Scholar
Miguel Á. Carreira-Perpiñán
Google Scholar ID: SYdYhxgAAAAJ
Professor of Computer Science, University of California, Merced
Citations & Impact
Citations
3,956
H-index
30
i10-index
61
Publications
20
Co-authors
0
Publications
Resume (English only)
Academic Achievements
- Developed several algorithms including the Tree Alternating Optimization (TAO) algorithm, the Learning-Compression (LC) algorithm, and the Method of Auxiliary Coordinates (MAC). Served as area chair or senior area chair for multiple international conferences such as NeurIPS 2025, ICML 2025, ICLR 2025, and AAAI 2025. Action editor for the Journal of Machine Learning Research (JMLR) since 2017. Received funding from the National Science Foundation (NSF) for $425,000 (2020-2025).
Research Experience
- Professor at the Department of Computer Science and Engineering, School of Engineering, University of California, Merced; supervised multiple PhD students.
Education
- Information not provided
Background
- Research interests: machine learning, intersection of optimization and machine learning (e.g., learning algorithms for deep neural nets and other 'nested' systems), decision trees and tree-based models, nonlinear embeddings, and optimal compression of deep neural nets. Also interested in dimensionality reduction/manifold learning, clustering, denoising, and other unsupervised learning problems, and mean-shift algorithms. Inspired by problems in speech processing, computer vision, sensor networks, robotics, and other application areas.
Miscellany
- Personal interest: Creating Cubist art using the TAO algorithm
Co-authors