Fall 2024

Networks are a natural way to represent a variety of interacting complex systems - from disease spreading and brain networks, to social networks and political networks.

In this course, we will study the foundations of networks and investigate how the structure of networks affects the spread of signals on the network. Next, we will learn about the geometry and topology of networks, and topological data analysis on networks. Finally, we will learn about graph neural networks and the interface between network properties and learning. This course contains a project. 

To create innovative AI-supported systems that truly benefit end-users in real-life situations, it is crucial to focus on the interactive and real-time user experience of AI. This course is centered around investigating the various aspects of human-AI systems, including interface design, user agency, explainability, ethics, and the human-centered design process involving AI. We will examine these through reading research papers and through the design and prototyping of an AI Task Guidance System.

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.

This course covers advanced topics on information retrieval systems and neural ranking algorithms. The content to be focused includes retrieval,  ranking, indexing, and online support for large-scale search services. Recent papers in top conferences on neural information retrieval will be reviewed, and issues in relevance, efficiency, and scalability will be studied.
 

Are blockchains real? There’s a lot of excitement about blockchains and cryptocurrencies mixed with a lot of skepticism and pessimism. One thing is clear, the field instigated tremendous advances to the foundations of distributed systems and applied cryptography. This course will overview key advances in blockchains with a focus on the scientific foundations underpinning them.

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.

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 sets the foundational knowledge necessary to understand quantum computing and quantum information science by covering some of the advanced linear algebra tools, and basic quantum mechanics. Students will learn about the four critical postulates of quantum mechanics, explore quantum circuits, and delve into the universal gate set. The curriculum includes studies on quantum teleportation, superdense coding, and the no-cloning theorem, alongside a comprehensive introduction to key quantum algorithms like Shor's Algorithm, Grover's Algorithm, and the Adiabatic Algorithm.