This course provides an in-depth examination of the fundamental principles of information assurance: authentication, integrity, confidentiality of distributed systems, network security, malware, privacy, intrusion detection, intellectual property, and protection.

This course will provide an introduction to the tools and techniques needed to construct and analyze performance models of computer systems, distributed systems, and communication networks.

Basic and advanced techniques for text-based information systems, including retrieval models, indexing and text representation, browsing and query formulation, data-intensive computing approaches, evaluation, and issues surrounding implementation. The course will include a substantial project such as implementation of major elements of search engines and applications.

This course examines the structure of modern computer systems. We explore recent research results that are influencing modern machine organizations, then consider specific features and their impact on software and performance. These may include superscalar issue, caches, pipelines, branch prediction, and parallelism. Midterm and final exams, individual projects, homework, in-class exercises. Prerequisites: COMPSCI 535 or equivalent.

Rigorous introduction to mathematical logic from an algorithmic perspective. Topics include: Propositional logic: Horn clause satisfiability and SAT solvers; First Order Logic: soundness and completeness of resolution, compactness theorem. We will use various state-of-the-art tools for applying logic to automatically verifying correctness properties of programs or finding errors, including model checkers, SAT and SMT solvers and theorem provers.

The design and analysis of efficient algorithms for important computational problems. Paradigms for algorithm design including Divide and Conquer, Greedy Algorithms, Dynamic Programming; and, the use of Randomness in Algorithms. Algorithms for Sorting and Searching, Graph Algorithms, Matroid, Linear Programming, Proving NP completeness, Approximation Algorithms for NP Complete Problems, and others. Prerequisites: The mathematical maturity expected of incoming Computer Science graduate students, knowledge of algorithms at the level of COMPSCI 311.

Students will work collaboratively to construct production-grade software used to advance the goal of Child Rescue. This course is a group-based, guided independent study. Our goal is to build practical machine learning models to be used by professionals dedicated to rescuing children from abuse. Students will be encouraged to design and build their own diagnostic and machine learning tools, while also learning from professionals in the fields of digital forensics and law enforcement.

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