Computational Autonomy

Our work

We develop approaches to automate reasoning and decision making in the presence of uncertainty. Our approaches focus on foundational algorithm development — we seek to create scalable, general, and adaptive algorithms that can intelligently work across many different application regimes. These goals require using and developing new tools in the areas of uncertainty quantification, statistical inference, and numerical analysis. We then apply these tools across areas ranging from data analysis and machine learning, to stochastic optimal control and optimization.

News

2020

u_m_logo.png
September 1: Nick's paper "Bayesian system ID: optimal management of parameter, model, and measurement uncertinty." was accepted to Nonlinear Dynamics. This paper provides a more robust formulation for learning dynamical systems from data than than a majority of least-squares based approaches currently used. You can read the paper here.
sandia_logo.jpg
September 1: Our collaborator John Jakeman made some really nice tutorials on our recently published MFNETS sampling framework for multifidelity UQ as part of the PyApprox package. You can find the tutorial here.
ACC2020_logo.png
July 1–3: Hang gives a presentation at the 2020 American Control Conference (virtually) on our paper "Uncertainty Quantification Using Generalized Polynomial Chaos for Online Simulations of Automotive Propulsion Systems."
ESI_Logo.png
May 4–5: Alex gives a presentation at the Workshop on Multilevel and multifidelity sampling methods in UQ for PDEs at the Erwin Schrödinger Institute (virtually): "Sampling algorithms for generalized model ensembles in multifidelity uncertainty quantification." Slides can be found here.
cnls_logo.png
January 12–16: Alex gives a talk at the Physics Informed Machine Learning Workshop hosted by the Center for Nonlinear Studies at LANL, in Santa Fe, NM, USA: "Bayesian approaches for data-driven learning of dynamical systems." Slides can be found here.

2019

maryland_logo.jpg
October 15: Alex gives a talk at the Department of Mathematics at the University of Marlyand, College Park, MD, USA: "Compression algorithms for enabling high-dimensional motion planning." Slides can be found here.
usc_logo.png
August 14-16: Alex gives a tutorial session at the 2019 UQ Summer School at USC in Los Angeles, CA, USA: "Tensor decompositions and their UQ applications". Slides can be found here.
usacm_logo.png
July 28 – August 1: Alex gives a talk at the 15th U.S. National Congress on Computational Mechanics in Austin, TX, USA: "Unifying multifidelity sampling approaches as approximate control variates"
eccomas_logo.png
June 24–26: Alex gives a talk at the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, in Crete, Greece: "New approaches to learn low-rank models of dynamical systems from streaming data."
limsi_logo.png
June 20: Alex gives an invited talk at LIMSI-CNRS, Orsay, France: "Compression algorithms for enabling high-dimensional motion planning"
uwashington_logo.png
June 6 – 7: Alex gives a talk at the Physics Informed Machine Learning Workshop at the University of Washington, Seattle, WA, 2020: "Scalable learning of dynamical systems." Slides can be found here.
sandia_logo.jpg
May 23: Alex gives an invited talk at Sandia National Laboratories, Albuquerque, NM: "Compression algorithms for enabling high-dimensional motion planning"

2018

13th WCCM
July 22–27: Alex gives a talk at the 13th World Congress in Computational Mechanics, New Your, NY, USA: "Multifidelity model management using latent variable networks"
ACC2018
June 27–29: Ezra Tal (MIT) gives a talk about our paper at ACC in Milwaukee, WI: "Continuous tensor train-based dynamic programming for high-dimensional zero-sum differential games"
eccomas_logo.png
June 11–15: Alex gives a talk at the 6th European Conference on Computational Mechanics, in Glasgow, Scotland
usc_logo.png
June 4–6: Alex gives a talk at the USC Workshop on Research Challenges and Opportunities at the interface of machine learning and uncertainty quantification, at USC, Los Angeles, CA, USA: "Regression in low-rank functional formats."
UQ18_logo.jpg
April 16–19: Alex gives a talk at the SIAM Conference on Uncertainty Quantification in Orange County, CA, USA: "Sparse regularization for low-rank regression"
doe_logo.png
January 30 – February 1: Alex participated in the DOE Workshop on Scientific Machine Learning in Rockville, MD.
cnls_logo.png
Jan 22: : Alex gives a talk at the Physics Constrained Machine Learning Workshop (hosted by the Center for Nonlienar Studies at LANL) in Santa Fe, NM, USA: "Multifidelity model management using latent variable Bayesian networks"
u_m_logo.png
January 19: Alex gives a talk in Ann Arbor at the Aerospace Graduate seminar: "Compression algorithms for enabling high-dimensional motion planning"
u_m_logo.png
January 18: Alex gives a talk in Ann Arbor at the Aerospace Graduate seminar: "Compression algorithms for enabling high-dimensional motion planning"
u_m_logo.png
January 1: Alex starts at University of Michigan

Get in Touch


Copyright (c) 2020, Alex Gorodetsky, goroda@umich.edu License: CC BY-SA 4.0