N. Benjamin Erichson

N. Benjamin Erichson

Assistant Professor

University of Pittsburgh


I am an Assistant Professor for Data-driven Modeling and Artificial Intelligence in the Department of Mechanical Engineering and Materials Science at the University of Pittsburgh. I am also a PI and senior researcher at the International Computer Science Institute (ICSI) in Berkeley. Before joining U Pitt, I was a postdoctoral researcher in the Department of Statistics at UC Berkeley, where I worked with Michael Mahoney. I was also part of the RISELab in the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley. Before to that, I was a postdoc at the Department of Applied Mathematics at the University of Washington (UW) working with Nathan Kutz and Steven Brunton. I earned my PhD in Statistics at the University of St Andrews, in Dec. 2017. My MSc in Applied Statistics is also from the University of St Andrews.

I am broadly interested at the intersection of deep learning, dynamical systems, and robustness—how can we build robust and intelligent dynamical systems that are computational efficient and expressive? I am also interested in leveraging tools from randomized numerical linear algebra to build modern algorithms for data-intensive applications such as fluid flows and climate science.

Projects in my lab span the space between development of robust and dynamical systems inspired neural network architectures, efficient training strategies for extracting knowledge from limited data, and data-driven modeling of scientific data.

I have two open positions for highly motivated and driven PhD students, starting September 2022. Drop me an email, if my research interests sparks your interest and you would like to pursue research in this area.

Full list of publications: Google Scholar.

  • Current Students and Postdocs:

    • Francisco Utrera (PhD student, U Pitt / UC Berkeley, joint w. Michael W. Mahoney)
  • Former students:

    • Vanessa Lin (undergraduate student, now at Google)
    • Evan Kravitz (master student, now Software Engineer at Amazon)

Email: erichson @ pitt dot edu


  • Deep Learning
  • Transfer Learning
  • Dimension Reduction
  • Sequence Modeling
  • Dynamical Systems


  • PhD in Statistics, 2017

    University of St Andrews

  • MSc in Applied Statistics, 2013

    University of St Andrewsy


Two papers accepted in NeurIPS 2021

Noisy Recurrent Neural Networks (preprint), and Compressing Deep ODE-Nets using Basis Function Expansions (preprint) which is joint work with Google Research.

Two papers accepted in ICLR 2021

Lipschitz Recurrent Neural Networks (preprint) and Adversarially-Trained Deep Nets Transfer Better (preprint).

Two papers accepted in ICML 2020

Forecasting sequential data using consistent Koopman autoencoders (preprint) and Error Estimation for Sketched SVD via the Bootstrap (preprint).

Recent & Upcoming Talks

Noisy Recurrent Neural Networks
Stabilized Dynamic Autoencoders