جست‌وجوی استاد و پژوهشگر

Polona Oblakمشاهده پروفایل

استاد

• Matrix Theory
• Graph Theory
• Spectral Graph Theory
• +۷ مورد دیگر

Polona Oblak serves as a Full Professor at the Faculty of Computer and Information Science, University of Ljubljana, where she is an integral member of the Laboratory for Mathematical Methods in Computer and Information Science. Her teaching responsibilities span foundational courses including Linear Algebra, Mathematical Modelling, and multiple levels of Mathematics instruction, reflecting her dual expertise in theoretical mathematics and computational applications. Her research centers on advanced Matrix Theory and Graph Theory, with pioneering contributions to Spectral Graph Theory and Inverse Eigenvalue Problems. She investigates structural properties of commuting matrices, nilpotent matrix centralizers, and tropical semiring algebra, extending theoretical frameworks to practical applications in computer vision and statistical analysis. Recent interdisciplinary projects like "DeepBeauty" demonstrate her ability to bridge pure mathematics with industry-relevant solutions in fashion technology. Analysis of her 15 most recent publications (2021-2025) reveals a dominant focus on spectral graph phenomena, particularly the inverse eigenvalue problem across diverse graph structures including trees, block graphs, and unicyclic graphs. Her work on tropical matrix factorization (e.g., Faststmf algorithm) provides efficient computational tools for sparse data, while theoretical breakthroughs like the "liberation set" concept redefine boundaries in spectral graph theory. This research trajectory shows increasing integration of algebraic methods with machine learning applications. Professor Oblak has secured substantial research funding through the Slovenian Research Agency (ARRS) and international collaborations, including the ongoing "Computer Vision" program (2019-2024) and bilateral projects with Bosnia and Herzegovina on nilpotent orbits. Her leadership in computationally intensive statistical methods (2016-2019) and deep generative models for the beauty industry (2020-2023) demonstrates consistent ability to translate theoretical advances into funded research initiatives, though specific student supervision details remain unlisted in available sources. Within the Laboratory for Mathematical Methods in Computer and Information Science, she contributes to a synergistic research environment where algebraic techniques directly inform computational solutions. Her work on Laplacian-integral graphs and tropical factorization algorithms exemplifies the laboratory's mission to develop mathematical foundations for next-generation information systems, with recent outputs showing heightened emphasis on algorithmic efficiency for real-world data challenges.

Eugene Demidenkoمشاهده پروفایل

استاد

• Biostatistics
• Statistical Inference
• Data Science
• +۵ مورد دیگر

Eugene Demidenko, PhD, is a Professor with multiple appointments at Dartmouth College, holding positions in Biomedical Data Science, Community and Family Medicine, Mathematics, and Engineering at the Geisel School of Medicine. His academic career spans several decades with significant contributions to statistical methodology and applications. Dr. Demidenko earned his PhD from the Central Economics-Mathematics Institute of Academy of Sciences in 1975 and an MSD from Moscow Pedagogical University in 1971. His educational background laid the foundation for his interdisciplinary approach to statistics and data science. His research focuses on developing exact optimal statistical inference methods for small samples, challenging traditional approaches that rely on asymptotic approximations. Dr. Demidenko's work bridges theoretical statistics with practical applications in biomedical research, epidemiology, and engineering. He has pioneered the M-statistics framework, which combines maximum concentration (MC) and mode (MO) approaches under a single methodological umbrella. His research extends to statistical analysis of images, tumor regrowth modeling, ill-posed inverse problems, and optimal portfolio allocation. Dr. Demidenko's publications demonstrate a consistent focus on improving statistical methodology across diverse fields. His work shows particular strength in developing exact inference procedures that avoid the limitations of traditional methods when sample sizes are small. The progression from his earlier work on mixed models to his recent M-statistics framework reveals an evolving research trajectory focused on addressing fundamental limitations in statistical practice. Ziegel Book Award in Statistics 2022 for "M-statistics: Optimal Statistical Inference for a Small Sample" Ranked among Top 2% World scientists according to Stanford University database Dr. Demidenko teaches a range of courses including QBS 124 (Advanced Biomedical Data Science), QBS 180 (Data Visualization), QBS 177 (Methods of Statistical Learning for Big Data), and mathematics courses on probability and statistical inference. While specific grant information isn't detailed in the provided text, his research output suggests substantial funding support for his methodological developments and applications. His work has significant implications for biomedical research where small sample sizes are common. His laboratory and research team focus on developing and implementing novel statistical methodologies, with a GitHub presence showing active development of R code for statistical methods. This computational approach enables practical implementation of his theoretical advances for researchers across disciplines.

