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Patricia Alonso RuizView profile

Associate Professor

• Analysis
• Probability
• Fractal Geometry
• +3 more

Patricia Alonso Ruiz is an Associate Professor in the Department of Mathematics at Texas A&M University (on leave AY24/25). She specializes in analyzing random phenomena in fractal-like environments, focusing on connections between analysis, probability, and geometry. Her research involves Dirichlet forms, heat kernels, and stochastic processes on fractals such as the Sierpinski gasket and diamond fractals. Her academic journey includes leadership in the TAMU Fractals Research Team, supported by an NSF grant. She has organized conferences like the BIRS Workshop on New Trends in Stochastic Analysis and the Cornell Conference on Analysis, Probability, and Mathematical Physics on Fractals. Her outreach efforts include the Kenya Workshop and educational programs like the Summer Educational Enrichment in Math. Key research areas include spectral theory of fractals, BV functions on metric measure spaces, and functional inequalities in fractal settings. Collaborations with researchers like Fabrice Baudoin and Alexander Teplyaev have yielded impactful work on heat kernel analysis and Besov spaces. She mentors undergraduate teams exploring spectral features of fractals, fostering the next generation of mathematicians. Publications span topics from nonlocal energies to Orlicz-Sobolev embeddings, reflecting her interdisciplinary approach bridging pure and applied mathematics. Her work highlights the interplay between fractal geometry and stochastic analysis, advancing understanding of complex systems in porous and non-smooth environments.

Pierre PerronView profile

Professor

• Econometrics
• Time Series Analysis
• Structural Change Analysis
• +4 more

Pierre Perron is a Professor of Economics at Boston University, specializing in econometrics and time series analysis. He holds positions in the Department of Economics, with a focus on structural change, unit root testing, and applications in climate change and financial markets. He received his PhD from Yale University. Education: PhD in Economics, Yale University Research Interests: Development of econometric methods for time series with structural breaks and unit roots Applications to climate change, financial markets, and macroeconomic policy Advances in statistical inference for nonstationary processes Key Awards: Fellow of the Econometric Society Clarivate Citation Laureate (2020) Doctor Honoris Causa from Pontificia Universidad Católica del Perú (2023) Recipient of the Distinguished Author Award (2022) Advising & Grants: Supervised numerous PhD students in econometrics, with many contributing to theoretical and applied research globally. His work has been supported by grants and recognized through high-impact publications. Publications: Authored/co-authored over 150 articles and books, including two volumes on Time Series Econometrics (2019) and cutting-edge work on structural change and climate modeling.

Michael BronsteinView profile

Professor

• Geometric Deep Learning
• Graph Representation Learning
• Computer Vision
• +4 more

Affiliations and Roles Michael Bronstein is a Professor at the Università della Svizzera italiana (USI) in the Faculty of Informatics and the Institute of Computational Science . He holds the Chair in Machine Learning and Pattern Recognition at Imperial College London and serves as Head of Graph Learning Research at Twitter . Previously, he was affiliated with the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) as an Adjunct Professor. Education Ph.D. in Computer Science, Technion–Israel Institute of Technology (2007) Visiting appointments at Stanford University, MIT, Harvard University, and Tel Aviv University Research Interests Bronstein's work focuses on geometric deep learning , graph representation learning , and non-rigid shape analysis . He pioneered methods for extending machine learning to non-Euclidean domains like graphs and manifolds. His research combines theoretical advancements in spectral geometry with practical applications in computer vision, robotics, and medical imaging. Publications Trends His articles emphasize geometric deep learning frameworks, functional maps for shape correspondence, and spectral methods for manifold analysis. Key themes include invariant representations, partial shape matching, and applications in 3D reconstruction and graph neural networks. Awards and Honors Five ERC Grants Royal Society Wolfson Research Merit Award IEEE and IAPR Fellowships World Economic Forum Young Scientist Advising and Entrepreneurship Bronstein is a serial entrepreneur, founding companies like Novafora , Invision (acquired by Intel), and Fabula AI (acquired by Twitter). His academic advising spans PhD and Master’s students in machine learning and geometry processing. Labs and Teams Active in the Institute of Computational Science at USI and leads Twitter’s Graph Learning Research team, focusing on real-world applications of geometric deep learning.

