Special Topics Course

Real world tasks such as assembly, repair, or surgery, often require the simultaneous use of both hands. In these scenarios, one hand is used to grasp a tool like a screwdriver or a scalpel, while the other hand is engaged in manipulating the object being worked on. Creating one-handed interfaces for users engaged in physical tasks that involve tools or objects presents several challenges. Learning how do to these tasks with an AI-based task assistant in AR/VR requires the ability to not only perform the task but engage with the AR/VR interface simultaneously.

This course provides an introduction to digital audio through the lens of the software used by the Allosphere Research Group at UC Santa Barbara. We will learn the basics of music synthesis (e.g.

This course will cover topics in two areas under privacy-preserving computing: a) federated or privacy-preserving

(no description available) 

This is a graduate-level research course on Conversational AI.  Over the duration of this course, we will delve into the latest publications within the expansive domain of Large Language Models (LLMs) and Conversational AI.

This course will teach you everything about offline rendering, so you will be able to write a fully functional industry-level renderer (such as Disney's Hyperion and Pixar's RenderMan) that produces stunning graphics. Topics in this course will cover the physics of light, the rendering equation, Monte Carlo integration, path tracing, physically-based reflectance models, participating media, other advanced light transport methods, production rendering approaches, and so on.

There’s a lot of excitement about blockchains and cryptocurrencies mixed with a lot of skepticism and pessimism, but advances in the foundations underlying blockchains are undisputable. The goal of this course is to weave an overview of prominent blockchains systems with key technical advances the field has instigated.

Course purpose: The course focuses on the ability to translate theoretical knowledge about algorithms, data structures, and complexity to efficiently be able to perform the complete process of analyzing a problem, estimate the running time of suggested solutions, and to implement a running program.

In Fall 2023 I will teach a course on Parameterized Algorithms (Similar to the course taught in Spring 2019) - we will (likely) cover chapters 1-7, 13 and 14 of the Parameterized Algorithms book, plus possibly some more recent research papers.

This course offers students an introduction to some of the latest state-of-the-art techniques in the field of deep learning. Throughout the course, students will delve into a wide range of advanced topics and methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative models such as large language models, and transformers, among others.