Special Topics and Advanced Topics Courses

• CRN 13592 (Bburg)
• Instructor: Kantarcioglu
• Area 6: Data and Information

Course Description: This course provides essential tools for safeguarding sensitive data in AI applications while ensuring robust privacy protections. Topics covered include: Fundamentals of Access Control, encryption-based methods for secure data processing, confidential computing , data anonymization, differential privacy, and approaches to understanding and defending against privacy and security threats to AI models.

• CRN: 21332 (Bburg)
•  Instructor: Nikolopoulos
• Area 2: Computer Systems

Course Description:  Use specialized code generation LLMs to optimize the code we use to train and infer from LLMs. Develop AI agents for HPC code generation

• CRN: 21333 (Bburg)
• Instructor: Khatri
  
• Area 1: Algorithms and Theory

Course Description: After an introduction to the basics of quantum information theory and quantum computing, this course dives into topics of current research, including: how to realize large-scale quantum computers (error correction), building a quantum internet (entanglement distribution), and models of quantum (machine) learning

• CRN: 13663 (Bburg)
• Instructor: Hoang
• Area 2: Computer Systems

Course Description:

• CRN: 13670 (Online Sync)
• Instructor: Reddy
  
• Area 6: Data and Information

Course Description: 

• CRN: 22081 (Bburg)
• Instructor: Farghallay
• Area  6: Data and Information

Course Description: Effective teaching approaches and required instructional/research skills for CS educators. Learning theories, active learning, multimedia presentation, course design, teaching with technology, student motivation, and inclusive learning. CS education research methods and publication venues. Preparing a teaching, research, and diversity statements for CS academic job applications.

• CRN: TBD
• Instructor: TBD
• Area 8: Human-Computer Interaction

Course Description: 

• CRN: 21334 (Bburg)
• Instructor: Rho
• Area 8: Human-Computer Interaction

Course Description:

• CRN: 13674 (Bburg)
• Instructor: Yanardag
• Area 9: Intelligent Systems

Course Description:

• CRN 92303 (Online)
• Instructor: S. Noh
•  

Course Description: In this course, we will concentrate on two key algorithms widely used in contemporary systems. Specifically, cache replacement algorithms and garbage collection algorithms in log structured-based systems (LSS) will be discussed. Caching replacement algorithms have been a topic of research for decades, yet we continually see new developments. Every major IT company, including Google, Meta, and Amazon, is developing its own replacement algorithm at various levels of systems as they are core to enhancing performance. Similarly, the notion of garbage collection algorithms for LSS has been around for decades. Yet, we still see developments of new algorithms as LSS has become fundamental in systems such as SSDs and Key Value Stores (KVS) such as RocksDB. In this course, our goal is to garner a concrete understanding of these algorithms and the developments around these algorithms within systems. Prerequisite: an undergraduate course in operating systems **if you have not taken an operating systems course but feel that you meet the prerequisite, please discuss with the instructor.

• CRN: 83528 (NCR); 83525 (Bburg)
•  Instructor: J. Cho
• Area: 9 Intelligent Systems

Course Description: We make decisions in our everyday lives, often under conditions of uncertainty caused by various factors. Despite several decades of research across multiple domains and disciplines, decision-making under uncertainty remains a challenging problem. This area has been explored in numerous directions, reflecting its complexity and significance. In this course, we will focus on key concepts, theories, applications, and the latest advancements in Artificial Intelligence (AI) related to decision-making under uncertainty. Given our time constraints and our focus as computer scientists, we will delve into methods supporting decision-making, specifically through belief theory, reinforcement learning, and deep learning.

• CRN: 91671 (Bburg)
• Instructor: M. Seyam 
  
• Area 0: Ethics and Research Methods. Not for MEng credit.

