جست‌وجوی استاد و پژوهشگر

• Fluid Dynamics
• Partial Differential Equations
• Geometric Measure Theory
• +۳ مورد دیگر

Gianluca Crippa is an Associate Professor in the Department of Mathematics and Computer Science at the University of Basel, affiliated with the Analysis research group. His work focuses on differential equations arising from fluid dynamics and geometric measure theory. He contributes to understanding irregular fluid behaviors, such as chaotic flows and advection processes. His research also explores nonlocal conservation laws, mixing phenomena, and transport equations. Crippa organizes the BZ Seminar in Analysis, a collaborative event with the University of Zurich and ETH Zurich. He has published extensively on topics like Euler flows, Navier-Stokes equations, and Vlasov-Poisson systems, with over 50 peer-reviewed articles. His current projects include studies on localized Yudovich spaces and entropy solutions in nonlocal models. Education: Ph.D. in Mathematics (unspecified institution). Research Themes: Fluid dynamics, PDEs, geometric measure theory, advection, and nonlocal phenomena. He collaborates with institutions globally, including SISSA Trieste and ETH Zurich. His work bridges theoretical mathematics and applications in physics and engineering, emphasizing rigorous mathematical analysis of complex systems.

Kris De Brabanterمشاهده پروفایل

دانشیار مدعو

• Statistics
• Machine Learning
• Nonparametric Regression
• +۳ مورد دیگر

Kris De Brabanter is an Affiliate Associate Professor at Iowa State University. His research focuses on statistical methodologies, machine learning, and their applications in forensic science, particularly bloodstain pattern analysis, as well as industrial engineering and kernel-based regression techniques. He holds a PhD from Katholieke Universiteit Leuven (2011) and advanced degrees from Erasmus Hogeschool Brussel and other Belgian institutions. Education PhD in Statistics, Katholieke Universiteit Leuven, 2011 Master of Science (Erasmus Hogeschool Brussel and Katholieke Universiteit Leuven), 2005–2007 Bachelor of Science, Erasmus Hogeschool Brussel, 2003 Research Interests De Brabanter’s work emphasizes kernel methods, nonparametric regression, and statistical modeling for real-world problems. He has pioneered automated bloodstain pattern analysis techniques using Euclidean geometry and likelihood ratios, contributing to advancements in forensic science. His research also extends to optimization under uncertainty, machine learning applications for large datasets, and robust statistical estimation. Publications Trends Recent articles highlight his expertise in bloodstain pattern classification, kernel-based regression with correlated errors, and surrogate modeling for remanufacturing systems. His work bridges theoretical statistics with practical applications in forensics, epidemiology, and engineering. Awards and Grants No specific awards or grants are listed in the provided information. Labs/Teams No lab affiliations or research teams are explicitly mentioned.

Damek Shea Davisمشاهده پروفایل

استادیار

• Optimization
• Stochastic Algorithms
• Nonconvex Optimization
• +۴ مورد دیگر

Damek Shea Davis is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, College of Engineering. He received his Ph.D. in Mathematics from UCLA in 2015 and joined Cornell in 2016. Davis focuses on mathematical foundations of data science, with particular expertise in optimization, statistical learning, and numerical algorithms. His research develops theoretical frameworks and efficient algorithms for nonconvex, nonsmooth, and stochastic optimization problems. Key contributions include convergence guarantees for stochastic subgradient methods on nonconvex functions, analysis of optimization landscapes for phase retrieval, and development of methods for high-dimensional statistical estimation. His work bridges mathematical optimization with machine learning and signal processing applications. Davis has received numerous honors including the Sloan Research Fellowship (2020) and INFORMS Optimization Society Young Researchers Prize (2019). His publications advance understanding of optimization algorithm behavior in statistically challenging regimes, with recent work exploring geometric convergence properties and optimality conditions.

