Special Topics Course

Graphs are a remarkably versatile combinatorial object, used to model social, neural, road and computer networks, execution flow of programs, relations in databases, and countless other things. In this class we will investigate computational problems whose inputs are graphs, and how algorithms for such problems can exploit the structure of the input graph to improve performance. The focus will be on provable worst-case running time performance of our algorithms. On the way the class will expose students to some of the modern structural graph theory.

In this course, we will examine upcoming user interface technologies that will impact how we interact with our devices and digital content in the future. These include: immersive technologies (augmented and virtual reality), physiological interfaces (e.g., brain and body interfaces), wearable computing (e.g., devices both for reading and writing data to the user's body), multisensory and multimodal interactions in mixed, augmented and virtual realities (e.g., spatial audio, body movement), haptics (e.g., force feedback, sensing weight, feeling textures), and others.

Mixed and Augmented Reality, an active research field since the 1990s, has recently gained significant popularity because of the possibility of being implemented on smartphones and because of its unique approach of offering context-based computing directly in a person's field of vision. Augmented Reality is the concept of overlaying computer-generated information on top of the physical world. Mixed Reality is a bit broader and subsumes the fields of Augmented Reality, Augmented Virtuality, and Virtual Reality.

This course explores advanced topics in highly scalable Internet services and their underlying systems architecture. Software today is primarily delivered as a service: accessible globally via web browsers and mobile applications and backed by millions of servers. Modern web frameworks (e.g., Ruby on Rails, Django, and Express), and continuous improvements to cloud providers (e.g., Amazon Web Services, Google Cloud Platform, and Microsoft Azure) make it increasingly easier to build and deploy these systems.

Deep learning has revolutionized many areas within AI, and it is on track to fundamentally transform many other industries. DeepMind's AlphaGo combined convolutional neural networks together with deep reinforcement learning and MCTS, and won many games against top human Go players. In computer vision, most of the leading systems in ImageNet competitions are based on deep neural networks.