LIDS Seminar Series

Abstract Online extremists in social networks pose a new form of threat to the general public. These extremists range from cyber bullies who harass innocent users to terrorist organizations such as ISIS that use social networks to spread propaganda. Currently, social networks suspend the accounts of such extremists in response to user complaints, but these…

Abstract The deployment of distributed renewable generation capacities, new ICT capabilities, as well as a more proactive role of consumers, are all motivating rethinking electricity markets in a more distributed and consumer-centric fashion. After motivating the design of various forms of consumer-centric electricity markets, we will focus on two alternative constructs (which could actually be…

Abstract Humans have been influencing the DNA of plants and animals for thousands of years through selective breeding. Yet it is only over the last 3 decades or so that we have gained the ability to manipulate the DNA itself and directly alter its sequences through the modern tools of genetic engineering. This has revolutionized…

How to conduct real-time analytics of streaming measurement data in the power grid? This talk offers a dynamic systems approach to utilizing data of different time scale for improved monitoring of the grid cyber and physical security. The first example of the talk presents how to leverage synchrophasor data dimensionality reduction and Robust Principal Component…

We consider a variant of the multiarmed bandit (MAB) problem where jobs or tasks queue for service, and service rates of different servers (agents) may be unknown. Such (queueing+learning) problems are motivated by a vast range of service systems, including supply and demand in online platforms (e.g., Uber, Lyft, Airbnb, Upwork, etc.), order flow in…

We bring the tools from Blackwell’s seminal result on comparing two stochastic experiments from 1953, to shine a new light on a modern application of great interest: Generative Adversarial Networks (GAN). Binary hypothesis testing is at the center of training GANs, where a trained neural network (called a critic) determines whether a given sample is…

Multiplier methods based on augmented Lagrangians are attractive in convex and nonconvex programming for their stabilizing and even convexifying properties. They have widely been seen, however, as incompatible with taking advantage of a block-separable structure. In fact, when articulated in the right way, they can produce decomposition algorithms in which low-dimensional subproblems can be solved…