Course Description: This course aims at helping students who are attending two of the major diversity conferences in the field of CS to prepare for the experience of participating in such events. The two conferences are: 

• CMD-IT/ACM Richard Tapia Celebration of Diversity in Computing Conference 
• Grace Hopper Celebration for Women in Computing

The course also builds upon such participation to engage students in diversity-related conversations, which would enable them to explore themselves and express their own ideas

• CRN: 83527 (Online)
• Instructor: S. Atkinson
• Area 7: Software Engineering

Course Description: This course is specifically designed for master’s students who have an interest in gaining knowledge about Artificial Intelligence (AI) and Machine Learning (ML) and how they are utilized in software development. Our main emphasis will be on exploring how you can leverage generative AI to enhance the software development process, including generating code, reading and analyzing code, as well as testing and documentation.

• CRN: 91706 (NCR)
• Instructor: Mengistu
  
• Area 7: Software Engineering

Course Description: This course gives students all-round practical and theoretical knowledge about Cloud Computing. The course starts with the economics of Cloud Computing and covers foundational Cloud Computing concepts such as the basics of Cloud Computing, service provisioning and deployment models, networking, and security in the cloud as well as virtualization and containerization technologies used in Cloud Computing. Serverless computing, big data analytics, and machine learning on the cloud will also be discussed. The discussions on the theoretical foundations are complemented with practical hands-on projects. Students will get a chance to use state-of-the-art solutions for Cloud Computing (such as Google's Google Cloud Platform (GCP) or Amazon’s Amazon Web Services (AWS)).

• CRN: 92336 (NCR)
• Instructor: Luther
• Area 8: Human Computer Interaction

Course Description: Open Source Intelligence (OSINT) investigations, which rely entirely on publicly available data such as social media, play an increasingly important role in protecting societies, from preventing cyberattacks, to combating disinformation, to finding missing persons. In this course, students will learn how to conduct effective, ethical OSINT investigations in domains such as cybersecurity, journalism, law enforcement, and human rights. Students will learn fundamental skills for OSINT collection and analysis, including ethics and security, discovery, verification, preservation, and publication, and the use of relevant tools and technologies. Students will work in teams to plan and execute a fictional scenario-driven OSINT Capture the Flag (CTF) event and hone their investigation skills by competing in other students' CTF events. Prerequisites: None, but creativity, tenacity, and enthusiasm for teamwork are encouraged.

• CRN: 83590 (Bburg)
• Instructor: Gao
• Area 2: Computer Systems

Course Description: This course aims to discuss key research topics in threat detection and forensics analysis. Example topics include: system auditing, ML-based threat detection, forensic investigation via data provenance techniques, threat intelligence, incident response, vulnerability management, and programmable defenses.

• CRN: 83591 (Bburg)
• Instructor: S. Hasan
• Area 2: Computer Systems

Course Description: As computing has become more ubiquitous in the lives of more people around the world -- from widespread mobile connectivity to pervasive applications of AI -- so too has the desire to apply these technical advances for social good. This course is designed for students who seek to build and design computing systems (broadly defined) to effect positive social change; as such, it takes a systems perspective to this objective: tracing root causes and identifying opportunities to effect change in an effective, beneficial, and sustainable way. Drawing upon readings from within computer science (including HCI, networking, and ICTD) as well as related literature (e.g., development economics and STS), we will take a critical perspective towards technology for social good to differentiate among interventions that actually yield social benefits, those that have minimal impact, and those that actively cause harm (despite good intentions). We will evaluate examples from a range of important areas, including global development, sustainability, health, social justice, policy, and liberation.

• CRN: 83599 (Bburg)
• Instructor: Zhou
• Area 6:  Data and Information

Course Description: In the era of big data, graph presents a fundamental data structure for modeling relational data of various domains, ranging from physics to chemistry, from neuroscience to social science. Machine learning on graphs (MLG) provides a powerful tool to distill knowledge and learn expressive representations from graph-structured data. In this course, we will introduce a number of advanced topics in MLG, including network ranking, network alignment, network summarization, community detection, anomaly detection, graph neural networks, logical reasoning over knowledge graphs, and multi-network mining.

