CS Department Colloquium Series

Concurrency, the processing of multiple requests simultaneously, is one of the most challenging problems large-scale server applications face in practice.

In this talk I will present my research on making computers see and think, two abilities essential for deep understanding of pixels. In particular, I will focus on understanding not just visual objects but also their connections.

In recent times, computer vision has made great leaps towards 2D understanding of sparse visual snapshots of the world. This is insufficient for robots that need to exist and act in the 3D world around them based on a continuous stream of multi-modal inputs.

The field of computer vision has made enormous progress in the last few years, largely due to convolutional neural networks. Despite success on traditional computer vision tasks, our systems are still a long way from the general visual intelligence of people.

Almost all of human knowledge is now available online, but the vast majority of it is principally encoded in the form of human language explanations. In this talk, I explore novel neural network approaches that open up opportunities for getting a deep understanding of natural language text.

Programming—the means by which we tell computers what to do—has changed a lot over time.

Why is my Internet slow? Why have I run out of data again? Is my thermostat talking to strangers? Internet users face seemingly simple questions like these every time they connect to the Internet with increasingly significant consequences for inaccurate information.

How can interactive devices connect with users in the most immediate and intimate way? This question has driven interactive computing for decades. If we think back to the early days of computing, user and device were quite distant, often located in separate rooms.

Abstract: