CS 598

Spring 2026 Part of Term 1

Part of Term 1
Jan 20-May 6

Special Topics

SHOW FINAL EXAM SCHEDULE

Credit: 2 TO 4 hours.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

May be repeated in the same or separate terms if topics vary.

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CS 598 class schedule data for spring 2026
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
43806	Lecture-Discussion	AM	2:00PM -3:15PM	MW	1214 Siebel Center for Comp Sci	Sinha, M	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Analytic Methods: TCS/Quantum Section Info: Analytic Methods in Theoretical Computer Science and Quantum Information. Description: This is an advanced graduate-level theory course about tools from analysis that have interesting applications in algorithms, complexity theory, quantum information and machine learning theory. Tentative topics include: analysis on the Boolean hypercube, Gaussian and stochastic processes, concentration of measure and random matrix theory. Along the way we will come across many fundamental tools in probability, analysis and geometry and see nice applications of these techniques to algorithms, combinatorics, complexity, quantum pseudorandomness and quantum error correction. Prerequisite: CS 473 is recommended. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC, MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
48248	Lecture-Discussion	CAG	11:00AM -12:15PM	TR	1214 Siebel Center for Comp Sci	Gunter, C	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Sec and Priv for IoT in Homes Section Info: Topic: The Internet of Things (IoT) provides connectivity of sensors and actuators using packet networking. This course considers the development of IoT in homes with particular attention to threats to security and privacy. Students will gain familiarity with the relevant research literature and develop a research project. Pre-requisites: knowledge of computer security and privacy. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
65118	Lecture-Discussion	CCE	2:00PM -3:15PM	R	Location Pending	Bailey, B	Part of Term: 1 Date Range: 01/20/26-05/06/26 Special Approval: Instructor Approval Required Credit: 4 hours Section Title: Chicago Capstone Experience Section Info: This section is intended for students in the MCS Chicago program only. In this capstone course, students will develop and apply design and engineering knowledge to successfully complete a real-world technology innovation project. Students will learn through hands-on design and implementation work, regular mentorship, and self-reflection activities. Students will receive mentorship from the instructor and, if possible, a non-faculty stakeholder for the project. Projects may be determined through a variety of means, including projects defined by the instructor, stakeholders in industry, government agencies, and nonprofits, open source software and data projects, and projects defined by students with instructor approval. An important criterion is that the projects connect with the Chicago community in some way. This section may have online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Please speak with your professor regarding expectations. Weekly in-person meeting in Classroom A at 200 S. Wacker Dr. Chicago. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC.
62819	Online	DHO	ARRANGED	n.a.	n.a.	Sun, J	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Deep Learning For Healthcare Section Info: Welcome to the Deep Learning for Healthcare course! In this course, we will explore deep learning (DL) methods and their applications in healthcare. The course will include video lectures, self-guided labs, and homework assignments. During this time, you will learn about different DL and health applications topics, and develop practical experience in building deep learning models using healthcare data. Additionally, you will learn how to use "pyhealth," a package specifically designed for healthcare AI tasks. We encourage you to use pyhealth for your final project and, if possible, contribute to its improvement. In the second half of the course will be a group project where you will work together to understand, replicate, and extend a recently published work in deep learning for healthcare. This project will give you hands-on experience in applying DL methods to real-world healthcare problems. To assess your understanding of the course material, we will have a midterm exam before the spring break. We hope you enjoy the course and learn a lot about deep learning and its applications in healthcare!. Course Objectives Upon completion of this course, you will be able to: 1. Understand and apply various deep learning models, including deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, attention models, graph neural networks, and deep generative learning. 2. Identify and implement different healthcare applications using DL methods, such as clinical predictive models, computational phenotyping, patient risk stratification, treatment recommendation, clinical natural language processing, and medical imaging analysis. 3. Develop practical experience in implementing various deep learning models on diverse medical data, using popular deep learning frameworks like PyTorch and data science software like Jupyter Notebook. 4. Gain hands-on experience in data preprocessing, data visualization, and model interpretation for healthcare applications. 5. Develop skills in critically evaluating and selecting appropriate DL models for healthcare applications. By the end of this course, you will have a comprehensive understanding of deep learning methods and their applications in healthcare, as well as practical experience in implementing DL models using popular frameworks and tools. You will be well-prepared to work on real-world healthcare projects that involve DL and contribute to the development of innovative healthcare solutions. Prerequisites Basic machine learning knowledge is helpful but not strictly required.* Strong programming skills in Python are required. A fair understanding of linear algebra and calculus is required. Sufficient system knowledge, such as using Linux and setting up programming environments on the cloud, is required. No knowledge in healthcare domain is required. Online MCS students should select section DLH. This is the on-campus section for Chicago MCS and Urbana students. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
