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

2021

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June 28-July 1: Brian presents our paper "Behavioral Cloning in Atari Games Using a Combined Variational Autoencoder and Predictor Model" at the IEEE Congress on Evolutionary Computation. The paper can be found here here.
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June 6-7: Nick presents a poster at the 3rd Annual Learning for Dynamics and Control Conference, hosted by ETH Zurich (remotely), describing a more robust objective for identifying linear systems from input-output data than traditional least-squares approaches. The paper can be found here.
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May 25-28: Alex presents at the Engineering Mechanics Institute Conference/Probabilistic Mechanics and Reliability 2021 Conference (EMI/PMC) on Bayesian system identification. Slides can be found here.
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May 7: Alex presents at the University of California, San Diego, Mechanical and Aerospace Engineering Dynamics and Control seminar. Slides can be found here.
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April 5: Hang receives the Distinguished Leadership Award from the College of Engineering. In the past couple of years, he has served multiple leadership roles in the College including co-chair of the 2021 Engineering Research Symposium (ERS), president of the Mechanical Engineering Graduate Council (MEGC), and a founding member of the Mechanical Engineering DEI committee.
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March 1-5: Trung and Nick present their recent research at SIAM Conference of Computational Science and Engineering (CSE21).
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March 3: We just released a preprint on inverse design of a self-oscillating gel using deep learning. This paper explores the construction of a deep learning architecture to map the motion of a sheet-like gel into parameters of its construction. You can find the paper, supplementary materials and videos, and the code at the following links preprint, supplementary material, code on bitbucket.
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February 25: We just released a preprint on reverse-mode differentiation for tensor networks with arbitrary network topologies. This paper shows how automatic differentiation schemes for sequences of tensor contractions can be enhanced by exploiting a few operation-specific properties. You can read the paper here.
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February 1: We just released a preprint on ensemble approximate control variate estimators. This paper addresses the issues regarding pilot sampling approaches for estimating correlations necessary to achieve variance reduction in multifidelity methods. You can read the paper here.

2020

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December 14-15 Alex presents at the Machine Learning in Science & Engineering Conference hosted by the Data Science Institute at Columbia about our Bayesian nonlinear system identification approaches. You can find the presentation here.
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December 14-18: Nick presents our paper "Bayesian Identification of Hamiltonian Dynamics from Symplectic Data" at CDC 2020 You can read the paper here.
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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.
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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.
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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."
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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.
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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

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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.
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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.
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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"
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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."
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June 20: Alex gives an invited talk at LIMSI-CNRS, Orsay, France: "Compression algorithms for enabling high-dimensional motion planning"
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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.
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May 23: Alex gives an invited talk at Sandia National Laboratories, Albuquerque, NM: "Compression algorithms for enabling high-dimensional motion planning"

2018

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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"
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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"
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June 11–15: Alex gives a talk at the 6th European Conference on Computational Mechanics, in Glasgow, Scotland
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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."
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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"
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January 30 – February 1: Alex participated in the DOE Workshop on Scientific Machine Learning in Rockville, MD.
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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"
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January 19: Alex gives a talk in Ann Arbor at the Aerospace Graduate seminar: "Compression algorithms for enabling high-dimensional motion planning"
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January 18: Alex gives a talk in Ann Arbor at the Aerospace Graduate seminar: "Compression algorithms for enabling high-dimensional motion planning"
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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