جست‌وجوی استاد و پژوهشگر

Valentino Magnaniمشاهده پروفایل

دانشیار

• Differential Geometry
• Geometric Analysis
• Sub-Riemannian Geometry
• +۵ مورد دیگر

Valentino Magnani is an Associate Professor in the Department of Mathematics at the University of Pisa, Italy. His research focuses on geometric analysis, sub-Riemannian geometry, and geometric measure theory, with an emphasis on topics such as area formulas, rectifiability, and the properties of measures in homogeneous groups. He has authored over 50 publications in prestigious journals like the Journal of Geometric Analysis, Advances in Mathematics, and Calculus of Variations and Partial Differential Equations. His work explores the interplay between differential geometry and analysis in non-Euclidean settings, including studies on submanifolds, Sobolev spaces, and coarea formulas. Magnani has organized and spoken at numerous international conferences, such as the 'Three Days in Sub-Riemannian Geometry' and 'Geometric Measure Theory in Trento.' He also contributes to academic activities through teaching advanced courses on geometric analysis and metric spaces, including PhD-level lectures on topics like area theory in homogeneous groups. Key research interests include the Michor-Mumford conjecture, symmetry properties in geometric structures, and the application of rough calculus to level sets in Heisenberg groups. His contributions have advanced understanding of measure-theoretic principles and geometric properties in stratified Lie groups and sub-Riemannian manifolds.

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

Michael Novack is an Assistant Professor at Louisiana State University. His research focuses on calculus of variations, geometric measure theory, and partial differential equations. He previously held postdoctoral positions at Carnegie Mellon University, the University of Texas at Austin, and the University of Connecticut. He earned his Ph.D. from Indiana University. His work addresses problems in geometric analysis, including free boundary regularity, isoperimetric inequalities, and liquid crystal models. Key topics include phase transitions in nematics, smectic liquid crystal configurations, and capillarity theory. Recent contributions involve rigorous analysis of Plateau’s laws via Allen-Cahn equations and infinite double bubble theorems. Notable publications explore energy minimization in liquid crystals, regularity criteria for hypersurfaces, and Wasserstein distance optimizations. His research bridges pure and applied mathematics, with applications in materials science and geometric modeling. He has delivered specialized lectures on Γ-convergence and contributed to workshops at Oberwolfach.

Leonid V. Kovalevمشاهده پروفایل

استاد

• Geometric Mapping Theory
• Metric Geometry
• Functional Analysis
• +۶ مورد دیگر

Leonid V. Kovalev is a Professor and Associate Chair of the Department of Mathematics at Syracuse University, within the College of Arts and Sciences. His research focuses on geometric mapping theory, metric space analysis, complex analysis, and potential theory. He holds leadership roles in undergraduate mathematics education and has contributed to the field through numerous NSF-funded projects. Education details are not explicitly listed, but his academic trajectory is evident through his extensive research output and faculty position. His work bridges pure mathematics disciplines such as geometric analysis, functional analysis, and complex variables, with applications to variational problems and harmonic mappings. Recent research emphasizes topics like Lipschitz properties in metric spaces, trigonometric polynomials, and geometric variational problems. His articles explore theoretical foundations and computational aspects of these areas, often intersecting with Fourier analysis and operator theory. Grants: NSF-funded projects include 'Lipschitz Analysis in Normed and Metric Spaces' (2018–2022) and 'NEAM 2019: The 4th Northeastern Analysis Meeting' (2019–2020). Teaching: Courses include Functional Analysis, Numerical Methods, and Calculus for Business and Life Sciences. Service: Serves as Associate Chair for Undergraduate Studies and advises Mathematics majors/minors. Administers WeBWorK course platforms. Labs/Teams: Collaborates on projects involving geometric analysis and metric space theory through his research group and conference organizing activities.

• Time Series Analysis
• Extreme Value Theory
• Financial Modelling
• +۵ مورد دیگر

Thomas Fung is a Senior Lecturer in Statistics at Macquarie University's School of Mathematical and Physical Sciences, serving as Director of Education and Deputy Course Director for the BSc program. He holds a PhD and B.Sc (Advanced Mathematics) from the University of Sydney. His research focuses on integer-valued time series analysis, extreme value theory, and financial modelling, addressing complex data issues like zero-inflation and heavy-tailedness. He teaches courses such as STAT1378 (Coding & Communication in Statistics) and actively supervises postgraduate students on projects like semiparametric models and climate data analysis. Key research projects include flexible models for count time series and improving financial time series analysis via Variance-Gamma processes. His work has been published in journals like Statistics and Probability Letters and Earth and Space Science . Awards include the 2023 Faculty of Science and Engineering Excellent Award for Educational Leadership and multiple Teaching Development Grants. He serves on editorial boards for the Australian and New Zealand Journal of Statistics and R Journal , and holds leadership roles in professional societies like the Statistical Society of Australia NSW Branch. His interdisciplinary collaborations span environmental science, public health, and criminology, reflecting his broad statistical expertise.

