Rate My Professor Mikhail Belkin

Belkin's research focuses on the mathematical, statistical, and computational foundations of deep learning, including spectral, manifold, and kernel methods, optimization, semi-supervised learning, clustering, and phenomena like overparameterization, interpolation, and double descent. He introduced foundational methods such as Laplacian Eigenmaps for dimensionality reduction and data representation (Neural Computation, 2003), Manifold Regularization: A geometric framework for learning from labeled and unlabeled examples (Journal of Machine Learning Research, 2006), and Consistency of spectral clustering (Conference on Learning Theory, 2008). Influential recent works include Reconciling modern machine-learning practice and the classical bias–variance trade-off (PNAS, 2019), Fit without fear: remarkable mathematical phenomena of deep learning through the prism of interpolation (Acta Numerica, 2021), Mechanism for feature learning in neural networks and backpropagation-free machine learning models (Science, 2024), and Linear recursive feature machines provably recover low-rank matrices (PNAS, 2025). His publications have amassed tens of thousands of citations, shaping modern understanding of machine learning theory. Belkin has received the NSF CAREER Award (2007), Lumley Research Award (2011), and was elected an ACM Fellow (2023) for contributions to modern machine learning theory and algorithms. He currently serves as Editor-in-Chief of SIAM Journal on Mathematics of Data Science (since 2024), previously as Action Editor for Journal of Machine Learning Research and Associate Editor for IEEE TPAMI, and Program Chair for COLT 2021.