جست‌وجوی استاد و پژوهشگر

• Calculus of Variations
• Optimal Transport
• Density Functional Theory
• +۳ مورد دیگر

Guy Bouchitté is a Full Professor at Université du Sud Toulon-Var. His research focuses on Calculus of Variations, Optimal Transport, Density Functional Theory, and Homogenization. He has contributed to theoretical advancements in optimal design, shape optimization, and multi-marginal transport problems. His work bridges mathematical analysis with applications in quantum chemistry, structural mechanics, and material science. Key research areas include asymptotic analysis of variational problems, optimal transport with non-linear costs, and the interplay between geometry and functional inequalities. Recent studies address dissociation limits in Density Functional Theory and quantization effects in many-body systems. He has organized seminars on Geometric Measure Theory and Frontiers of the Calculus of Variations. His contributions span over 35 publications, including works on Monge-Kantorovich duality, compliance sensitivity, and spectral optimization for Schrödinger operators. Professional activities include participation in workshops such as 'Variational Challenges in Materials Science' and 'Calculus of Variations and Applications'.

Kuang-Yao Leeمشاهده پروفایل

دانشیار

• Statistical machine learning
• Statistical genomics
• Graphical modeling and causality learning
• +۲ مورد دیگر

Kuang-Yao Lee is an Associate Professor in the Department of Statistical Science at the Fox School of Business and Management, Temple University, holding a tenure-track appointment. Previously, he served as an associate research scientist at Yale University’s Center for Statistical Genomics and Proteomics, where he developed statistical methods for high-dimensional data and conducted collaborative research. Dr. Lee's educational background includes: PhD in Statistics from The Pennsylvania State University Master of Science in Mathematics from National Taiwan University Bachelor of Science in Mathematics from National Taiwan University His research centers on statistical genomics and machine learning with high dimensionality as a unifying theme. He develops advanced methodologies in graphical modeling, causality learning, high-dimension inference, and semi/non-parametric statistics. His work bridges theoretical statistics with practical applications in bioinformatics, image analysis, and functional data, often through collaborations with biologists and computer scientists on genome-wide association studies, gene regulatory networks, and pathway analysis. Dr. Lee's publication trajectory reveals a concentrated focus on functional data analysis and graphical models over the past five years. His contributions include foundational work on functional directed acyclic graphs, nonparametric functional graphical modeling, and sufficient dimension reduction in metric spaces. These innovations demonstrate consistent integration of causal inference frameworks with high-dimensional genomic applications, reflecting his dual expertise in statistical theory and biological data science.

• Calculus of Variations
• Materials Science
• Mathematical Analysis
• +۴ مورد دیگر

Roberto Alicandro is a Full Professor at the University of Naples Federico II. His research focuses on calculus of variations, mathematical analysis of materials science, and nonlinear elasticity. He has contributed to topics such as Γ-convergence, dislocation dynamics, and discrete-to-continuum limits in crystalline systems. Key research interests include variational analysis of singular perturbations, energy minimization in lattice systems, and multiscale modeling of defects in solids. He has co-authored multiple influential papers and books in journals like Math. Ann. , SIAM J. Math. Anal. , and Archive Rational Mech. Anal. . Alicandro has organized events such as the 2012 Meeting on Applied Mathematics and Calculus of Variations, and participated in workshops like 'Calculus of Variations and Partial Differential Equations' in 2017 and 'From Atomistic to Continuum Models in Materials Science' in 2014. His work bridges discrete and continuous frameworks in solid mechanics and materials science.

Janice Scealyمشاهده پروفایل

دانشیار

• Compositional Data Analysis
• Directional Statistics
• Robust Statistics
• +۵ مورد دیگر

Janice Scealy is an Associate Professor in the Research School of Finance, Actuarial Studies & Statistics at The Australian National University (ANU). She holds a PhD in Statistics from ANU (2011) and a Bachelor of Mathematics (Honours) from the University of Wollongong (2003). Her research focuses on compositional data analysis, directional statistics, robust statistics, and applications in geosciences like palaeomagnetism and seismology. She has held roles including ARC DECRA Fellow (2018-2022) and has supervised multiple research projects. Notable contributions include advancements in hypersphere-based statistical models and robust estimation techniques. Education: PhD (ANU 2011), BMath (Wollongong 2003) Her research interests include statistical methods for manifold-valued data, geostatistics, and model selection in linear mixed models. Recent work applies elliptical distributions to seismic moment tensor classification and integrates Fisher's palaeomagnetic theories with modern statistics. She leads projects on non-Euclidean data analysis and biologics immunogenicity assessment. Key collaborations involve geophysical applications and statistical methodologies for spatial data. Awards include ANU research grants (2018–2025) and contributions to international statistical reviews.