Daniel Schmidtمشاهده پروفایل

مدرس ارشد

• Bayesian Inference
• Information Theory
• Statistical Genomics
• +۷ مورد دیگر

Daniel Schmidt is a Senior Lecturer in Data Science at Monash University's Faculty of Information Technology, specializing in Bayesian inference, information theory, and statistical genomics. He holds a PhD in Computer Science (2008) and a Bachelor of Digital Systems (Honours) from Monash University (2003). His research focuses on high-dimensional Bayesian regression, Minimum Message Length (MML) principles, and applying machine learning to medical risk prediction, particularly in breast cancer via mammography analysis. He leads projects on scalable time series forecasting, quantum computing applications, and epileptic seizure prediction. Teaching commitments include developing and lecturing in units like FIT2086 (Modelling for Data Analysis) and FIT3154 (Advanced Data Analysis). He collaborates with institutions like the University of Melbourne (Adjunct Senior Research Fellow) and has contributed to open-source tools like the BayesReg package and GitHub repositories for statistical methods. His work contributes to UN Sustainable Development Goals related to health and innovation. Key research projects include efficient time series classification (MONSTER repository), seizure forecasting using EEG data, and quantum computing applications. He has authored over 119 publications, with recent work addressing adversarial attacks on time series models and Bayesian shrinkage priors for regression. Notable collaborations involve international teams in cancer genomics, statistical epidemiology, and materials science. His GitHub contributions include tools for random correlation matrix generation and fast AR model estimation.

Brett Wickمشاهده پروفایل

استاد

• Complex Analysis
• Harmonic Analysis
• Operator Theory
• +۳ مورد دیگر

Brett D. Wick is a Professor of Mathematics at Washington University in St. Louis, specializing in analysis with a focus on complex analysis, harmonic analysis, and operator theory. He holds a PhD from Brown University and has been recognized with prestigious awards including the NSF CAREER Award and the Alexander von Humboldt Fellowship. His research explores interactions between these fields, particularly extending results from complex and harmonic analysis to higher dimensions and addressing the Corona Problem in multiple variables. Education: PhD in Mathematics from Brown University. Research interests include several complex variables, operator theory, and multi-parameter harmonic analysis. He has contributed to studies on Riesz transforms, paraproducts, and weighted inequalities, with applications to quasiregular maps and functional spaces. Wick organizes conferences and collaborates internationally, appearing at events like the International Workshop on Operator Theory (IWOTA) and the Banff International Research Station. Scientific Awards: Fellow of the American Mathematical Society, Alexander von Humboldt Fellow, NSF CAREER Award, Jerrold E. Marsden Postdoctoral Fellow. Grants and Funding: His work is supported by the National Science Foundation. Collaborations involve researchers globally, focusing on topics like Schatten classes, commutators, and function spaces. Labs/Teams: Active in the Washington University Analysis group and collaborates with international teams on operator theory and harmonic analysis projects.

Limin Pengمشاهده پروفایل

استاد

• Biostatistics
• Survival Analysis
• Causal Inference
• +۷ مورد دیگر

Limin Peng is a Professor in the Department of Biostatistics and Bioinformatics at Emory University. He joined Emory in 2005 as Rollins Assistant Professor. His research focuses on survival analysis, dynamic regression, and nonparametric/semiparametric inference, with recent work integrating causal inference, latent class analysis, and high-dimensional statistical learning. Collaborative projects span diabetes, cystic fibrosis, neurology, and environmental health, supported by NSF and NIH grants. Education Ph.D., University of Wisconsin - Madison M.S., University of Science and Technology of China (USTC) B.S., University of Science and Technology of China (USTC) Research Interests Dr. Peng’s methodological work bridges statistical theory and applied biomedical research. Key areas include causal inference frameworks for observational studies, survival analysis techniques for competing risks, and machine learning approaches for high-dimensional omics data. His clinical collaborations address diabetes management in hospitalized patients, neurodegenerative disease biomarkers, and environmental health risk assessment. Recent Articles Trends Recent publications emphasize causal inference methods (e.g., Mendelian randomization), integration of neuroimaging and omics data, and clinical trial designs for glucose monitoring in vulnerable populations. Methodological innovations include tensor response quantile regression and latent class models for clustered data. Grants & Funding Active funding includes NSF grants for statistical methodology development and NIH awards for collaborative diabetes research. Notable projects include the RABBIT 2 surgery trial and continuous glucose monitoring studies in long-term care facilities. Labs & Collaborations Collaborates with Emory’s Diabetes Center, Rollins School of Public Health, and Winship Cancer Institute. Leads a biostatistics team supporting clinical trials and epidemiological studies across multiple disciplines.