Francesco D'Angeloمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Deep Learning
• +۳ مورد دیگر

Francesco D'Angelo is a Researcher and Doctoral Assistant at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Computer and Communication Sciences (IC) and the Institute of Computer Science and Communications Systems (IINFCOM). He is part of the Theory of Machine Learning Laboratory (TML) and enrolled in the Doctoral program in computer and communication sciences (EDIC) under the EPFL Doctoral School (EDOC). His research focuses on foundational aspects of machine learning, including sparse attention mechanisms, Bayesian neural networks, uncertainty estimation, and continual learning. He holds the position of Doctoral Assistant at the TML lab, where he contributes to advancing theoretical and practical applications of machine learning algorithms. Research Interests: Francesco's work bridges theoretical computer science with applied machine learning. His key areas include developing robust models for uncertainty quantification, improving generalization in overparameterized networks through regularization techniques like weight decay, and exploring the interplay between transformer architectures and causal structure induction. He also investigates methods to mitigate catastrophic forgetting in continual learning scenarios and applies Bayesian approaches to enhance out-of-distribution detection capabilities. Publications: His recent work explores topics such as sparse attention mechanisms in transformers, the necessity of weight decay in deep learning optimization, and Bayesian methods for uncertainty estimation. These contributions highlight his expertise in both foundational theory and practical algorithm design within the machine learning domain. Labs & Teams: As part of the TML lab, he collaborates on interdisciplinary projects at the intersection of machine learning theory and applications, contributing to advancements in neural network architectures and probabilistic modeling techniques.

Marita Thomasمشاهده پروفایل

استاد

• Material Modeling
• Damage Mechanics
• Fracture Mechanics
• +۵ مورد دیگر

Marita Thomas is a Professor (W2) for Applied Analysis at Freie Universität Berlin since August 2022 and Head of the Weierstrass group 'Bulk-Interface Processes' at the Weierstrass Institute since April 2017. She holds a PhD in Mathematics from Humboldt-Universität zu Berlin (2010) and a Diplom in Mathematics (with application to chemistry) from the University of Stuttgart (2006). Her research focuses on material modeling, damage and fracture mechanics, variational methods, and the coupling of optoelectronics with thermomechanics. She has led numerous projects including those within CRC 1114 'Scaling Cascades in Complex Systems' and the Berlin Mathematics Research Center MATH+. Education: PhD in Mathematics (Humboldt-Universität, 2010); Diplom in Mathematics (University of Stuttgart, 2006) Key Projects: Materials with discontinuities on multiple scales, Interface dynamics bridging stochastic and hydrodynamic descriptions, Dynamics of rock dehydration Her research integrates analytical approaches with numerical simulations, addressing challenges in continuum mechanics, phase-field fracture, and optoelectronic device optimization. She has organized international workshops and minisymposia, including the Thematic Einstein Semester on Energy-based Mathematical Methods.

Valery Vishnevskiyمشاهده پروفایل

مدرس

• Biomedical Imaging
• Magnetic Resonance Imaging (MRI)
• Ultrasound Imaging
• +۳ مورد دیگر

Dr. Valery Vishnevskiy is a Lecturer at the Department of Information Technology and Electrical Engineering at ETH Zürich , affiliated with the Biomedical Imaging Group . His research focuses on advanced medical imaging techniques, particularly in cardiac MRI , ultrasound , and machine learning applications . He contributes to projects involving 4D flow MRI reconstruction , speed-of-sound imaging , and motion-corrected diffusion tensor analysis . Primary Affiliation : ETH Zürich, Department of Information Technology and Electrical Engineering Research Interests : Biomedical Imaging, MRI Reconstruction, Ultrasound Physics, Cardiac Imaging, Deep Learning His recent work emphasizes self-supervised learning for medical imaging, including the development of FlowMRI-Net for accelerated 4D flow MRI and Speed-of-Sound Imaging using diverging waves. Publications span hyperpolarized 13C metabolic imaging , viscoelasticity reconstruction , and probabilistic sampling optimization . Dr. Vishnevskiy's contributions to deformable image registration include methods for handling sliding interfaces in abdominal and cardiac imaging, with applications in respiratory motion compensation and tissue ablation monitoring . He also explores mathematical models for enzyme activity analysis and behavioral pattern detection.

