Sharyn O’Halloran, George Blumenthal Professor of Political Economics and Professor of International and Public Affairs at Columbia
Jeannette Wing, Avanessians Director of the Data Sciences Institute at Columbia University
"FATES: Fairness, Accountability, Transparency, Ethics, Safety and Security"
Presented by Neal Goldstein, Managing Director JP Morgan – Global Head of Connectivity Solutions
Dan Jurafsky | Stanford University, Linguistics and Computer Science
Presented by Lucas Saloumi (Data Scientist), Rick Winslow (Head of Commercial Digital Innovation) and Zeina Zeitouni (Product Manager)
Capital One is a pioneer in applying both artificial intelligence and design thinking to make banking smarter, more intuitive and personal. Presenters from the Commercial Banking Digital Innovation team will show how designers and data scientists can work together on cross-functional teams to solve complex problems and build compelling new products. They will share examples of how machine learning classification and outlier detection has been applied in their business. And they will discuss the challenges faced when deploying intelligent systems at scale, all while keeping customers and business partners engaged in the process.
This is event is co-sponsored by the Department of Computer Science's Distinguished Lecture Series.
Yann LeCun, Facebook AI Research & New York University
Dr. Geoffrey West
Author of Scale: The Universal Laws of Growth, Innovation, Sustainability and the Pace of Life in Organisms, Cities, Economies, and Companies
Data Science Institute | Sense, Collect & Move Data Center Seminar
Speaker: Steven Low, Caltech
Optimal power flow (OPF) is fundamental because it underlies numerous power system operation and planning problems. In this talk, I will give a sample of optimization problems in the management of a large network of distributed energy resources. The nonlinearity of power flow equations leads to the nonconvexity of OPF, one of the main computational challenges in power system applications. We describe a method to deal with nonconvexity through semidefinite relaxation. Semidefinite programs are hard to scale to large OPF problems. We describe a highly scalable distributed solution based on ADMM. These algorithms are offline in that they iterate until the computation has converged before applying the final solution to the grid, and are therefore not suitable for real-time optimization of distributed energy resources at scale. We describe realtime OPF that explicitly exploits the network as a power flow equation solver and characterize its performance in tracking changing network conditions. Finally, in practice, not all network nodes have sensors or can be controlled. We characterize controllability and observability of power flow dynamics in terms of the spectral properties of its Laplacian matrix. This characterization can be used to optimize the placement of sensors and actuators in the grid.