Abstract: Abstract: Much of our success in artificial intelligence stems from the adoption of a simple paradigm: specify an objective or goal, and then use optimization algorithms to identify a behavior (or predictor) that optimally achieves this goal. This has been true since the early days of AI (e.g., search algorithms such as A* that […]

Abstract: Deep learning (DL) is a powerful approach to modeling complex and large scale data. However, DL models lack interpretable quantities and calibrated uncertainty. In contrast, probabilistic graphical modeling (PGM) provides a framework for formulating an interpretable generative process of data and a way to express uncertainty about what we do not know. How can […]

Abstract: Reconfigurable analog devices are a powerful new computing substrate especially appropriate for executing dynamical systems in an energy efficient manner. These devices leverage the physical behavior of transistors to directly implement computation. Under this paradigm, voltages and currents within the device implement continuously evolving variables in the computation. In this talk, I discuss compilation […]

Abstract: Some claim AI is the “new electricity” due to its growing significance and ubiquity. My research investigates this vision from an HCI perspective: How can we situate this remarkable technology in ways people perceive as valuable? How could we form a symbiotic relationship between AI systems and their users, to do things neither can […]

Abstract: Some claim AI is the “new electricity” due to its growing significance and ubiquity. My research investigates this vision from an HCI perspective: How can we situate this remarkable technology in ways people perceive as valuable? How could we form a symbiotic relationship between AI systems and their users, to do things neither can […]