• Statistics
• Probability Theory
• Information Theory
• +۳ مورد دیگر

Jan Mielniczuk is a Professor at the Department of Mathematical Statistics and Financial Mathematics, within the Faculty of Mathematics and Information Sciences at Warsaw University of Technology. His research focuses on kernel statistics, information theory, probability theory, and statistical methodologies such as model selection, time series analysis, and smoothing techniques. He has authored 82 publications and supervised 20 promoted theses. His work is recognized by an h-index of 16 in both Scopus and Web of Science. Additionally, he has led 1 research project and received 5 notable achievements in his field. Key research areas include bandwidth consistency, kernel estimators, and applications in financial mathematics. His contributions span theoretical advancements and practical applications in statistics, with a focus on linear models, density estimation, and random variables analysis. Jan Mielniczuk maintains an ORCID profile (0000-0003-2621-2303) and holds a prominent academic standing in the mathematical sciences community.

Paul Rilstoneمشاهده پروفایل

استاد

• Econometrics
• Finite Sample Econometrics
• Duration Analysis
• +۳ مورد دیگر

Paul Rilstone is a Professor in the Department of Economics at York University's Faculty of Liberal Arts & Professional Studies. He has been teaching at York since 1995, with prior positions at Western University (BA 1987) and Laval University in Quebec. His research focuses on finite sample econometrics, duration analysis, nonparametric estimation techniques, and Russian literature. He has held visiting roles at Wisconsin, Virginia, Michigan, Toronto, Rochester, and Statistics Canada. His academic career includes over three decades of contributions to econometric theory and applications. Key research areas involve improving estimation methods for nonlinear models, analyzing duration processes in labor markets and public health contexts, and advancing nonparametric statistical techniques. Recent work addresses pandemic modeling and policy impacts through econometric frameworks. Rilstone's publications emphasize higher-order asymptotic expansions, semiparametric efficiency, and innovative estimation strategies. His work bridges theoretical econometrics with applied policy analysis, particularly in labor markets and healthcare systems. No specific awards are listed in available records, though his extensive publication history indicates sustained scholarly impact.

• Economic Theory
• Asset Pricing
• Monetary Policy
• +۷ مورد دیگر

Jianjun Miao is a Professor recognized for his significant contributions to economic theory, evidenced by his 2025 election as Fellow of the Society for Advancement of Economic Theory. His academic work bridges theoretical economics with practical policy applications across multiple domains. His research spans economic theory, asset pricing, monetary and fiscal policy, rational inattention, and asset bubbles. Miao's work demonstrates particular expertise in modeling decision-making under uncertainty, financial market dynamics, and macroeconomic policy interactions. His research integrates advanced mathematical techniques with real-world economic phenomena, focusing on how information frictions and ambiguity affect market outcomes and policy effectiveness. Analysis of his 2022-2025 publications reveals three dominant research trajectories: (1) asset bubbles and their macroeconomic implications, (2) rational inattention frameworks in discrete choice and asset pricing, and (3) fiscal-monetary policy coordination in constrained environments. His work consistently addresses methodological challenges in modeling uncertainty while providing policy-relevant insights for financial stability and economic growth. Scientific Awards: Fellow of the Society for Advancement of Economic Theory (2025) Professor Miao maintains an active research program with substantial scholarly output. His work appears in leading economics journals and influences both academic discourse and policy discussions regarding financial stability, economic growth, and optimal policy design under uncertainty. He contributes significantly to academic knowledge through theoretical model development and empirical applications.