David Hofmeyrمشاهده پروفایل

مدرس ارشد

• Statistics
• Machine Learning
• Cluster Analysis
• +۵ مورد دیگر

Dr David Hofmeyr serves as a Senior Lecturer in Statistics at Lancaster University's School of Mathematical Sciences, specializing in advanced statistical methodology and machine learning applications for complex data analysis. His research focuses on: Estimation of complexity for non-standard estimators (clustering models, linear projections) Non-parametric regression and classification methodology Cluster analysis and unsupervised dimension reduction theory Spatial, temporal, and spatio-temporal applications of flexible regression models Recent publications demonstrate his interdisciplinary approach bridging machine learning with environmental science (soil mapping via survival analysis) and theoretical classification improvements. His work consistently emphasizes practical implementation of statistical theory in real-world data challenges. Dr Hofmeyr actively supervises PhD candidates through the STOR-i Centre for Doctoral Training and collaborates with the Statistical Artificial Intelligence research group. He maintains an open invitation for potential PhD students in his core research areas. No scientific awards were documented in the provided materials.

• Changepoint Detection
• High-dimensional Time Series
• Statistical Inference
• +۳ مورد دیگر

Haeran Cho is Professor of Statistical Science in the School of Mathematics at the University of Bristol, holding a BSc and PhD in Statistics. Her research focuses on developing foundational methodologies for detecting structural changes in complex high-dimensional data streams, with applications spanning finance, environmental monitoring, and biomedical engineering. Her primary research interests include changepoint detection in non-sparse regression frameworks, factor model diagnostics for tensor time series, and robust nonparametric segmentation techniques. She pioneers approaches that handle heavy-tailed distributions, temporal dependence, and high-dimensional scaling—addressing critical limitations in classical change point theory through adaptive covariance scanning and multiscale inference frameworks. Professor Cho received the Research Prize in 2013 for her contributions to statistical theory. She currently leads the £1.2M EPSRC-funded project Statistical Foundations for Detecting Anomalous Structure in Stream Settings (DASS, 2024-2029), developing real-time anomaly detection systems for industrial applications. Previous projects include quantile factor modeling for high-dimensional time series (2019). Her software implementations ( CptNonPar , mosum , fnets ) have become standard tools in statistical computing, with over 500 citations. She actively collaborates through Horizon Europe initiatives and supervises postgraduate researchers in statistical methodology development.

David Fajmanمشاهده پروفایل

دانشیار

• General Relativity
• Cosmology
• Gravitational Waves
• +۳ مورد دیگر

David Fajman is an Associate Professor at the University of Vienna, where he leads the Gravitational Physics group and serves as Head of Gravitational Physics. His research centers on Einstein's field equations, gravitational waves, and cosmological structures, blending mathematical physics with geometric analysis to study spacetime dynamics and relativistic matter. Research interests include: Dynamics of spacetime geometry under gravitational forces Formation and stability of cosmological structures Relativistic fluid mechanics in expanding universes Mathematical analysis of black holes and singularities Asymptotic behavior of solutions to Einstein's equations His recent publications focus on spacetime stability, cosmological fluids, and black hole physics, with emphasis on analytical techniques in general relativity and cosmology. Key trends include mathematical investigations of spacetime asymptotics, singularity formation, and stability thresholds in relativistic systems. Research Projects: Geometric Transport equations and the nonvacuum Einstein flow (FWF, 2017–2021) Relativistic fluids in cosmology (FWF, 2021–2024) Matter dominated cosmology (FWF, 2025–present) He advises PhD student Liam Urban and collaborates with postdocs Adam Cieślik and Maximilian Ofner. Former group members include Gernot Heißel, Zoe Wyatt, and Hamed Barzegar.