• Mathematical Analysis
• Numerical Analysis
• Semiconductor Physics
• +۳ مورد دیگر

Nella Rotundo is an Associate Professor (Ricercatrice di tipo B) in Mathematical Analysis (MAT/07) at the University of Florence, Department of Mathematics. Her research focuses on mathematical modeling of semiconductors, partial differential equations, and numerical methods. She has held postdoctoral positions at the Weierstrass Institute (Berlin), McGill University (Montreal), and the University of Calabria. She is a Principal Investigator in the MATH+ Incubator project IN-B3 addressing doping variations in silicon crystals. Her academic background includes a PhD in Mathematics applied to Engineering (University of Catania), and a Master's in Mathematics (University of Calabria). She has contributed to over 9 peer-reviewed publications since 2010, with recent work emphasizing numerical methods for semiconductor simulation and inverse problems in materials science. Rotundo has presented research at conferences including the Young Applied Mathematicians Conference (Rome 2024) and AMaSiS (Berlin). Her technical contributions span finite element methods, optimal control theory, and computational approaches to semiconductor characterization.

Max Russoمشاهده پروفایل

استادیار

• Computational Statistics
• Bayesian Statistics
• Decision Theory
• +۵ مورد دیگر

Max Russo is an Assistant Professor of Statistics at the Ohio State University, where he focuses on developing advanced statistical methodologies for biomedical research and healthcare data analysis. PhD from the University of Padova (2019) Research spans computational statistics, Bayesian modeling, and clinical trial design Key applications: drug safety profiling, high-dimensional data analysis, electronic health records (EHR) Russo's research combines Bayesian statistics with computational innovation , particularly for challenges in clinical trial design and real-world evidence generation . He specializes in: Variational Bayes techniques for scalable inference Signal detection in high-dimensional healthcare databases Bayesian decision theory for adaptive clinical trials Electronic health records (EHR) analysis Pharmacoepidemiology and medication safety His recent publications highlight cross-disciplinary impacts in biostatistics and public health , with a focus on: Drug safety surveillance using claims data Comparative effectiveness of pharmaceutical interventions Algorithmic approaches for complex healthcare datasets Policy implications of clinical trial design Contact: Russo.325@osu.edu

Fred Parkمشاهده پروفایل

استادیار

• Mathematical Image Processing
• Computer Vision
• Numerical Partial Differential Equations
• +۳ مورد دیگر

Fred Park serves as Assistant Professor of Mathematics in the Department of Mathematics & Computer Science at Whittier College. His research bridges mathematical theory with practical applications in image processing and computer vision, maintaining active collaborations across computational disciplines. Education: Ph.D. in Mathematics, University of California, Los Angeles M.A. in Mathematics, University of California, Los Angeles Professor Park's research focuses on mathematical image processing with emphasis on total variation methods, blind deconvolution, and shape modeling. His work spans computer vision applications including 3D surface reconstruction, texture extraction, and medical image analysis. Current projects explore non-convex optimization for deep learning compression and scientific computing implementations for real-world problem solving. His teaching philosophy integrates real-world mathematical problems to inspire undergraduate learning. Recent publications demonstrate strong trends in image segmentation frameworks combining anisotropic-isotropic total variation, network slimming for CNN efficiency, and medical imaging applications such as COVID-19 lung scan clustering. His work consistently bridges theoretical mathematics with computational implementation. Professor Park actively seeks to incorporate research into undergraduate education, developing methodologies to motivate learning through practical applications. His teaching experience spans undergraduate lower and upper division courses at UCLA, UC Irvine, and University of Michigan during postdoctoral positions. Research facilities include computational resources at Whittier College's Science & Learning Center (SLC 218), supporting work in image processing, shape modeling, and 3D reconstruction from point cloud data.

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

Francesco Maggi is a Full Professor in the Department of Mathematics at the University of Texas at Austin. His research focuses on geometric measure theory, calculus of variations, and partial differential equations, with an emphasis on isoperimetric problems, capillarity theory, and mean curvature flow. His work explores stability properties of geometric configurations, regularity of minimal surfaces, and applications to physical phenomena like soap films and crystal structures. Key research areas include: geometric variational problems, quantitative isoperimetric inequalities, nonlocal surface tensions, and singular limits in free boundary problems. His contributions have advanced understanding of minimal surfaces, rigidity in geometric inequalities, and energy-driven pattern formation. Recent work addresses topics such as almost-honeycomb structures in low-energy clusters, mesoscale flatness criteria for hypersurfaces, and the approximation of Plateau’s laws via phase-field models. Collaborations span analysis, geometry, and mathematical physics, with applications to materials science and continuum mechanics.