• CRN: 83597 (Bburg)
• Instructor: Meng
• Area 7: Software Engineering

Course Description: Software Engineering (SE) focuses on the process of developing and maintaining software. The SE research proposes solutions to various problems in the requirements analysis, design, implementation, testing, and maintenance of software. With various approaches investigated, researchers aim to improve programmer productivity and software quality. 

The goal of this course is two-fold. First, it will increase your knowledge of various research topics in SE to cultivate research interests in the area, and to recommend best practice for software development and maintenance. Second, it will provide an environment that promotes and rewards creative thinking, problem solving, idea presentation, and oral and written communication.

• CRN: 91681 (Bburg)
• Instructor: I.F. Williams
• Area 8: Human-Computer Interaction 

Course Description: This course is designed to provide students with a comprehensive understanding of participatory design methods and frameworks. This involves learning about the different ways in which researchers and community members collaborate to generate knowledge that is useful for everyone. The course will cover recommended practices for building equitable partnerships with communities, as well as methods for gathering and synthesizing data to understand community priorities. Students will also have the opportunity to reflect on how their own identity shapes their design practices. At the end of the course, students will be expected to develop a community engagement plan and research proposal.

• CRN: 91681 (Bburg)
• Instructor: Y. Yao
• Area 8: Human Computer Interaction

Course Description: This course provides an in-depth look into privacy, privacy laws and self-regulations, privacy-enhancing technologies and mechanisms, and privacy design. Privacy will be examined from historical, philosophical, cultural, legal, economic, behavioral, and technical perspectives. This course is designed primarily for graduate students who are interested in privacy and are from a wide range of disciplines such as information science, computer science and engineering, law, business, media studies, economics, politics, and psychology.

• CRN: 91682 (Bburg)
• Instructor: Thomas
• Area 9: Intelligent Systems

Course Description: Humans are able to reason about how concepts read in text, heard in audio, and seen in visual content (different modalities of data) relate to one another by drawing on a learned multimodal understanding of the world. For example, reading a textual description might allow one to recognize a bird they have never seen before by drawing on a background understanding of what the color blue looks like and what a high-pitched bird call sounds like. Thus, building artificially intelligent systems capable of robust multimodal reasoning is an area of intense research interest. This graduate-level seminar course will introduce students to the latest research in multimodal computer vision, with a significant emphasis on vision and language. The course will feature foundational lectures, in-depth student-led presentations on state-of-the-art research, and classroom discussions. Students will complete an intensive, semester-long research project and report. A background in deep learning is strongly recommended. Example topics include representation learning, fusion, pretraining, privileged modalities, prompt learning, cross-modal retrieval, model architectures (e.g. transformers, two-stream, convolutional), attention mechanisms, zero-shot and few-shot recognition, knowledge representation, generative models, foundation models, and embodied vision.

• CRN: 91776 (Bburg)
• Instructor:  Bhattacharya
• Area 10 : Computational Biology and Bioinformatics

Course Description: This course will survey the emerging field of computational modeling of molecular structures driven by advances in artificial intelligence (AI), with an emphasis on predictive modeling. We will investigate relevant issues from interdisciplinary perspectives of macromolecular modeling and machine learning. Topics will include: deep generative modeling, language modeling of molecular sequences, macromolecular geometry learning and representation,and end-to-end learning: from sequence to structures. The course will emphasize readings in the scientific literature and novel projects.

• CRN: 92411 (Online Sync)
• Instructor:  S. Flammia
• Area 1: Algorithms and Theory

Course Description: This course provides an in-depth exploration of quantum algorithms, focusing on foundational concepts and cutting-edge research. Topics include seminal algorithms like Shor's and Grover’s as well as advanced subjects such as quantum simulation, machine learning, and hybrid quantum-classical algorithms will also be covered. The course includes lectures and student-led presentations. A strong background in linear algebra is required.