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The Doctor of Philosophy in Computer Science (Ph.D in Computer Science) is awarded to candidates who complete both the advanced鈥慶oursework and research requirements of the program. Working closely with a faculty advisor, each student selects graduate鈥憀evel classes that strengthen their command of core computer鈥憇cience principles – ranging from machine learning and cybersecurity to data systems, software engineering, human鈥揷omputer interaction, high鈥憄erformance computing, and theoretical foundations.
Candidates are also required to carry out independent research. Under the guidance of a experienced researchers, they tackle a focused problem in computer science, aiming to innovate new ideas or technologies. The culmination of this work is a dissertation that is publicly defended before a committee of experts.
Typical milestones include a qualifying examination in the first year, a formal dissertation proposal in the second year, and ongoing pu blication of findings in leading conferences and journals. Many candidates also gain teaching experience by assisting with undergraduate or master鈥檚 courses.
Graduates of this program will be prepared for impactful careers鈥攚hether as academics shaping the next generation of computer scientists, researchers advancing knowledge in cutting鈥慹dge labs, or industry leaders driving innovation with deep technical insight.
Course Description of PhD in Computer Science
COSC 702听 听 听 听 听Advanced AI-Driven Software Engineering (3-0-3)
Prerequisites: Familiarity with (a) software engineering techniques, methods, and tools; (b) machine learning; (c) natural language processing; (d) LLMs and generative AI
This course听is an advanced course on AI-driven software engineering, which deals with the advanced topics in quality requirements for听mission-critical systems, large-scale software architecture, and data mining of software engineering repositories and artifacts. Topics include mission critical non-functional requirements听safety, security, privacy, and trust; large-scale software architecture听patterns听and听re-structuring;听data听mining听error听logs,听and听other听selected听topics.
COSC 720听 听 听 听 听 Quantum Computing (3-0-3)
Prerequisites:听Undergraduate knowledge of Linear Algebra, Probability Theory, and a graduate course on Algorithm Design (COSC 607 or听equivalent)
This course introduces the basics of quantum computation from the point of view of a computer scientist. The course starts by reviewing the basic postulates of quantum mechanics and the mathematical background needed to understand the quantum model of computation. Then simple quantum algorithms are introduced to demonstrate how quantum computing differs from classical computing, followed by more sophisticated algorithms such as Grover鈥檚 search algorithm 听and Shor鈥檚 factoring algorithm. In the last part of the course, brief introductions to more advanced topics such as quantum random walks, quantum cryptography, quantum error correction and Hamiltonian simulation are given.
Prerequisites: Design and Analysis of Algorithms. Recommended but not required: Systems Optimization or 听Advanced Systems Optimization
This is an advanced course on efficient techniques for solving convex programming problems, which covers a range of topics at the intersection of Mathematical Programming and Algorithm Design. The focus is on how efficiently to solve linear/convex programs, with emphasis on computational complexity.
Prerequisites: Advanced data structure, 听advanced statistics, 听optimization techniques
Machine learning, a subset of Artificial Intelligence, aims to create systems that automatically improve with experience. It has many applications, including on-line data analysis, data mining and anomaly detection for cyber-security. Prediction and the study of generalization from data are central topics of Data Analysis and Statistics. These two domains aim at the same goal, that is, gaining insight from data and enabling prediction. This course provides a selection of the most important topics from both of these subjects. The course will start with machine learning algorithms, followed by some statistical learning theory, which provides the mathematical foundation for them. We will then bring this theory into context, providing the transition into Bayesian analysis.
Prerequisites: Graduate level course in Image Processing and Analysis
Computer systems that automate the analysis and the interpretation of image are getting increasing demand in areas of basic research and industrial applications. Current applications include remote sensing medical diagnosis from radiographic images, control of manufacturing through parts inspection, image recovery from web servers, database management and image archives, automatic digital photo generation, criminal and forensic investigation, to mention just few. This course covers the essential and recent advanced in computer vision paradigms related deep learning and other advance image analysis techniques for solving real work applications.
Prerequisites: Graduate level course in Advanced Computer Networks 听
Secure Network Communication: Cryptographic algorithms, Digital Certificates, PKI. Network Entity Authentication and Access Control, Network Reconnaissance, Firewalls, Intrusion Detection and Prevention Systems, Honeynets. 听Security Protocols: IPsec, SSL, VPN, HTTPS. Application Security: Popular application attacks and countermeasures. Security concerns on CPS including Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA): Network security case studies. Advanced Topics in cybersecurity: IoT Security, Blockchain, Cloud Security.