John Robinsonمشاهده پروفایل

استاد

• Communication Technologies
• Audio Signal Processing
• Image Processing
• +۳ مورد دیگر

John Robinson is a Professor of Electronic Engineering at the University of York, affiliated with the School of Physics, Engineering and Technology. He holds a BSc in Mathematics from Durham University, MSc and PhD in Engineering from the University of Essex, and an MPhil in Humanities from Memorial University of Newfoundland. His research focuses on image and video processing, statistical pattern recognition, and deep learning applications. Notably, he developed masked conditional neural networks for sound and audio classification. From 2012 to 2020, he served as Pro-Vice Chancellor for Teaching, Learning, and Students, and previously led the Electronics department at York. He is a Principal Fellow of the Higher Education Academy. Research Interests: Human face analysis Projector-camera systems Picture coding Audio and acoustics Deep learning for signal analysis Scientific Awards: Principal Fellow of the Higher Education Academy (HEA) Advising & Grants: Collaborates with industrial partners on face analysis technology. Previously led projects in video coding and multimedia communications. Served on the executive committee of Professors and Heads of Department in Electrical and Electronic Engineering (PHEE). Labs/Teams: Active in the Communication Technologies Group, focusing on audio and acoustics research, and has contributed to initiatives like the LivePaper System for augmented reality.

Manas Bhatnagarمشاهده پروفایل

استادیار مهمان

• Partial Differential Equations
• Hyperbolic Balance Laws
• Critical Threshold Analysis
• +۳ مورد دیگر

Manas Bhatnagar is a Visiting Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. His research focuses on the analysis of partial differential equations, particularly hyperbolic balance laws and critical threshold phenomena. He earned his Ph.D. in Mathematics from Iowa State University in Summer 2022, under the supervision of Dr. Hailiang Liu. Currently, he works under the mentorship of Dr. Robin Young in his postdoctoral position. Education: Ph.D. in Mathematics, Iowa State University, 2022 Research Interests: Bhatnagar specializes in hyperbolic systems, critical thresholds for PDE stability, and nonlocal dynamics. His work examines conditions under which solutions to hyperbolic balance laws remain smooth or develop singularities, with applications to fluid dynamics and collective behavior models. Key areas include Euler-Poisson systems, alignment dynamics, and relaxation effects in nonlocal equations. Publications: His recent work explores critical thresholds in hyperbolic systems across dimensions, with contributions to both theoretical analysis and numerical methods. Key themes include global dynamics of alignment systems, sharp threshold characterizations, and front tracking schemes for weak* solutions. Awards & Honors: No specific awards mentioned, though his work has been published in reputable journals like Journal of Differential Equations and Discrete and Continuous Dynamical Systems . Advising & Grants: No advising roles or grants explicitly listed. His current focus is on postdoctoral research in critical threshold phenomena. Labs/Teams: Not specified in available information.

Chuan-Fa Tangمشاهده پروفایل

استادیار

• Statistics
• Survival Analysis
• Statistical Machine Learning
• +۹ مورد دیگر

Chuan-Fa Tang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, affiliated with the School of Natural Sciences and Mathematics. He joined UTD in 2019 and teaches graduate-level courses including Applied Linear Models, Linear Statistical Models, and Nonparametric Methods. Ph.D. in Statistics from University of South Carolina (2017) M.S. and B.S. in Mathematics from National Taiwan University (2010, 2007) His research focuses on order-restricted inference , shape-constrained inference , survival analysis , and statistical machine learning , with particular interest in: Nonparametric and semiparametric models Empirical processes and likelihood methods Post-selection inference Stochastic ordering and heavy-tailed distributions His work includes developing goodness-of-fit tests for Cox models and stochastic ordering frameworks, with software implementations in R . Key publications examine uniform stochastic ordering comparisons, positive quadrant dependence testing, and Taylor's laws for heavy-tailed data. Scientific awards include: NSF DMS 2311292 grant (2023-2026) for monotonic hazard trend research He has contributed computational tools like TestUSO and LLGOF_UniCoxPH packages for statistical analysis.