• Algebraic Geometry
• Topology
• Homotopy Theory
• +6 more

Frank Neumann is a researcher affiliated with the University of Pavia , specifically within the Algebra and Geometry research group. His work focuses on advanced topics in algebraic geometry, topology, and homotopy theory, particularly moduli stacks, principal bundles, and spectral sequences. Research Areas : Algebraic Geometry, Homotopy Theory, Moduli Stacks, Principal Bundles, Topology, Arithmetic Geometry. The titles of his recent publications highlight his contributions to cohomology theories, geometric structures, and their interactions with number theory and differential geometry. His work often bridges abstract algebraic geometry with topological and homotopical methods. Despite no explicit mention of awards or student supervision in the provided texts, his research outputs indicate sustained engagement with foundational mathematical problems. He is contactable via email at frank.neumann@unipv.it .

Douglas W. CarterView profile

Assistant Professor

• Experimental turbulent flows
• Particle-turbulence interaction
• Noise generation of separated flows
• +7 more

Douglas W. Carter is an Assistant Professor in the Department of Mechanical, Materials, and Aerospace Engineering at the Armour College of Engineering, Illinois Institute of Technology. He leads the Experimental Turbulent Flows Lab, focusing on advanced experimental techniques for fluid dynamics research. Education: Ph.D., University of Minnesota, 2019 M.S., University of Minnesota, 2017 B.S., University of New Hampshire, 2014 Research Interests: Dr. Carter investigates experimental turbulent flows, particle-turbulence interactions, and noise generation in separated flows. His expertise spans particle tracking velocimetry, hypersonics, compressible flows, and data-driven methods for fluid systems. Research emphasizes experimental validation of turbulence models and development of novel diagnostic tools like FLEET velocimetry. Publications: Recent work explores hypersonic flow diagnostics, pressure reconstruction in stalled airfoils, and turbulence cascade dynamics. Publications demonstrate consistent focus on experimental fluid mechanics, high-speed flow measurements, and low-order modeling for aerodynamic prediction. Laboratory: The Experimental Turbulent Flows Lab (Rettaliata Engineering Center) develops cutting-edge techniques for turbulent flow analysis, including multi-scale imaging and optical diagnostics for high-speed applications.

Panagiotis DimitrakopoulosView profile

Associate Professor

• Computational Fluid Dynamics
• Multiphase Flows
• Mesoscale Polymer Dynamics
• +3 more

Panagiotis (Panos) Dimitrakopoulos is an Associate Professor in the Department of Chemical & Biomolecular Engineering at the University of Maryland, College Park. His research focuses on computational fluid dynamics, multiphase flows, and biophysical systems. He specializes in modeling elastic capsules, microfluidic devices, and polymer dynamics using advanced numerical algorithms. His work intersects fluid mechanics, biomedical engineering, and materials science. He has developed spectral boundary element methods for interfacial dynamics and contributed to understanding capsule deformation in microfluidic environments. His studies address drug delivery systems, microfluidic sorting mechanisms, and hemodynamic forces in biological systems. Key applications include designing next-generation drug carriers and analyzing blood cell mechanics in microcirculation. Notable contributions include analyzing elastic capsule migration in converging micro-capillaries, multi-compartment micro-capsule fabrication, and non-Newtonian fluid dynamics in confined geometries. His research spans from theoretical fluid mechanics to biomedical applications, emphasizing computational modeling and experimental validation.