43771	Lecture-Discussion	DHT	9:30AM -10:45AM	MW	1214 Siebel Center for Comp Sci	Hakkani Tur, D	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Conversational AI Section Info: Conversational AI. Description: The goal of this course is to cover advanced research topics about conversational AI systems and review founding papers as well as recent work in task-oriented dialog systems, open-domain and social conversational systems, and conversations with embodied systems. We will review previous work on component-wise approaches, as well as end-to-end systems based on large language models (such as ChatGPT), and discuss where they converge and diverge. The target audience is graduate students who plan to or are already working on these topics. As our time permits, I also plan to invite leading researchers in this field to present guest lectures. Students are expected to propose and work on a research project in one of these areas; we will discuss the proposals and progress throughout the course. In addition to preparing their final projects, students will present paper reviews and will do a peer review of others' proposals and project reports. Prerequisite: CS 446 and one of CS 447 or CS 546. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC, MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
43732	Online	DLH	ARRANGED	n.a.	n.a.	Sun, J	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Deep Lrng for Healthcare Section Info: Welcome to the Deep Learning for Healthcare course! In this course, we will explore deep learning (DL) methods and their applications in healthcare. The course will include video lectures, self-guided labs, and homework assignments. During this time, you will learn about different DL and health applications topics, and develop practical experience in building deep learning models using healthcare data. Additionally, you will learn how to use "pyhealth," a package specifically designed for healthcare AI tasks. We encourage you to use pyhealth for your final project and, if possible, contribute to its improvement. In the second half of the course will be a group project where you will work together to understand, replicate, and extend a recently published work in deep learning for healthcare. This project will give you hands-on experience in applying DL methods to real-world healthcare problems. To assess your understanding of the course material, we will have a midterm exam before the spring break. We hope you enjoy the course and learn a lot about deep learning and its applications in healthcare!. Course Objectives Upon completion of this course, you will be able to: 1. Understand and apply various deep learning models, including deep neural networks, convolutional neural networks, recurrent neural networks, autoencoders, attention models, graph neural networks, and deep generative learning. 2. Identify and implement different healthcare applications using DL methods, such as clinical predictive models, computational phenotyping, patient risk stratification, treatment recommendation, clinical natural language processing, and medical imaging analysis. 3. Develop practical experience in implementing various deep learning models on diverse medical data, using popular deep learning frameworks like PyTorch and data science software like Jupyter Notebook. 4. Gain hands-on experience in data preprocessing, data visualization, and model interpretation for healthcare applications. 5. Develop skills in critically evaluating and selecting appropriate DL models for healthcare applications. By the end of this course, you will have a comprehensive understanding of deep learning methods and their applications in healthcare, as well as practical experience in implementing DL models using popular frameworks and tools. You will be well-prepared to work on real-world healthcare projects that involve DL and contribute to the development of innovative healthcare solutions. Prerequisites Basic machine learning knowledge is helpful but not strictly required.* Strong programming skills in Python are required. A fair understanding of linear algebra and calculus is required. Sufficient system knowledge, such as using Linux and setting up programming environments on the cloud, is required. No knowledge in healthcare domain is required. This course is only for students that are in the Computer Science MCS-DS Program. Additional ProctorU fees may apply. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to Computer Science or Bioinformatics major(s). Restricted to MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
65866	Online	DSO	ARRANGED	n.a.	n.a.	Park, T	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Advanced Bayesian Modeling Section Info: This section is only for students that are in the Computer Science Online MCS/MCS-DS Program offered on the Coursera platform. Additional ProctorU fees may apply. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to Computer Science or Bioinformatics major(s). Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
39663	Lecture-Discussion	ECP	9:30AM -10:45AM	MW	0220 Siebel Center for Comp Sci	Wang, G	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Ethical Computing in Practice Section Info: Ethical Computing in Practice. Description: This course examines the ethical and societal dimensions of computing technologies, with a particular focus on algorithmic decision-making and its impacts on individuals, communities, and institutions. Students will engage with frameworks such as algorithmic fairness, value-sensitive design, and responsible research and innovation (RRI), while analyzing real-world cases where algorithmic systems create both benefits and harms. The course emphasizes critical reflection, stakeholder analysis, and effective communication of technical and ethical findings. It is structured as a seminar, with weekly readings, student-led discussions, and peer feedback. Students will also complete a semester-long research-oriented project with intermediate milestones (proposal, progress update, and final presentation). For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC.
