C3.ai DTI Announces AI for Energy and Climate Security Awards
C3.ai DTI Awards $4.4M to 21 Projects – five involving Digital Futures – to Advance Breakthrough AI Research for the Energy Sector
C3.ai Digital Transformation Institute (C3.ai DTI) announced on June 10 the second round of C3.ai DTI awards, focused on 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 issued this call for proposals in February 2021 and received 52 submissions. A rigorous peer-review process led to 21 awards for research proposals to improve resilience, sustainability, and efficiency through such measures as carbon sequestration, carbon markets, hydrocarbon production, distributed renewables, and cybersecurity, among other topics.
“The world’s energy infrastructure will need to undergo radical changes to address the impact of global energy generation,” says Thomas M. Siebel, chairman and CEO of C3ai. “In the face of this crisis, the Institute is proud to bring together the best and brightest minds and provide direction and leadership to support objective analysis and AI-based, data-driven science for climate security.”
The 21 projects were each awarded $100,000 to $250,000, for an initial period of one year, in one of nine categories.
“Digital Futures is a member of the C3.ai Digital Transformation Institute since February 2021, and we are proud to see five projects involving our researchers among the successful grantees,” says Karl H. Johansson, Director Digital Futures and Professor at KTH. “These projects are in collaboration with UC Berkeley, University of Illinois at Urbana-Champaign, MIT and Princeton.”
The awarded projects involving Digital Futures are:
Optimization of Agricultural Management for Soil Carbon Sequestration Using Deep Reinforcement Learning and Large-Scale Simulations
- Naira Hovakimyan, W. Grafton and Lillian B. Wilkins Professor, University of Illinois at Urbana-Champaign
- Nicolas Martin, Assistant Professor, Crop Sciences, University of Illinois at Urbana-Champaign
- Guillermo Marcillo, Agronomy Data Scientist, University of Illinois at Urbana-Champaign
- Zahra Kalantari, Associate Professor in Environmental and Engineering Geosciences, KTH Royal Institute of Technology
- Henrik Sandberg, Professor, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology
- Saurabh Amin, Associate Professor, Civil and Environmental Engineering, Massachusetts Institute of Technology
- György Dán, Professor, Network and Systems Engineering, KTH Royal Institute of Technology
- Gyözö Gidofalvi, Associate Professor, Geoinformatics, KTH Royal Institute of Technology
Machine Learning for Power Electronics-enabled Power Systems: A Unified ML Platform for Power Electronics, Power Systems, and Data Science
- Minjie Chen, Assistant Professor of Electrical and Computer Engineering, Princeton University
- H. Vincent Poor, Michael Henry Strater University Professor, Princeton University
- Prateek Mittal, Associate Professor of Electrical Engineering, Princeton University
- Lars Nordström, Professor, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology
- Xiongfei Wang, Visiting Professor, KTH Royal Institute of Technology