Current Teaching

I am not teaching during the 2022-2023 academic year.

Past Teaching

Classes are listed in the order Fall, Winter, Spring unless otherwise stated.

Course Descriptions

CSE 216: Formal Methods

Formal methods are a set of mathematical techniques for modeling, designing, and analyzing systems to obtain rigorous guarantees about their behavior. These include ways to prove that a system is correct under all possible inputs, or to automatically synthesize correct systems from high-level specifications of what they should do. This course will survey a range of fundamental formal methods techniques for modeling, specification, verification, synthesis, and testing, highlighting applications to hardware and software design, cybersecurity, robotics, autonomous systems, and machine learning. The course will include a project where students will apply formal methods to a problem in a field of their choice. Primary topics we will cover include logical formalisms, SAT/SMT solving, model checking, program synthesis, symbolic execution, and test generation. According to student interest, we will also cover a selection of advanced topics such as modeling of cyber-physical systems, temporal logic falsification, and verification of neural networks.

Prerequisites: No formal prerequisites, but students should be familiar with propositional (Boolean) logic, finite automata, the complexity classes P and NP, and basic graph algorithms. CSE 103 or an equivalent course would be suitable to provide this background.

CSE 102: Introduction to Analysis of Algorithms

This course covers fundamental techniques for the design and analysis of algorithms. We will discuss how to formally describe an algorithm and rigorously prove it correct, as well as techniques for analyzing the amount of time or memory an algorithm requires. We will study algorithms for core graph and optimization problems that are widely used in practical applications, as well as several general paradigms for designing new algorithms. These include divide-and-conquer, greedy algorithms, and dynamic programming. At the end of the course, students will understand how many of the most important algorithms work, and be able to design and analyze correct and efficient algorithms for new problems.

CSE 103: Computational Models

This course covers mathematical models of computation. These models provide the foundation for the theory of computer science, allowing us to ask and answer questions like "are there problems that computers can’t solve?" The concepts and algorithms we will study also underlie many areas of computer science practice, e.g. textual search, compilers, and constraint solvers. We start with finite automata and regular languages, studying their expressiveness as well as algorithms for manipulating them. We do the same for the more powerful models of pushdown automata and context-free languages. In the second half of the course, we study Turing machines, a model of general computation. We will explore computability theory, which examines which problems can be solved by algorithms, as well as complexity theory, which examines what resources are needed to solve computational problems.