no code implementations • 1 May 2023 • Benjamin D. Kraske, Anshu Saksena, Anna L. Buczak, Zachary N. Sunberg
As artificial intelligence (AI) algorithms are increasingly used in mission-critical applications, promoting user-trust of these systems will be essential to their success.
no code implementations • 16 Feb 2023 • Jennifer Sleeman, David Chung, Anand Gnanadesikan, Jay Brett, Yannis Kevrekidis, Marisa Hughes, Thomas Haine, Marie-Aude Pradal, Renske Gelderloos, Chace Ashcraft, Caroline Tang, Anshu Saksena, Larry White
We describe an adversarial game to explore the parameter space of these models, detect upcoming tipping points, and discover the drivers of tipping points.
no code implementations • 14 Feb 2023 • Jennifer Sleeman, David Chung, Chace Ashcraft, Jay Brett, Anand Gnanadesikan, Yannis Kevrekidis, Marisa Hughes, Thomas Haine, Marie-Aude Pradal, Renske Gelderloos, Caroline Tang, Anshu Saksena, Larry White
We describe how this methodology can be applied to the discovery of climate tipping points and, in particular, the collapse of the Atlantic Meridional Overturning Circulation (AMOC).
no code implementations • 17 Mar 2021 • Kevin Schultz, Anshu Saksena, Elizabeth P. Reilly, Rahul Hingorani, Marisel Villafane-Delgado
Collective motion among biological organisms such as insects, fish, and birds has motivated considerable interest not only in biology but also in distributed robotic systems.
no code implementations • 16 Mar 2021 • Ryan Mukherjee, Derek Rollend, Gordon Christie, Armin Hadzic, Sally Matson, Anshu Saksena, Marisa Hughes
In this work, we develop machine learning models that use satellite imagery to perform indirect top-down estimation of road transport emissions.
no code implementations • 21 Jul 2020 • Kevin Schultz, Marisel Villafane-Delgado, Elizabeth P. Reilly, Grace M. Hwang, Anshu Saksena
We assess a number of power distribution systems with respect to metrics of signal structure and identify several correlates to system properties and further demonstrate how these metrics relate to performance of some GSP techniques.
no code implementations • 6 Nov 2018 • Ritchie Lee, Ole J. Mengshoel, Anshu Saksena, Ryan Gardner, Daniel Genin, Joshua Silbermann, Michael Owen, Mykel J. Kochenderfer
Finding the most likely path to a set of failure states is important to the analysis of safety-critical systems that operate over a sequence of time steps, such as aircraft collision avoidance systems and autonomous cars.