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EECS at UC Berkeley
The awards recognize projects that are using AI techniques and digital transformation to advance energy efficiency and lead the way to a lower-carbon, higher-efficiency economy that will ensure energy and climate security. 'C3.ai DTI selects research proposals that inspire cooperative research and advance machine learning and other AI subdisciplines.
Each project was awarded $100,000 to $250,000, for an initial period of one year. The winning proposals were: Offline Reinforcement Learning for Energy-Efficient Power Grids - Sergey Levine, Assistant Professor, Electrical Engineering and Computer SciencesWe propose to develop offline RL algorithms to incorporate real-world data in training an RL agent to reduce emissions associated with running an electrical grid.Sharing Mobile Energy Storage: Platforms and Learning Algorithms - Kameshwar Poolla, Cadence Design Systems Distinguished Professor of Mechanical EngineeringThis proposal aims to design, validate, and test platforms and learning algorithms for mobile storage applications, which can simultaneously serve the role of generation (supplying energy) and distribution (reticulating energy).Reinforcement Learning for a Resilient Electric Power System - Alberto Sangiovanni-Vincentelli, Edgar L.
UC Berkeley, Georgia Tech and USC launch new National AI Research Institute
By unifying data-driven and model-driven approaches at the core of AI and operations research, the institute will help deliver the next generation of control and optimization algorithms for operating electricity grids with distributed renewable generation, and for designing and operating efficient and resilient supply chains.” Pascal Van Hentenryck, professor and associate chair for Innovation and Entrepreneurship at Georgia Tech’s School of Industrial and Systems Engineering, is the lead principal investigator of the AI institute.
“We are delighted to join forces with interdisciplinary research teams at Georgia Tech and USC to advance AI and optimization technologies to address grand challenges in highly constrained settings such as logistics and supply chains, energy and sustainability, and circuit design and control.” To transform decision-making at massive scales, the institute will develop approaches that not only predict and quantify uncertainty, but also continuously learn and reason to develop the best solutions for the challenges at hand.
The AI institute’s methodology thrusts include a new generation of algorithms that use end-to-end learning to tightly integrate forecasting and decision-making, and novel machine-learning methods that can handle massive combinations of factors and constraints.
Computing, Data Science, and Society
The inaugural ACM conference on Equity and Access in Algorithms, Mechanisms, and Optimization (EAAMO ’21) aims to highlight work where techniques from algorithms, optimization, and mechanism design, along with insights from other disciplines, can help improve equity and access to opportunity for historically disadvantaged and underserved communities.