60197	Lecture-Discussion	FAL	12:30PM -1:45PM	WF	0216 Siebel Center for Comp Sci	Lai, F	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Systems for GenAI Section Info: Systems of Generative AI. Description:This course will introduce the key concepts and the state-of-the-art in practical, scalable, and fault-tolerant software systems for emerging Generative AI (GenAI). At the end of the course you will be able to: 1) Critique and evaluate the design details of state-of-the-art GenAI systems 2) Develop and utilize tools to profile and understand the performance of GenAI systems 3) Propose new research ideas in topics related to support practical GenAI. Prerequisite: Students are expected to have good programming skills and must have taken at least one undergraduate-level systems-related course (from operating systems, databases, distributed systems, or networking). Having an undergraduate ML/AI course is helpful but not required. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
46428	Lecture-Discussion	FTS	2:00PM -3:15PM	TR	106B8 Engineering Hall	Ganesan, A	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Fault-Tol Consistent Data Sys Section Info: Fault-tolerant and consistent data center systems. Description: How are distributed systems built in the modern data center? How do hardware trends impact system design? How do we rethink decades-old protocols and ideas for the modern data center? If you are curious about answers to these questions, this course is for you. This course will dive deep into replication and consensus protocols, geo-replicated systems, distributed transactions, and various consistency models and how to implement them. We will also learn how traditional distributed protocols have been rearchitected for emerging hardware such as persistent memory, RDMA, and programmable switches and NICs. We will also discuss case studies from production systems. Prerequisites: Operating Systems (CS 423) or Distributed Systems (CS 425) For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
56940	Lecture-Discussion	GA	11:00AM -12:15PM	TR	0218 Siebel Center for Comp Sci	Warnow, T	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Graph Algorithms Section Info: Graph Algorithms for Community Structure Detection in Large Networks. Description: This is a course on applied algorithms, focusing on the use of discrete mathematics, graph theory, probability theory, statistics, machine learning, and simulations, to design and analyze algorithms for community detection, community search, and community extraction in large graphs (e.g., social networks, biological networks, and citation graphs) with millions of nodes, with the goal of making important breakthroughs in either theory or development of improved scalable methods. We will examine these questions from both a theoretical perspective (e.g., computational complexity and design of algorithms for hard optimization problems, resolution limit) as well as from a data-driven perspective. Of particular interest in this course are how well existing methods actually perform on large real-world and synthetic datasets in terms of cluster quality, which includes density, separability, and well-connectedness (e.g., the size of the minimum edge cut). Clustering accuracy will also be explored using synthetic networks, and so limitations of current simulators are also of interest. The course will involve substantial literature review and critique, hands-on assignments using existing codes on large networks, and a final course project that is designed by the student, and which should be presented in a final paper that is suitable for submission to a conference such as KDD, Neurips, or Complex Networks and their Applications. This course is designed for PhD students in Computer Science with an interest in algorithm design and implementation (including parallel and HPC implementations), but students from closely related programs (e.g., ECE, ISE, Mathematics, and Statistics) with strong theoretical backgrounds are welcome. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
43812	Lecture-Discussion	GAI	11:00AM -12:15PM	TR	0216 Siebel Center for Comp Sci	Banerjee, A	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Geospatial AI Section Info: Geospatial Artificial Intelligence. Description: This course introduces core concepts and modern methods in geospatial artificial intelligence, focusing on spatial and spatiotemporal models based on data from remote sensing (RS), Earth observation (EO), and smart city systems. Geospatial data is inherently multi-modal—combining imagery, time series, maps, and sensor streams—and the course explores how to model such complexity using both classical and modern AI tools. We begin with foundational approaches including kriging, Gaussian processes, and spatiotemporal geostatistics. The course then moves into advanced AI-driven models, including self-supervised learning, geospatial foundation models, and architectures adapted from vision and language domains to Earth data. Applications include weather forecasting, crop type and yield prediction, resource management, and urban analytics. A hands-on component using modern geospatial ML frameworks (e.g., TorchGeo) will enable students to engage directly with real-world RS/EO data and models. Prerequisite: Coursework in applied machine learning, data mining, and deep learning: One of CS 412, CS 441, 444, 446, or equivalent. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC.
