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.
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.
Machine learning on graphs (static/dynamic, attributed, undirected/directed, single/ensemble) has emerged as an important research topic that finds applications in many domains including social networks, infrastructure design and maintenance, drug discovery, brain networks, and material design. This course will discuss recent advances in machine learning on graphs including neural network architectures and methods to encode graphs into low-dimensional spaces to facilitate machine learning.
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), multi-sensory 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.
This course covers advanced topics on information retrieval, web search, and related scalable information systems. The content to be focused includes search engines, indexing, retrieval, and ranking, and support for large-scale online search services/data processing. Recent papers in top conferences will be reviewed, and issues in relevance, efficiency, and scalability will be studied.
This course will focus on learning problems for networking, i.e., how network protocols or network operators make their decisions at different granularities (e.g., network, TCP, application, etc.) to keep networks safe and performant. How these decisions are made right now, and how one can replace existing heuristics-based decision-making with ML-based learning models. We will learn about the challenges of applying machine learning to learning problems in networking and how we can resolve them.
The Internet of Things (IoT) embeds ordinary physical objects in our environment with digital intelligence -- via sensing, control, communications, and compute capabilities. As such, IoT advances are likely key drivers of the next computing revolution. This course will teach IoT systems end-to-end, including the fundamentals of IoT devices, software architectures, communication protocols, security approaches, as well as the distributed, multi-tier (e.g. sensors, edge, and cloud) programming and deployment considerations needed to support emerging IoT applications.