Prerequisites: Graduate level knowledge of operating systems, computer communication, computer architecture, dynamical systems and partial differential equations.
This course is a hands-on introduction to high-performance computing (HPC) for PhD students whose research includes highly complex computational problems. The course will cover the HPC hardware infrastructure and programming models with emphasis on the HPC cluster currently available in KU. The first half of the course will focus on familiarizing the students with the available HPC tools such as the multicore processing nodes, graphics processing nodes, operating system, programming languages, job submission, communication protocols, and programming models. The second half of the course will apply these tools to the solutions of computational problems from various engineering disciplines, including video processing, computer animation, large-scale power grid analysis, deep learning, computational electromagnetics, and computational fluid dynamics.. One distinguishing feature of this course is a semester-long project that will result in the implementation of a full, working HPC program and its application to a computational problem in the student鈥檚 area of PhD research.
Prerequisites: Knowledge of machine learning (ML)/ artificial intelligence (AI) cybersecurity (or equivalent)
Artificial Intelligence (AI) and Machine learning (ML) extend the attack surface of devices and systems, setting up a complex interplay between attackers and defenders.听To mitigate cybersecurity threats associated with AI and ML, research focuses on analyzing security risks and developing defense methods. Additionally, efforts are made to enhance the security of AI and ML systems.
Prerequisites: Graduate level course in Image Processing and Analysis or Natural Language Processing
In this course, we explore the cutting-edge of vision-language models. This class explores the latest research and technologies that show case the benefits of large model architectures, emphasizing the integration of text and visual data. Through lectures, hands-on projects, and case studies, students will gain a deep understanding of multimodal systems, including their development, implementation, and impact on diverse applications. This course equips the students with the background needed to innovate in the rapidly evolving field of artificial intelligence.
Prerequisites: Proficiency in a high-level programming language, preferably Python, Familiarity with core concepts of machine learning and deep learning
This course offers an in-depth exploration of large language models (LLMs), starting with foundational concepts of data preprocessing and model architecture, including the transformative attention mechanisms. We then progress to advanced techniques for pre-training and fine-tuning, such as low-rank adaptation (LoRA) and reinforcement learning from human feedback (RLHF). Practical applications of LLMs for computing and engineering are also examined, such as text generation, content summarization, and semantic search. Additionally, we address pivotal research topics pertinent to advancing the technology responsibly, such as model explainability, aligning models with intended objectives, and achieving multimodal LLMs. The curriculum addresses the ethical considerations and social impact of AI, emphasizing responsible development with discussions on model transparency, bias mitigation, and aligning AI with human values. Designed to balance theory and practice, this course is designed to equip students with the knowledge and know-how required to succeed in research and industry-related roles in the rapidly evolving landscape of LLMs and generative AI.
Prerequisites: (a) Proficiency in a high-level programming language, preferably Python; (b) Familiarity with core concepts of machine learning and deep learning; (c) Good mathematical foundations in calculus, linear algebra, and probability and statistics
This course offers an integrated approach to quantum machine learning (QML), merging core principles of quantum computing with machine learning techniques. Starting with quantum fundamentals such as qubit mechanics and state manipulation, students will delve into quantum data encoding strategies, quantum circuits, and binary classification with quantum circuits. The curriculum covers quantum Bayesian networks, parameterized quantum circuits and training methods, and quantum support vector machines, highlighting their application in supervised and reinforcement learning tasks. The course contains weekly hands-on training and practical exercise sessions. The course culminates with student-led project presentations and a forward-looking discussion on QML applications and future trends to prepare graduate students to innovate at the frontier of quantum computing and machine learning.
Prerequisites: Will be specified according to the particular topics offered under this course number
This course covers selected contemporary topics in electrical and computer engineering. 听The topics will vary from semester to semester depending on faculty availability and student interests. Proposed course descriptions are considered by the Department of Electrical and Computer听Science on an ad hoc basis and the course will be offered according to demand. The proposed course content will need to be approved by the Graduate Studies Committee. The Course may be repeated once with change of contents to earn a maximum of 6 credit hours