59156	Lecture-Discussion	GBL	3:30PM -4:45PM	WF	4039 Campus Instructional Facility	Liu, G	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Gen Mod for BioMed & Life Sci Section Info: Generative models for biomedicine and life sciences. Generative models have greatly advanced the field of biological sciences and medicine, enabling atomic level design and modeling of molecular systems such as protein, DNA and small molecules. In this course, we will discuss recent advances in generative AI for biomedicine, with special focus on geometric aware deep learning, multimodal diffusion and flow matching on diverse data manifolds, as well as foundational models for biomedicine and life sciences applications such as protein design, drug discovery, and understanding functions and dynamics of complex biomolecular systems. This will be a graduate level course in seminar format. Prerequisite: Introduction to Machine Learning. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
65116	Lecture-Discussion	HGE	2:00PM -3:15PM	TR	0220 Siebel Center for Comp Sci	Kao, D	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: HCI Applied to Games and Educ Section Info: HCI Applied to Games and Education. Description: This course will explore Human-Computer Interaction (HCI) as applied to both education and games. Students will engage in readings from all three areas, developing a foundational understanding of common approaches to studying HCI as well as current intersections with education and gaming. A major component of the course is a semester-long research project in which students will engage in HCI, games, and education research. Prerequisite: A background in HCI, games, or education is recommended. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
65175	Lecture-Discussion	HS	12:30PM -1:45PM	WF	0220 Siebel Center for Comp Sci	Sundaram, H	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Causal Methods for HCI Section Info: This course is an introduction to causal inference and bayesian statistics. The course will cover the following topics: (1) causal inference, including the structural causal models, directed acyclic graphs, counterfactuals, the do-calculus, the backdoor criterion, the front-door criterion, the instrumental variable approach, the propensity score matching method, and the potential outcomes framework; (2) Bayesian estimates of parameters, including Markov chain Monte Carlo and stochastic variational inference methods (3) Applications of causal inference and Bayesian statistics in HCI, especially with empirical data. The course will include lectures, discussions, and hands-on programming assignments using Python. Students will be expected to complete a final project that applies the concepts learned in the course to a real-world problem. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
78195	Lecture-Discussion	HSC	12:30PM -1:45PM	WF	ARR Illini Center	Sundaram, H	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Causal Methods for HCI Section Info: This course is an introduction to causal inference and bayesian statistics. The course will cover the following topics: (1) causal inference, including the structural causal models, directed acyclic graphs, counterfactuals, the do-calculus, the backdoor criterion, the front-door criterion, the instrumental variable approach, the propensity score matching method, and the potential outcomes framework; (2) Bayesian estimates of parameters, including Markov chain Monte Carlo and stochastic variational inference methods (3) Applications of causal inference and Bayesian statistics in HCI, especially with empirical data. The course will include lectures, discussions, and hands-on programming assignments using Python. Students will be expected to complete a final project that applies the concepts learned in the course to a real-world problem. This section is intended for Chicago MCS only. There may be online and in person components. You are responsible for completing homeworks, quizzes, and any in person activities that are required. Please speak with your professor regarding expectations. Weekly in-person meeting in Classroom A at 200 S. Wacker Dr. Chicago. For up-to-date information about CS course restrictions, please see the following link: http:// go.cs.illinois.edu/csregister. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS: Computer Sci OFF - UIUC.
