Daniel D. Lee
Daniel D. Lee studies learning and autonomous systems, including shared research on non-negative matrix factorization. Lee earned a physics degree at Harvard and a doctorate in condensed matter physics at MIT. He also conducted earlier research at Bell Labs in theoretical physics and biological computation. Lee’s Cornell group studies general computational principles in biological systems and applying them to autonomous systems. His learning research connects biological computation with engineered systems.
- Now at
- Cornell University
- Works
- 184
- Citations
- 26,132
- h-index
- 37
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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As of October 2026
Physics and biological computation
Lee earned a physics degree at Harvard and a doctorate in condensed matter physics at MIT. He also conducted earlier research at Bell Labs in theoretical physics and biological computation.
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1999
Learning parts from data
Lee and H. Sebastian Seung published Learning the Parts of Objects by Non-negative Matrix Factorization in Nature. Their paper described learning parts of faces and semantic features of text. The method uses non-negative components whose combinations are additive. Both researchers share authorship of this work.
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2000 conference
Analyzing factorization algorithms
Lee and Seung followed with Algorithms for Non-negative Matrix Factorization in the NIPS 2000 proceedings. They analyzed two multiplicative update algorithms, one for least squares error and one for generalized Kullback–Leibler divergence. The abstract describes convergence arguments using an auxiliary function.
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As of October 2026
Biological principles and autonomous systems
Lee’s Cornell group studies general computational principles in biological systems and applying them to autonomous systems. His learning research connects biological computation with engineered systems.
Sources and credits
Photo: tech.cornell.edu; Daniel D. Lee profile photograph; creator not stated. License: Not stated by source. Source: tech.cornell.edu. Cropped to a square and resized. Ask us to remove or replace this photo.