• Statistics
• High-Dimensional Data Analysis
• Statistical Genetics
• +7 more

Aaron J Molstad is an Assistant Professor in the Department of Statistics at the University of Minnesota – Twin Cities, within the College of Science and Engineering. His research lies at the intersection of statistical methodology and genomic data science, with a focus on developing rigorous and scalable methods for modern high-dimensional datasets. His research interests include high-dimensional statistics, covariance and precision matrix estimation, regression modeling with structured responses, variable selection, and integrative analysis of omics data. He develops methods tailored for compositional data, multivariate responses, and ancestry-specific genetic association studies, contributing to both theoretical statistics and public health applications. The recent publications and funded projects highlight a strong trend in developing objective, reliable, and heterogeneous-aware statistical frameworks for genomics and biomedicine. His work emphasizes methodological innovation with direct applicability to complex biological data, particularly in diverse populations and multi-omics integration. Awarded grants from the National Science Foundation and the National Institutes of Health demonstrate recognition of his research’s significance and impact. These include projects on inference from omics data, new regression models for categorical responses, and integrative genomics in African American populations. Objective and reliable methods for inference from modern omics data (NSF, 2024–2027) Collaborative Research: New Regression Models for Multiple Categorical Responses (NSF, 2024–2025) Integrative Genomics into Genetic Association Studies of Blood Pressure and Stroke in African Americans (NIH/Fred Hutchinson, 2023–2024) Dr. Molstad advises and collaborates on major genomic studies involving protein expression, blood pressure, stroke, and ancestry-specific effects. While specific PhD students are not listed, his role as Principal Investigator on multiple grants indicates mentorship of graduate researchers and postdoctoral scholars. He is also active in the broader statistical community, with publications in top-tier journals such as Biometrika , Biometrics , and Genome Biology .

• Crystal Plasticity
• Materials Science
• Computational Materials Engineering
• +5 more

Martin Diehl is a computational materials scientist affiliated with KU Leuven (Departments of Computer Science and Materials Engineering) and the Max-Planck-Institut für Eisenforschung GmbH in Germany. His work focuses on crystal plasticity simulations, computational materials engineering, and multi-physics modeling of metallic systems. Research interests include: Crystal plasticity finite element method (CPFEM) and spectral solvers Microstructure evolution and damage mechanics Machine learning applications in materials design Development of the DAMASK simulation toolkit Multi-phase steel alloys and heterogeneous deformation Integrated computational materials engineering (ICME) Key trends in his publications since 2021 highlight advancements in: Multi-physics DAMASK framework for coupled chemo-mechanical and thermal simulations AI-driven inverse design of steel microstructures Damage modeling in dual-phase steels Collaborative software development for materials science Experimental-simulation integration for stress-strain partitioning High-resolution spectral methods for finite strain analysis He actively collaborates with institutions like Harbin Institute of Technology, University of Oxford, and research groups across Europe and Asia.

Christopher HowlsView profile

Professor

• Asymptotic Analysis
• Semiclassical Analysis
• Applied Mathematics
• +3 more

Professor Christopher Howls is a Professor of Mathematics and Director of the University Doctoral College at the University of Southampton. He holds a First in Joint Honours Mathematics and Physics from the University of Bristol and a PhD in Mathematical Physics under Sir Michael Berry FRS. His career includes roles at the University of Manchester, Brunel, and Southampton, where he has served as Head of the Applied Mathematics group and Director of the Faculty Graduate School. His research focuses on asymptotic analysis and its applications to quantum mechanics, general relativity, and aeroacoustics. He has supervised numerous PhD students and postdocs across mathematics, physics, and engineering. His funding portfolio includes over £35m in grants. He chairs the British Applied Mathematics Colloquium and has held visiting positions globally. Key roles include editorial board memberships (e.g., Proceedings of the Royal Society A) and leadership in EPSRC initiatives like the Meet the Mathematicians outreach program. His awards include Fellowships from the Institute of Mathematics and the Higher Education Academy. He has authored over 70 publications and delivered over 70 invited talks worldwide.