31666	Lecture-Discussion	HT	2:00PM -3:15PM	WF	0216 Siebel Center for Comp Sci	Tong, H	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Graph Machine Learning Section Info: This course will provide an in-depth understanding of graph machine learning techniques and their applications in a variety of real problems. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
40659	Lecture-Discussion	LMZ	9:30AM -10:45AM	TR	3025 Campus Instructional Facility	Zhang, L	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Software Engr with LLM Agents Section Info: Software Engineering with LLM Agents. Description: Modern Large Language Models (LLMs) and agents have demonstrated remarkable capabilities across diverse fields. This course will dive deep into the intersection of LLM agents and software engineering, illustrating how recent advances in generative AI can substantially transform the way people build and maintain software systems. Prerequisite: CS 447, Natural Language Processing. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC, MCS: Computer Sci OFF - UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. Not intended for First Time Freshman students.
44818	Lecture-Discussion	MR	3:30PM -4:45PM	WF	2101 Everitt Laboratory	Mashfiqui Rabbi Shuvo, M	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: AI Applied to Behavior Health Section Info: The U.S. spends more on healthcare than other wealthy nations but has poorer health outcomes. One reason for this gap is the focus on “sick care,” which reactively treats illness with costly interventions that often fail to improve outcomes. A better approach is proactive care that emphasizes prevention and healthy living. Behavioral health—which includes lifestyle and mental, emotional, and social well-being—is central to this shift. In this course, we will develop practical AI solutions to monitor and improve behavioral health. This course makes extensive use of modern mobile and wearable sensing technologies. The curriculum has two parts. In the first part, we focus on continuous monitoring of well-being (e.g., mental health) and lifestyle behaviors such as physical activity, nutrition, sleep, stress, and substance use. Continuous monitoring lays the foundation for identifying health risks early, before problems occur. The second part covers interventions to improve health outcomes. We will explore early risk prediction and the design of personalized, just-in-time interventions. Each week, the instructor will introduce a specific topic (e.g., sleep), and students will present and discuss related research papers. For the final assignment, students may choose between a project or a paper that applies course concepts in an innovative way. This course takes a human-centered AI approach, as the technologies must seamlessly integrate into free-living settings for effective monitoring and intervention. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
50225	Lecture-Discussion	PBR	11:00AM -12:15PM	TR	1304 Siebel Center for Comp Sci	Zhao, S	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Adv Physics-Based Rendering Section Info: Advanced Physics-Based Rendering. Description: This course covers a core topic in computer graphics: 3D rendering by physics-based simulation of light. The course will contain lectures covering mathematical and algorithmic foundations of rendering (e.g., Monte Carlo integration and path integrals) as well as seminar-like sessions for discussing recent advances in rendering. In addition, we will go over basics in differentiable rendering in this course. Prerequisite: CS 418. https://go.cs.illinois.edu/CSregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
56938	Lecture-Discussion	PPM	12:30PM -1:45PM	TR	0220 Siebel Center for Comp Sci	Rauchwerger, L	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Parallel Programming Models Section Info: Title: Parallel Programming Models for Portable High Performance Computing Portable High Performance Computing means portable performance in time and space. In other words, the performance of programs requiring HPC (resource intensive codes) need to be portable across the main existing architectures and across the machines that will be developed in time. In order not to have to rewrite entire applications to port them across systems we raise the level of abstraction at which programs are written and leave the 'details" of mapping the application onto the architecture to the vendors themselves. One way to achieve portable performance of applications and ease of programming effort is to use a hierarchical system of generic and domain specific libraries. In this course we will present several high level libraries and languages that can be built on top of the existing software development infrastructure, i.e. do not require new compilers, debuggers, etc. performance Libraries have to implement parallel algorithms and programs without requiring the user to deal with the complexities of parallel program execution on a parallel architecture. In this course we first present the state of the art in parallel programming models. Then we will present the latest approaches to parallel library design and how they alleviate the performance portability and ease of use problems. We will cover generic libraries such as TBB, STAPL, Cilk++ and then spend some time on infrastructures for machine learning applications such as Tensorflow. Grading will be based on paper presentations and a final class project requiring parallel programming and performance evaluation on a a parallel computer. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
61163	Lecture-Discussion	RAN	2:00PM -3:15PM	WF	2101 Everitt Laboratory	Alagappan, R	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Storage Systems Section Info: Cloud data centers are powered by storage systems such as key-value stores, file systems, and databases. This course will explore such storage systems, focusing on their theoretical foundations and practical aspects. First, we will learn about data-structural ideas (e.g., LSMs, Be-Trees) and how they have led to the construction of efficient storage systems. Then, we will focus on practical systems issues (e.g., data safety, crash recovery) in building these systems. This course will be research-oriented and discussion-based: most classes will be based on research papers. An essential part of this course is a final research project. At the end of the course, students will be able to critique systems research papers, understand fundamental problems in storage systems, and have experience working on a research project. Students must have a background in undergraduate-level operating systems (CS 423). For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC.
