Select a calendar:
Filter April Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for April 08, 2022
-
CS Colloquium: Priya Donti (Carnegie Mellon University) - Optimization-in-the-loop AI for energy and climate
Fri, Apr 08, 2022 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Priya Donti , Carnegie Mellon University
Talk Title: Optimization-in-the-loop AI for energy and climate
Series: CS Colloquium
Abstract: Addressing climate change will require concerted action across society, including the development of innovative technologies. While methods from artificial intelligence (AI) and machine learning (ML) have the potential to play an important role, these methods often struggle to contend with the physics, hard constraints, and complex decision-making processes that are inherent to many climate and energy problems. To address these limitations, I present the framework of "optimization-in-the-loop AI," and show how it can enable the design of AI models that explicitly capture relevant constraints and decision-making processes. For instance, this framework can be used to design learning-based controllers that provably enforce the stability criteria or operational constraints associated with the systems in which they operate. It can also enable the design of task-based learning procedures that are cognizant of the downstream decision-making processes for which a model's outputs will be used. By significantly improving performance and preventing critical failures, such techniques can unlock the potential of AI and ML for operating low-carbon power grids, improving energy efficiency in buildings, and addressing other high-impact problems of relevance to climate action.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Priya Donti is a Ph.D. Candidate in Computer Science and Public Policy at Carnegie Mellon University. Her research explores methods to incorporate physics and hard constraints into deep learning models, in order to enable their use for forecasting, optimization, and control in high-renewables power grids. She is also a co-founder and chair of Climate Change AI, an initiative to catalyze impactful work in climate change and machine learning. Priya is a recipient of the MIT Technology Review's 2021 "35 Innovators Under 35" award, the Siebel Scholarship, the U.S. Department of Energy Computational Science Graduate Fellowship, and best paper awards at ICML (honorable mention), ACM e-Energy (runner-up), PECI, the Duke Energy Data Analytics Symposium, and the NeurIPS workshop on AI for Social Good.
Host: Bistra Dilkina
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: By invitation only.
Contact: Assistant to CS chair
-
PhD Defense - Eric Heiden
Fri, Apr 08, 2022 @ 11:00 AM - 01:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Candidate: Eric Heiden
Time: April 8, 11am-1pm PT
Location: RTH 406 and on Zoom (https://usc.zoom.us/j/9965174023?pwd=SzlUV1NSUlZQVUNGZTNlT2h4YWpjQT09)
Committee:
Gaurav Sukhatme (chair), Jernej Barbic, S.K. Gupta, Sven Koenig, Stefanos Nikolaidis
Title: Closing the Reality Gap via Simulation-based Inference and Control
Abstract:
Simulators play a crucial role in robotics - serving as training platforms for reinforcement learning agents, informing hardware design decisions, or facilitating the prototyping of new perception and control pipelines, among many other applications. While their predictive power offers generalizability and accuracy, a core challenge lies in the mismatch between the simulated and the real world. This thesis addresses the reality gap in robotics simulators from three angles.
First, through the lens of robotic control, we investigate a robot learning pipeline that transfers skills acquired in simulation to the real world by composing task embeddings, offering a solution orthogonal to commonly used transfer learning approaches. Further, we develop an adaptive model-predictive controller that leverages a differentiable physics engine as a world representation that is updatable from sensor measurements.
Next, we develop two differentiable simulators that tackle particular problems in robotic perception and manipulation. To improve the accuracy of LiDAR sensing modules, we build a physically-based model that accounts for the measurement process in continuous-wave LiDAR sensors and the interaction of laser light with various surface materials. In robotic cutting, we introduce a differentiable simulator for the slicing of deformable objects, enabling applications in system identification and trajectory optimization.
Finally, we explore techniques that extend the capabilities of simulators to enable their construction and synchronization from real-world sensor measurements. To this end, we present a Bayesian inference algorithm that finds accurate simulation parameter distributions from trajectory-based observations. Next, we introduce a hybrid simulation approach that augments an analytical physics engine by neural networks to enable the learning of dynamical effects unaccounted for in a rigid-body simulator. In closing, we present an inference pipeline that finds the topology of articulated mechanisms from a depth or RGB video while estimating the dynamical parameters, yielding a comprehensive, interactive simulation of the real system.
WebCast Link: https://usc.zoom.us/j/9965174023?pwd=SzlUV1NSUlZQVUNGZTNlT2h4YWpjQT09
Audiences: Everyone Is Invited
Contact: Computer Science Department
