Lawrence K. Saul

Timeline

1. By 2000

   Physics degrees and AT&T Labs

   He earns a bachelor's degree in physics at Harvard and a doctorate in physics at MIT, with a dissertation on exact computations in the statistical mechanics of disordered systems. By 2000, he is a member of the technical staff at AT&T Labs and sits on the editorial board of the new Journal of Machine Learning Research.

2. 2000–2003

   Locally linear embedding

   In 2000, he and Sam T. Roweis publish "Nonlinear Dimensionality Reduction by Locally Linear Embedding" in Science; the method maps high-dimensional inputs into a single global coordinate system of lower dimensionality, and its optimizations involve no local minima. In 2003, the Journal of Machine Learning Research publishes their paper "Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds," which presents locally linear embedding as an unsupervised learning algorithm that computes low dimensional, neighborhood preserving embeddings of high dimensional data.

3. 2008–2013

   Editor-in-chief of JMLR

   He starts working alongside the journal's founding editor Leslie Kaelbling in mid 2008 and takes over the Journal of Machine Learning Research completely in 2009. Kevin Murphy and Bernhard Schölkopf take over from him as co-editors-in-chief in March 2013.

4. 2009

   Kernels for deep learning

   With Youngmin Cho, he introduces at NIPS 2009 a family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. In the Journal of Machine Learning Research in 2009, he and Kilian Q. Weinberger publish "Distance Metric Learning for Large Margin Nearest Neighbor Classification," which learns a Mahalanobis distance metric for k-nearest neighbor classification from labeled examples.

5. July 2022

   Joining the Flatiron Institute

   In July 2022, he joins the Center for Computational Mathematics at the Flatiron Institute as a Senior Research Scientist. He arrives from UC San Diego, where he is professor and vice-chair in the Department of Computer Science and Engineering; his career before Flatiron also includes a tenured faculty post at UPenn and a post as a member of the technical staff at AT&T Labs.

6. March 2025

   Winning the Flywire challenge

   In March 2025, he and Daniel Lee win the Flywire VNC Matching Challenge, hosted by the Princeton Neuroscience Institute. Their method aligns the connectomes of a male and a female fruit fly by matching graphs of the neurons in the ventral nerve cords.

7. 2025

   An AISTATS best paper

   In 2025, he and Charles Margossian receive the best paper award at the AISTATS conference in Phuket for their work on variational inference in location-scale families.

8. As of October 2026

   Machine learning at Flatiron

   He is a Senior Research Scientist and Group Leader in the Center for Computational Mathematics at the Flatiron Institute. His work ranges across high dimensional data analysis, latent variable modeling, variational inference, and representation learning.