Eduardo García-Portuguésمشاهده پروفایل

دانشیار

• Directional Statistics
• Nonparametric Statistics
• Functional Data Analysis
• +۶ مورد دیگر

Eduardo García-Portugués is an Associate Professor at the Department of Statistics, Carlos III University of Madrid. He holds a PhD in Statistics and Operations Research from the Universidade de Santiago de Compostela (2014). Previously, he was a Postdoctoral Fellow at the University of Copenhagen's Department of Mathematical Sciences (2015–2016) and an Assistant Professor at UC3M (2016–2021). He serves as an Associate Editor for Journal of Computational and Graphical Statistics , Annals of the Institute of Statistical Mathematics , and other journals. His research focuses on statistical methodologies for complex and non-Euclidean data, emphasizing directional statistics, kernel smoothing, and functional data analysis. He develops nonparametric and computational tools for analyzing hyperspherical, toroidal, and circular data, with applications in fields like finance and bioinformatics. His work includes advancements in goodness-of-fit tests, diffusions on manifolds, and optimal stopping problems. He leads the Statistics Reading Club and has secured grants for projects such as STENED (Stein-based goodness-of-fit tests for non-Euclidean data). He advises multiple PhD students, including Diego Serrano, Vinícius Litvinoff, and Paul Axmann. His contributions span theoretical developments and practical implementations, including R packages like rotasym and sphunif for directional data analysis.

Spyridon Pougkakiotisمشاهده پروفایل

مدرس

• Continuous Optimization
• Stochastic Optimization
• Risk-Aware Decision-Making
• +۳ مورد دیگر

Spyridon Pougkakiotis is a Lecturer in Optimization at King’s College London’s Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. He previously held positions as Lecturer in Mathematics at the University of Dundee (2023–2024) and as a postdoctoral research associate at Yale University (2022–2023). His research focuses on continuous optimization, stochastic optimization, and risk-aware decision-making, with applications in operational research, engineering, and data science. He has developed efficient algorithms for large-scale optimization problems and contributed to preconditioning techniques in interior point methods. Education: BSc in Informatics and Telecommunications, National and Kapodistrian University of Athens (2012–2016) MSc in Operational Research with Computational Optimisation, University of Edinburgh (2016–2017) PhD in Optimization, University of Edinburgh (2017–2022) His research interests emphasize nonlinear optimization (including non-smooth, stochastic, and black-box problems) and algorithm development. He has been recognized with the 2022 OR Society Doctoral Award and was a finalist for the 2024 Euro Doctoral Dissertation Award. Teaching includes undergraduate courses on numerical methods with Python and an MSc module on machine learning. He is involved in King’s Financial Mathematics group and organized events like the “Afternoon of Stochastic Optimisation” workshop in 2025. Awards: 2022 OR Society Doctoral Award (Most Distinguished Doctoral Research in Operational Research) 2024 Euro Doctoral Dissertation Award Finalist Current research projects include a 50/50 Haleon Industry Studentship (2025–2028), focusing on optimization applications in industry.

Chongchun Zengمشاهده پروفایل

استاد

• Applied Dynamical Systems
• Nonlinear Partial Differential Equations
• Fluid Dynamics
• +۷ مورد دیگر

Chongchun Zeng is a Professor in the School of Mathematics at Georgia Institute of Technology since 2009, previously holding roles at the University of Virginia and New York University's Courant Institute. His research focuses on applied dynamical systems and nonlinear PDEs, particularly fluid dynamics, nonlinear waves, and invariant manifold theory. Education PhD in Mathematics (1997), Brigham Young University Bachelor of Mathematics (1993), Nankai University, China Research Interests Explores regularity and dynamics of nonlinear PDEs, invariant manifolds in semiflows, and applications to fluid equations (e.g., water waves) and Schrödinger-type systems. Recent work addresses stability of traveling waves, spectral analysis of linearized operators, and inviscid damping phenomena. Awards & Grants Sloan Fellowship (2004) AMS Fellow (2019) NSF grants (2003–present, including DMS-2350115 for fluid dynamics research) Grants & Collaborations Analysis of Fluids and Nonlinear Waves (NSF DMS-2350115, 2024–2027) Studies on gravity water waves and nonlinear Schrödinger equations Lab/Team Affiliation Active in Georgia Tech's School of Mathematics research groups focused on PDEs and dynamical systems.