Special Topics Course

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.

In recent years, we have witnessed the widespread usage of ML tools for various classification, detection, and control problems. More recently, we have witnessed the use of ML for various networking problems as well. However, operationalizing ML solutions for networked systems is more nuanced than simply calibrating existing tools, developed for other domains (image classification, NLP, etc.). More in-depth exploration to develop flexible, scalable, and generalizable ML-based networked systems.

One of the perennial goals of Computer Graphics is creating high quality images which are indistinguishable from photographs: a goal referred to as photorealism. Another important goal is interactivity for visualization, simulation, gaming and other real-time applications. These two goals have historically been at odds with each other.

This course is a graduate-level introduction to automated reasoning techniques and their application in tools for the design, analysis, and construction of software. In the first half of the course, we will survey the logical foundations and algorithms behind SAT solvers and SMT solvers. In the second half of the course, we will apply these techniques to automatic bug finding, program verification, and program synthesis. As a student in this course, you will learn how solvers work, and how to use them to build cool programming tools. 

The course covers all aspects of blockchains and cryptocurrencies with a slight focus on security, including distributed consensus, smart contracts, economics, scalability, and applications. We will focus on the Ethereum ecosystem as the case study.

We will cover cutting-edge research topics in quantum information and modern quantum cryptography, including unclonable cryptography, notions of quantum pseudorandomness, post-quantum cryptography, and protocols. Background in quantum information and cryptography will be assumed. 

The course is about cutting edge techniques in machine learning and cybersecurity. We will introduce various deep learning methods that are widely used in security applications, including multilayer perceptron, recurrent neural networks, and transformers. We will also discuss the trustworthiness issue of these methods, including explainability and adversarial robustness. 
 

After over 30 years of research and engineering in controllable quantum devices, we are at the cusp of discovering the first real-world applications for quantum computing systems. A critical element that connects high-level applications to low-level quantum system designs is quantum control and metrology.