Scott Linderman

Timeline

1. 2008

   Cornell and Microsoft engineering

   He earns a BS in electrical and computer engineering at Cornell University in 2008, then spends three years as a software engineer at Microsoft before graduate school.

2. 2016–2019

   Harvard doctorate and Columbia postdoc

   In 2016, he completes a computer science PhD at Harvard University with Ryan Adams and Leslie Valiant. He receives the 2016 Leonard J. Savage Award for outstanding dissertation work in applied methodology from the International Society for Bayesian Analysis. From 2016 to 2019, he is a Simons Collaboration on the Global Brain postdoctoral fellow at Columbia University, working with Liam Paninski and David Blei.

3. 2017

   Recurrent switching dynamical systems

   In 2017, he coauthors “Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems” with Matthew Johnson, Andrew Miller, Ryan Adams, David Blei and Liam Paninski. The work models complex time series as sequences of simpler dynamic units and learns how transitions depend on observations or hidden continuous states.

4. 2019

   Joining Stanford Statistics

   He joins Stanford in 2019 as an assistant professor of statistics and a member of the Wu Tsai Neurosciences Institute. His work develops statistical models and computer algorithms to analyze recordings of neuronal activity in animal brains.

5. 2022

   S5 state space layers

   In 2022, he coauthors the preprint “Simplified State Space Layers for Sequence Modeling” with Jimmy T. H. Smith and Andrew Warrington. Their S5 layer uses a single state space model with multiple inputs and outputs, and supports parallel computation for tasks involving long sequences.

6. 2023

   A McKnight Scholar Award

   He is one of ten neuroscientists selected by the McKnight Endowment Fund for Neuroscience for a McKnight Scholar Award in 2023. His work applies machine learning to neural data and develops statistical methods to address problems in neurobiology.

7. July 2024

   Keypoint-MoSeq for animal behavior

   In July 2024, he coauthors “Keypoint-MoSeq: parsing behavior by linking point tracking to pose dynamics,” published in Nature Methods. The platform uses machine learning to identify units of animal behavior from tracked body points without human supervision, separating tracking noise from behavior through a generative model.

8. October 2025

   A neural data science center

   In October 2025, he and Laura Gwilliams explain plans for the new Stanford Center for Neural Data Science. The center aims to bring scholars together to tackle neuroscience problems and advance data science.

9. June 2026

   Co-founding Engram

   By June 2026, he is a co-founder of Engram alongside Jessy Lin and Jack Morris. The company trains knowledge directly into AI model weights and develops a memory layer for intelligent agents.

10. As of October 2026

    Associate professor at Stanford

    As of October 2026, he is an associate professor of statistics and a Wu Tsai Neurosciences Institute Faculty Scholar at Stanford University. He also holds courtesy associate professorships in computer science and electrical engineering and co-directs the Stanford Center for Neural Data Science.