Quan Zhangمشاهده پروفایل

استادیار

• Machine Learning
• Statistics
• Interpretable Learning
• +۷ مورد دیگر

Quan Zhang is an Assistant Professor in the Department of Accounting and Information Systems at the Broad College of Business, Michigan State University. With a Ph.D. in Information, Risk and Operations Management from the University of Texas at Austin (2020), an M.S. in Biostatistics from the University of Minnesota (2015), and a B.S. in Biological Science and Economics from Peking University (2012), his academic journey bridges diverse disciplines. Education Ph.D., Information, Risk and Operations Management - University of Texas at Austin (2020) M.S., Biostatistics (Ph.D. study) - University of Minnesota at Twin Cities (2015) B.S., Biological Science and Economics - Peking University (2012) His research focuses on interpretable machine learning , particularly balancing model interpretability and capacity using Bayesian and variational inference methods. Applications span telemedicine , quantitative marketing , medical data analysis , and online finance , with recent work on watermarking for copyright protection and robust representation learning. Selected publications include his 2025 paper on Beyond Invisibility: Learning Robust Visible Watermarks , 2023 work on Weibull Racing Survival Analysis , and foundational 2018 contributions to Nonparametric Bayesian Lomax Delegate Racing . His methodological innovations in Bayesian multinomial regression and survival modeling have been published in Advances in Neural Information Processing Systems and the Journal of Machine Learning Research . He contributes to software development with Python and R implementations for Bayesian models, available in his GitHub repository. His teaching includes graduate-level ITM885 Machine Learning and Optimization , emphasizing practical applications in business analytics.

• Physics
• Chemistry
• Machine Learning
• +۵ مورد دیگر

Giuseppe De Luca is a Research Fellow at the Department of Physics and Chemistry - Emilio Segrè, University of Palermo. His work spans interdisciplinary domains including theoretical physics, biophysics, machine learning, and social dynamics. Research Themes : Model averaging techniques, liquid-liquid phase separation, quantum complexity, holography, and social exclusion effects in education. Methodologies : Bayesian inference, multiscale analysis, and machine learning optimization. Recent publications focus on weighted-average least squares estimation, structural degradation in biomaterials, and virtual reality applications in psychological research. He contributes to the SHARE project with statistical expertise in survey weighting and imputation strategies.

Jeffrey Grahamمشاهده پروفایل

دانشیار

• Applied Mathematics
• Computational Mathematics
• Numerical Analysis
• +۶ مورد دیگر

Jeffrey Graham is an Associate Professor in the School of Math and Computer Sciences at Susquehanna University, where he has served as Head of Mathematical Sciences since 2007. He previously held roles as Acting Head of Physics (2009–2014) and Assistant Professor (2001–2007), with earlier experience at Western Carolina University (1992–2001). PhD in Mathematics (1992), Rensselaer Polytechnic Institute MS in Mathematics (1986), Texas Tech University BS in Computer Science and Mathematics (1984), Iowa State University His research bridges mathematics and computer science, focusing on inverse problems, differential equations, and computational methods. He has pioneered educational technologies, including MATLAB GUIs and Excel-based tools, and explored intersections with music theory via group theory applications. Jeffrey’s recent publications span quantum computing (2010), financial mathematics (2014), and leadership strategies (2011), reflecting his interdisciplinary approach. His work emphasizes pedagogical innovation and computational modeling across fields. Jeffrey has secured multiple grants, including NSF travel funding (1993), Microsoft instructional lab support (1997), and NSF REU grants (2008–2010). He has served on editorial boards, curriculum committees, and search committees, demonstrating institutional leadership.

Ami Wieselمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Covariance Estimation
• +۳ مورد دیگر

Ami Wiesel is a Professor at The Rachel and Selim Benin School of Computer Science and Engineering at The Hebrew University of Jerusalem. His research focuses on statistical signal processing, machine learning, and covariance estimation. Previously, he completed his postdoctoral studies at the University of Michigan with Professor Alfred Hero, earned his PhD in Electrical Engineering from Technion under Professors Yonina Eldar and Shlomo Shamai, and obtained his MSc and BSc in Electrical Engineering from Tel Aviv University. His research interests include robust covariance estimation , statistical learning , signal detection , and MIMO communications . Wiesel has made significant contributions to the field of structured covariance estimation, particularly in elliptical distributions and Tyler's estimator. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and hyperspectral imaging. Wiesel's publications show a clear trend toward integrating deep learning with traditional statistical signal processing methods. His recent work explores unbiased estimation using neural networks, fair principal component analysis, and deep learning applications for target detection with constant false alarm rate. His research spans theoretical foundations in covariance estimation to practical implementations in radar and communications systems. Among his notable scientific achievements are: Young Author Best Paper Award (2019) for 'Learning to Detect' Young Author Best Paper Award (2006) for 'Linear precoding via conic optimization for fixed MIMO receivers' Student Paper Award (2017) for 'Deep MIMO detection' Wiesel has advised numerous graduate students who have gone on to publish significant work in the field. His research has been supported by various grants focusing on statistical signal processing, machine learning applications, and radar systems. His monograph 'Structured Robust Covariance Estimation' (2015) has become a reference in the field. He maintains an active research group focusing on the intersection of statistical learning and signal processing, with applications in communications, radar, and medical imaging.