Bruno OlshausenView profile

Professor

• Neuroscience
• Computational Neuroscience
• Theoretical Neuroscience
• +5 more

Bruno Olshausen is a Professor at the University of California, Berkeley, holding appointments in the Helen Wills Neuroscience Institute and the School of Optometry. He also directs the Redwood Center for Theoretical Neuroscience, focusing on mathematical and computational models of brain function. His research explores visual system processing, sparse coding, and neural mechanisms underlying perception. Olshausen earned his B.S. and M.S. in Electrical Engineering from Stanford University and a Ph.D. in Computation and Neural Systems from Caltech. Previously, he was on the faculty at UC Davis (1996–2005) before joining UC Berkeley. Education : Ph.D. in Computation and Neural Systems, California Institute of Technology, 1994 M.S. in Electrical Engineering, Stanford University, 1987 B.S. in Electrical Engineering, Stanford University, 1986 Research Interests : Olshausen's work bridges neuroscience and computer science, emphasizing the development of computational models for understanding visual processing, sparse coding, and neural representation. His lab explores topics such as optic flow analysis, hierarchical scene representation, and neuromorphic systems. Key themes include the study of neural circuits, probabilistic models of perception, and the application of these insights to AI and data compression. Grants & Labs : Director of the Redwood Center for Theoretical Neuroscience Recipient of grants in computational neuroscience and neuromorphic engineering Labs/Teams : His research group collaborates on projects involving neural network models, analog computing with emerging memory systems, and hyperdimensional computing architectures.

Wolfgang ReichelView profile

Professor

• Nonlinear Partial Differential Equations
• Mathematical Physics
• Wave Phenomena
• +4 more

Prof. Dr. Wolfgang Reichel is a faculty member at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and leading the Workgroup Nonlinear Partial Differential Equations . He serves as deputy speaker of the Collaborative Research Center (CRC) 1173 "Wave Phenomena: Analysis and Numerics", and is a speaker for the KIT-Center MathSEE "Mathematics in Sciences, Engineering, and Economics". His research focuses on nonlinear partial differential equations, mathematical modeling, and numerical analysis of wave phenomena. His workgroup includes postdocs, doctoral researchers, and master students, and he collaborates with colleagues like Thomas Bartsch, Bernd Kawohl, Michael Plum, Guido Sweers, and Tobias Weth. Regular events organized by Prof. Reichel include the Karlsruhe PDE-Seminar and annual Nonlinear PDE Days summer/winter schools. He has taught courses such as Rand- und Eigenwertprobleme, Functional Analysis, and Analysis of PDEs across multiple semesters. Scientific publications reveal a focus on nonlinear wave equations (e.g., Maxwell's, Klein-Gordon, Lugiato-Lefever), soliton dynamics, frequency comb generation, and symmetry properties in elliptic PDEs. Articles often address existence proofs, stability analysis, and numerical methods for wave phenomena in periodic media and nonlinear optics. Collaborative work spans institutions like MacMaster University, University of Stuttgart, and University of Connecticut.

Hilário AlencarView profile

Professor

• Differential Geometry
• Mathematics Education
• Geometric Analysis
• +2 more

Hilário Alencar is a full Professor at the Federal University of Alagoas (UFAL), affiliated with the Institute of Mathematics. He holds significant leadership roles, including Coordinator of the Center for Research in Computational Mathematics (CPMAT/UFAL) and President of the Commission for Monitoring and Evaluation of the Management Contract (MCTI/IMPA). Previously, he served as President of the Brazilian Mathematical Society (2009–2013; 2015–2017) and Provost of Graduate Studies and Research at UFAL (1995–2000). His research focuses on Differential Geometry , particularly hypersurfaces, curvature flows, and geometric analysis, with additional interests in mathematics education for basic education. He co-founded the PROFMAT national professional master's program in mathematics. Alencar has advised 3 doctoral and 21 master's students, emphasizing geometric analysis and applied mathematics. His recent publications (2014–2025) explore rigidity theorems, self-shrinkers, and geometric inequalities in Riemannian manifolds. Awards and Honors: Order of Scientific Merit (Commander and Grand Cross classes) University Merit Medal (UFAL 45 years) FAPEAL Merit Commendation Honorary Member of the Brazilian Mathematical Society Full Member of the Brazilian Academy of Sciences and The World Academy of Sciences (TWAS) He leads the Computational Mathematics Research Center (CPMAT) and has coordinated major projects funded by CNPq, CAPES, and FINEP, strengthening mathematical infrastructure and education in Brazil.