68084	Lecture-Discussion	SC3	3:30PM -4:45PM	TR	1302 Siebel Center for Comp Sci	Saha, K	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Computational Social Science Section Info: In this course, we will explore how social behaviors are mediated by computational systems. Focusing on a combination of sociological foundations and recent advances in computational social science, natural language processing, and human-centered AI, we will learn to apply computational techniques to answer social science questions. Through this course, students will read and critique high-impact research papers, lead and engage in class discussions, work on implementing new methods during in-class lab sessions, and execute a group research project for their final paper. Prerequisites for the Course: The assignments and activities (i.e., research labs, reading reflections, research project) in this course are specifically aimed at students interested in performing computational social science research. As a result, students enrolled in this course are expected to have a good grasp of python, statistics, and basic data analysis. Restrictions: Restricted to Grad students. Undergrads can apply for exceptions. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
77278	Lecture	SEG	9:30AM -10:45AM	MW	0218 Siebel Center for Comp Sci	Lou, Y	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Software Engr for GenAI Section Info: Software Engineering for Generative AI with Professor Yiling Lou. Description: Artificial intelligence is reshaping modern software systems, with generative AI models increasingly integrated to provide intelligent functionality. This course explores emerging software engineering methods and practices for designing, developing, and operating AI-integrated software. Topics include recent advances in software quality assurance and maintenance techniques (such as testing, debugging, security, efficiency optimization, and DevOps) for agentic systems. We will also discuss testing and debugging techniques for AI libraries and compilers, as well as the broader ecosystem supporting AI-integrated software. This graduate-level course will be in seminar format, focusing on cutting-edge research papers and semester-long projects. Prerequisite: CS 447, Natural Language Processing. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
39669	Lecture-Discussion	SML	9:30AM -10:45AM	TR	2036 Campus Instructional Facility	Olson, L	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Scientific Machine Learning Section Info: Scientific Machine Learning. Description: This course will cover the theory and practice of Scientific Machine Learning (SciML), which leverages machine learning tools for scientific computing. Topics include learning-based methods for differential equations, neural ODEs and PDEs, physics-informed networks and model discovery, interpretable and explainable learning, differentiable and probabilistic programming for scientific computing, and uncertainty quantification via learning. Efficient parallel implementation of algorithms on scalable computing architectures will be emphasized. Prerequisite: Familiarity with introductory numerical methods (e.g., CS 357, CS 450, or TAM 470), the basics of machine learning and neural networks, and some knowledge of numerical methods for PDEs (e.g. CS555 or similar) is expected. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
50226	Lecture-Discussion	TCS	11:00AM -12:15PM	MW	1214 Siebel Center for Comp Sci	Herman, G	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Teaching CS at College Level Section Info: Teaching Computer Science at the College Level. Description: In this course, students will learn about the fundamentals of course design, assessment, and pedagogy for teaching computing at the college level. The course is aimed at preparing graduate students for the academic job market, both preparing for job talks and successfully navigating the first year as faculty. Students will prepare teaching demonstrations, homework assignments, observe excellent teachers, and write a teaching philosophy statement. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
40235	Lecture-Discussion	TLR	11:00AM -12:15PM	MW	0218 Siebel Center for Comp Sci	Ringer, T	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Build your own Proof Assistant Section Info: Build your own Proof Assistant. Description: Proof assistants like Rocq, Lean, and Isabelle make it possible to write machine-checked proofs about software and mathematics interactively, with the help of automation. In this class, we will work together to build a proof assistant from scratch as a class, learning about every piece from the foundations to the user experience as we build. Prerequisite: CS 421 or research experience in PL/FM/SE. For up-to-date information about CS course restrictions, please see the following link: http://go.cs.illinois.edu/csregister Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS: Computer Sci OFF - UIUC or MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.