جست‌وجوی استاد و پژوهشگر

• Mathematics of Data
• Numerical Linear Algebra
• Randomized Algorithms
• +۵ مورد دیگر

Liza Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University's School of Engineering and Applied Science. She is also an associated faculty member of the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Her research focuses on randomized numerical linear algebra and the mathematics of data science, with connections to high-dimensional probability and stochastic optimization. She develops mathematically justified randomized algorithms for large-scale data, particularly interested in settings where data exhibits mathematical structure such as spectral decay, multi-modality, or non-negativity. Her work addresses challenges in data compression, recovery, and optimization under structured noise conditions. Dr. Rebrova's recent publications show a strong trend toward developing robust algorithms for linear systems, tensor data compression, and nonnegative matrix factorization. Her work bridges theoretical foundations with practical applications in machine learning and data science, with emphasis on memory efficiency and handling corrupted or incomplete data. NSF DMS-2309685 (Single PI, 2024-2026): "Outliers are not what they seem: data-aware, flexible, and robust randomized iterative methods" NSF DMS-2108479 (Collaborative, 2022-2024): "Fast, Low-Memory Embeddings for Tensor Data with Applications" (co-PIs Mark Iwen and Deanna Needell) She currently advises three PhD students (Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko) and has previously advised Abraar Chaudhry (PhD 2024) and Nicolo Grometto (Masters thesis 2023). At Princeton, she teaches graduate courses including ORF526 (Graduate Probability), ORF387 (Networks), and ORF523 (Convex and Conic Optimization).

Nicolas Gillisمشاهده پروفایل

استاد

• Low-rank matrix approximations
• Nonnegative matrix factorization
• Continuous and discrete optimization
• +۶ مورد دیگر

Nicolas Gillis is a Professor in the Department of Mathematics and Operational Research at the Faculty of Engineering, University of Mons, Belgium. His research focuses on theoretical and applied aspects of low-rank matrix approximations, nonnegative matrix factorization (NMF), optimization, and computational complexity, with applications in machine learning, data mining, and hyperspectral imaging. Research Interests: Low-rank matrix approximations, NMF, numerical linear algebra, machine learning, and signal processing. Publications: Authored the first comprehensive book on NMF, available in print and digital formats, with accompanying MATLAB code on GitLab. Teaching: Created a doctoral course on NMF with 6 lectures and 4 exercise sessions for the SOCN doctoral school. Upcoming Events: Organizing the third workshop on Low-Rank Models and Applications (LRMA 25) in September 2025, and the SOCN study day in Mons.