Check the 2023 Edge Symposium where I will be giving a keynote on fusing AI and Optimization for Energy.
Keynote at the 2023 Edge Symposium
Category: News
Seminar at Brown Engineering
I will be delighted to be giving a talk at Brown Engineering next week.
Impacts of Differential Privacy on Fostering More Racially and Ethnically Diverse Elementary Schools
Check this featured piece by Keyu Zhu and Nabeel Gillani at the Montreal AI Ethics Institute.
Institute Research Awards Honor 4 Engineers
Georgia Tech’s executive vice president for research has recognized four College of Engineering faculty members and a spaceflight group for their outstanding contributions to the research enterprise.
Subject to
My interview with the Anand, who is performing an incredible service to the community.
EdX MOOC on Constraint Programming
On February 7, the first session of our EdX MOOC on Constraint Programming will take place. It uses the MINI-CP system.
Longitudinal Education Program of AI4OPT
AI4OPT moved into the CODA building in Midtown Atlanta
AI4OPT@GT moved to the 12th floor of the CODA building. Per wikipedia, “CODA is a mixed-use development at Tech Square in Midtown Atlanta. The 770,000-square-foot (72,000 m2) building contains 645,000 square feet (59,900 m2) of office space, 80,000 square feet (7,400 m2) of “high performance computing space/data center”, 30,000 square feet (2,800 m2) of street level retail space, and a 20,000-square-foot (1,900 m2) outdoor living room”.
2022 Caspar Bowden PET Award for Outstanding Research in Privacy Enhancing Technologies
Cuong Tran, Ferdinando Fioretto, Pascal Van Hentenryck, and Zhiyan Yao have been awarded the 2022 Caspar Bowden PET Award for Outstanding Research in Privacy Enhancing Technologies for their paper “Decision Making with Differential Privacy under the Fairness Lens” published in the Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence (IJCAI-21).
Machine learning for fast and Scalable AC-OPT
Check the paper Spatial Network Decomposition for Fast and Scalable AC-OPF Learning for a machine learning approach to AC-OPF. The paper combines ideas from optimization and machine learning to obtain a fast and scalable learning approach.