Megan DewdneyView profile

Associate Professor

• Plant Pathology
• Citrus Disease Management
• Fungal Pathogens
• +3 more

Dr. Megan Dewdney is an Associate Professor and Extension Specialist in the Department of Plant Pathology at the University of Florida's Citrus Research and Education Center. Her extension work focuses on integrated management strategies for citrus canker, citrus greening (huanglongbing), and fungal pathogens, while her research investigates the biology of bacterial and fungal pathogens to develop enhanced disease control methods. She holds a Ph.D. and leads active research programs in citrus pathology. Her primary research interests include: Integrated disease management systems for citrus pathogens Biology and epidemiology of fungal and bacterial diseases in citrus Development of molecular detection tools for plant pathogens Implementation of precision agriculture technologies for disease forecasting Physiological impacts of huanglongbing on citrus trees Analysis of her recent publications reveals strong emphasis on: Advanced detection methods using hyperspectral imaging and machine learning Genomic studies of fungal pathogens like Phyllosticta citricarpa Development of web-based disease advisory systems for growers Physiological management of huanglongbing-affected trees Molecular characterization of citrus pathogens She currently advises doctoral student Eva Mulandesa and collaborates with postdoctoral researcher Pamela Suellen. Her laboratory team includes research technicians, a lab manager, and program support staff focused on citrus pathology research.

Tamer ÖlmezView profile

Professor

• Biomedical Signal Processing
• Brain-Computer Interfaces
• Deep Learning
• +7 more

Tamer Ölmez is a Professor in the Department of Electronics and Communication Engineering at Istanbul Technical University (ITU), College of Engineering, where he conducts cutting-edge research in biomedical signal processing, brain-computer interfaces (BCI), and deep learning applications in medical systems. His work bridges engineering and neuroscience, with a strong focus on EEG-based motor imagery classification, medical image analysis, and embedded deep learning systems. His research interests include motor imagery EEG signal processing , brain-computer interfaces , feature extraction , deep neural networks , classification algorithms , and medical image analysis . He applies machine learning and signal processing techniques to improve diagnostic accuracy and system performance in neuroengineering and healthcare technologies. The recent publications highlight a consistent trend in leveraging divergence-based deep neural networks , convolutional neural networks , and small-sized models for efficient and accurate classification in BCI and medical imaging. His work emphasizes performance improvement with reduced channel counts, noise elimination, and real-time applicability in embedded systems. Scientific Awards: Excellent Oral Presentation Certificate, June 1, 2015 Advising and Grants: He is actively supervising 26 theses in progress, indicating a strong mentoring role. He has led multiple funded research projects, including those funded by ITU’s Technology Transfer Office (TTO) and Scientific Research Projects (BAP), such as 'Classification of Medical Images with Deep Learning Method in Embedded Systems' and 'New Approaches to Finding Optimal Protein Folding'. Labs and Research Teams: While specific lab names are not mentioned, his collaborative fingerprint and project leadership suggest he leads or is a key member of a research group focused on biomedical signal processing, neural networks, and intelligent systems at ITU.

Xingye QiaoView profile

Professor

• Statistics
• Machine Learning
• Causal Inference
• +3 more

Xingye Qiao is a Professor and Chair in the Department of Mathematics and Statistics at Binghamton University, State University of New York . He serves as Chair of the Data Science Transdisciplinary Area of Excellence steering committee and has been affiliated with Binghamton since 2010. Education : Ph.D. in Statistics (2010), University of North Carolina at Chapel Hill M.S. in Statistics (2007), University of North Carolina at Chapel Hill B.S. in Mathematics and Applied Mathematics (2005), Fudan University His research focuses on Statistics, Machine Learning, and Causal Inference , with recent work exploring conformal prediction methods, treatment effect estimation, and set-valued classification techniques. His publications span journals like Transactions on Machine Learning Research , NeurIPS , and AAAI . His 15 most recent articles (2025-2020) demonstrate expertise in areas including: bandit feedback systems, treatment effect heterogeneity analysis, spectral clustering for neuroscience, and conformal prediction under distribution shifts. These works blend statistical theory with real-world applications in healthcare, ecology, and data science. He mentors Ph.D. students in mathematical sciences and has supervised research topics such as: machine learning in precision medicine, goodness-of-fit tests for spatial processes, and high-dimensional data analysis. Courses he teaches include Math 605: Theory of Machine Learning and Data 501: Predictive & Inferential Analytics .