LAMBERT Zoéمشاهده پروفایل

دانشیار

• Signal and image processing
• Medical imaging
• Deep learning
• +۷ مورد دیگر

Zoé Lambert is an Associate Professor at the University of Rouen Normandy and a member of the LITIS laboratory. She completed her PhD in Applied Mathematics (2019–2022) at INSA Rouen Normandie, funded by the Normandy Region, with a thesis on hybrid variational and deep learning approaches for medical image segmentation. Prior to her PhD, she worked as a Research Engineer at INSA Rouen (2018–2019) on weakly supervised learning for medical imaging and as a Data Scientist Intern at AID in Paris (2018). Her current research focuses on integrating geometric and topological constraints into deep learning models for medical imaging and coastal cliff crack detection. Research Interests Zoé develops advanced image segmentation methods combining variational models and deep learning, with emphasis on: Topological prescriptions in CNNs Geometric constraints for anatomical accuracy Registration techniques via nonlinear elasticity Public dataset creation for crack segmentation Directional total variation decomposition models AI applications in energy transition (Prioreno tool) Scientific Contributions She co-organized the SegTHOR challenge at IEEE ISBI’19, which automated segmentation of thoracic organs-at-risk for radiotherapy. Her work on weighted total variation regularization and Mumford-Shah terms has improved segmentation reliability in scenarios with limited data. She also contributed to the DEPHY3GEO project at CEREMA (2023–2024), focusing on cliff crack detection using thermal infrared imagery. Honors and Collaborations Zoé won first place in the Normandy Datathon (2023) for her energy vulnerability decision support tool. She served on INSA Rouen’s Scientific Council (2020–2022) and laboratory council (LMI). Her collaborations span LITIS, LMI, CEREMA, INRIA, and international institutions like INSA Lyon and the University of Bordeaux.

• Machine Learning
• Probabilistic Models
• Bayesian Inference
• +۵ مورد دیگر

Michael Hughes is an Assistant Professor of Computer Science at Tufts University's School of Engineering. His research focuses on statistical machine learning and its applications in healthcare, including probabilistic models, Bayesian inference, and semi-supervised learning. He leads projects such as automated cardiovascular disease diagnosis via echocardiograms and mortality risk prediction from electronic health records. Education: PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research Interests: Bayesian hierarchical models, variational inference, clinical informatics, and interpretable AI. His work bridges theoretical advancements with high-impact applications in healthcare. Awards & Activities: Recipient of multiple reviewer awards at top conferences (NeurIPS, AISTATS) Organizer of workshops on Bayesian methods and health informatics Principal investigator on grants from NSF, NIH, and the US Army Teaching: Courses include Intro to Machine Learning, Bayesian Deep Learning, and Statistical Pattern Recognition. Labs & Teams: Leads the Tufts Machine Learning research group and collaborates with clinicians at Tufts Medical Center on healthcare AI projects.

Bin Liمشاهده پروفایل

پژوهشگر

• Machine Learning
• Biomedical Engineering
• Bioimage Informatics
• +۵ مورد دیگر

Dr. Bin Li is a postdoctoral researcher in the Institute of Biomedical Engineering at the University of Oxford . His work bridges machine learning , bioimage informatics , and computational pathology , with a focus on advancing AI applications in healthcare. Education: PhD in Biomedical Engineering (2020) from the University of Wisconsin-Madison under Professor Kevin Eliceiri. Dr. Li's research centers on developing deep learning techniques for histopathology , emphasizing weakly supervised and semi-supervised learning to analyze unlabeled/coarsely-labelled data. He also explores multimodal learning for image-based drug screening, integrating data from microscopy, genomics, and proteomics. His publication history reveals expertise in stochastic PDEs , singular SPDEs , and nonlinear dynamics , indicating cross-disciplinary foundations in mathematics and physics. Dr. Li contributes to the Professor Jens Rittscher's group , focusing on innovative computational methods for biomedical image analysis.

Sarah Fisherمشاهده پروفایل

مدرس

• Philosophy of Language
• Digital Speech Ethics
• Generative AI
• +۴ مورد دیگر

Dr. Sarah Fisher is a Lecturer in Philosophy at Cardiff University, specializing in socially and empirically engaged philosophy of language. She holds a Research Fellow position at University College London (UCL), contributing to interdisciplinary projects on content moderation ethics and free speech online. Fisher's academic journey includes a PhD from the University of Reading, an MSc from the University of Edinburgh, and a BA from the University of Oxford. Her research spans philosophy of language, digital speech ethics, and the implications of AI on communication. Her research focuses on linguistic framing, contextual effects on meaning, and the challenges posed by generative AI in public discourse. She has published widely in journals such as Philosophy Compass , Synthese , and Review of Philosophy and Psychology . Fisher actively engages in public debates on content moderation, AI governance, and the ethics of online communication. Recent talks include discussions on synthetic content, AI accountability, and the role of philosophy in addressing digital challenges. She is a regular speaker at conferences and collaborates with institutions like Yale Law School and the University of Oxford. Fisher’s work bridges theoretical philosophy with applied issues in technology, policy, and social media governance.