition to his teaching, Thomas actively supervises postgraduate students and has led research projects such as: Flexible Models for Time-Series of Counts (ongoing) Improving financial time series models using the Variance-Gamma process Thomas is engaged with professional bodies including the International Statistical Institute and the Statistical Society of Australia, and contributes to academic journals as an Associate Editor.

Tobias Riedمشاهده پروفایل

استادیار

• Partial differential equations
• Calculus of variations and optimal control
• Statistical mechanics
• +۴ مورد دیگر

Tobias Ried is a tenure-track Assistant Professor in the School of Mathematics at Georgia Institute of Technology, where he conducts research at the intersection of partial differential equations, mathematical physics, and statistical mechanics. Prior to joining Georgia Tech, he served as a group leader in Felix Otto's research group at the Max Planck Institute for Mathematics in the Sciences in Leipzig, Germany. His work is supported by NSF grant DMS-2453121 and focuses on fundamental problems in kinetic theory, optimal transport, and micromagnetics. Ried's research interests span a wide range of mathematical topics with strong connections to physical applications. His primary focus lies in partial differential equations and their applications to problems in statistical mechanics and mathematical physics. He has made significant contributions to the theory of kinetic equations including the Boltzmann and Kac models, optimal transport theory with applications to economics and machine learning, and micromagnetics with relevance to materials science. His work often combines analytical techniques from calculus of variations with insights from probability theory and functional analysis. Analysis of Ried's publication record reveals a strong trend toward interdisciplinary mathematical research that bridges theoretical mathematics with concrete physical applications. His early work focused on kinetic theory and nonlinear wave phenomena, particularly examining smoothing properties of solutions to Boltzmann-type equations. More recently, his research has expanded into optimal transport theory, where he has investigated regularity properties of transport maps and Wasserstein barycenters, and micromagnetics, where he has established precise scaling laws for domain branching phenomena. This evolution demonstrates a consistent focus on variational methods applied to complex physical systems. Ried has established productive collaborations with prominent researchers in mathematical physics including Dirk Hundertmark, Felix Otto, and Michael Loss. His work has appeared in top-tier mathematics journals such as Communications in Mathematical Physics, Inventiones Mathematicae, and Archive for Rational Mechanics and Analysis. While specific awards are not documented in the available information, his research has generated significant scholarly impact with his publications cited over 60 times according to zbMATH Open. Ried maintains an active research program supported by NSF funding, with recent work exploring singular stochastic partial differential equations, variational problems related to spectral inequalities, and the mathematical foundations of micromagnetic phenomena. His research group at Georgia Tech likely focuses on advancing the theoretical understanding of complex systems through rigorous mathematical analysis, though specific details about students and postdoctoral researchers are not provided in the available documentation.

• Mathematical Foundations of Machine Learning
• Online Convex Optimization
• Statistical Learning Theory
• +۵ مورد دیگر

Dr. Tim van Erven is an Associate Professor at the University of Amsterdam , affiliated with the Korteweg-de Vries Institute for Mathematics . His research focuses on the mathematical foundations of machine learning , particularly adaptive methods in online convex optimization, PAC-Bayesian concentration inequalities, and robust learning strategies. Research Interests : Mathematical machine learning, explainable AI, online learning, statistical learning theory, and adaptive optimization. Grants & Awards : VICI (2025), VIDI (2019), TOP grant (2016), NIPS Outstanding Reviewer (2014), Rubicon (2011). Teaching : Involved in graduate-level machine learning and statistical learning theory courses. His recent work explores explainable machine learning and robust optimization , with a focus on algorithmic recourse, gradient filtering, and theoretical guarantees. His publications span top venues like NeurIPS, COLT, and JMLR, often addressing the intersection of statistics and machine learning. He co-organized workshops such as the AI & Mathematics (AIM) initiative and contributed to the COLT program committee. His group develops MetaGrad and other adaptive algorithms for online learning.

Frank van der Meulenمشاهده پروفایل

استاد

• Statistical Inference for Stochastic Processes
• Bayesian Computation
• Filtering Methods
• +۴ مورد دیگر

Frank van der Meulen is a Professor of Mathematical Statistics at the Vrije Universiteit Amsterdam (VU), where he leads the Department of Mathematics. He holds leadership roles including Director of the VU Bachelor Programme in Business Analytics, Chair of the Mathematical Statistics section at the Netherlands Society for Statistics and Operations Research (VVSOR), and Organizer of the nationwide Van Dantzig seminar. His research focuses on statistical inference for stochastic processes, Bayesian computation, and applications in fields like sports engineering, climate science, and maritime engineering. He has developed influential methods such as 'backward filtering forward guiding' for simulating conditioned Markov processes. Frank obtained his PhD in 2005 from VU Amsterdam and has held academic positions at TU Delft (2007–2022) before returning to VU as a Full Professor in 2022. He teaches courses in statistics, probability, and data science, and has authored packages like BridgeLandmarks and BayesianDecreasingDensity. His work bridges theoretical advancements with practical applications, emphasizing collaboration across disciplines. Research interests include Bayesian computational methods (MCMC, SMC), stochastic differential equations, and inference for diffusions on manifolds. Recent work explores stochastic phylogenetic models and fatigue calculations for maritime structures. He consults on statistical and machine learning projects, offering expertise through his GitHub repositories and published software tools.