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Mikhail Belkin

Professor
Photo of Mikhail Belkin

Biographical Info

Mikhail Belkin received his Ph.D. in 2003 from the Department of Mathematics at the University of Chicago. His research interests are in theory and  applications of machine learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps, Graph Regularization and Manifold Regularization algorithms, which brought ideas from classical differential geometry and spectral analysis to data science. His recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. This empirical evidence necessitated revisiting some of the basic concepts in statistics and optimization.  One of his key recent findings is the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation.

Mikhail Belkin is a recipient of a NSF Career Award and a number of best paper and other awards. He has served on the editorial boards of the Journal of Machine Learning Research, IEEE Pattern Analysis and Machine Intelligence and SIAM Journal on Mathematics of Data Science.

Website: misha.belkin-wang.org

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Rose Yu

Assistant Professor
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Babak Salimi

Assistant Professor
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Jelena Bradic

Professor 858.534.3992
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Lucila Ohno-Machado

Professor
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Shannon Ellis

Assistant Teaching Professor
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R Stuart Geiger

Assistant Professor
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Benjamin Smarr

Assistant Professor
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Michael Pazzani

Distinguished Researcher 858.246.2287

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Executive Office

Mikhail Belkin

Professor
Photo of Mikhail Belkin

Biographical Info

Mikhail Belkin received his Ph.D. in 2003 from the Department of Mathematics at the University of Chicago. His research interests are in theory and  applications of machine learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps, Graph Regularization and Manifold Regularization algorithms, which brought ideas from classical differential geometry and spectral analysis to data science. His recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. This empirical evidence necessitated revisiting some of the basic concepts in statistics and optimization.  One of his key recent findings is the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation.

Mikhail Belkin is a recipient of a NSF Career Award and a number of best paper and other awards. He has served on the editorial boards of the Journal of Machine Learning Research, IEEE Pattern Analysis and Machine Intelligence and SIAM Journal on Mathematics of Data Science.

Website: misha.belkin-wang.org

Related by Category

Photo of Rose Yu

Rose Yu

Assistant Professor
Photo of Babak Salimi

Babak Salimi

Assistant Professor
Photo of Jelena Bradic

Jelena Bradic

Professor 858.534.3992
Photo of Lucila Ohno-Machado

Lucila Ohno-Machado

Professor
Photo of Shannon Ellis

Shannon Ellis

Assistant Teaching Professor
Photo of R Stuart Geiger

R Stuart Geiger

Assistant Professor
Photo of Benjamin Smarr

Benjamin Smarr

Assistant Professor
Photo of Michael Pazzani

Michael Pazzani

Distinguished Researcher 858.246.2287

Communications

Mikhail Belkin

Professor
Photo of Mikhail Belkin

Biographical Info

Mikhail Belkin received his Ph.D. in 2003 from the Department of Mathematics at the University of Chicago. His research interests are in theory and  applications of machine learning and data analysis. Some of his well-known work includes widely used Laplacian Eigenmaps, Graph Regularization and Manifold Regularization algorithms, which brought ideas from classical differential geometry and spectral analysis to data science. His recent work has been concerned with understanding remarkable mathematical and statistical phenomena observed in deep learning. This empirical evidence necessitated revisiting some of the basic concepts in statistics and optimization.  One of his key recent findings is the “double descent” risk curve that extends the textbook U-shaped bias-variance trade-off curve beyond the point of interpolation.

Mikhail Belkin is a recipient of a NSF Career Award and a number of best paper and other awards. He has served on the editorial boards of the Journal of Machine Learning Research, IEEE Pattern Analysis and Machine Intelligence and SIAM Journal on Mathematics of Data Science.

Website: misha.belkin-wang.org

Related by Category

Photo of Rose Yu

Rose Yu

Assistant Professor
Photo of Babak Salimi

Babak Salimi

Assistant Professor
Photo of Jelena Bradic

Jelena Bradic

Professor 858.534.3992
Photo of Lucila Ohno-Machado

Lucila Ohno-Machado

Professor
Photo of Shannon Ellis

Shannon Ellis

Assistant Teaching Professor
Photo of R Stuart Geiger

R Stuart Geiger

Assistant Professor
Photo of Benjamin Smarr

Benjamin Smarr

Assistant Professor
Photo of Michael Pazzani

Michael Pazzani

Distinguished Researcher 858.246.2287