CS 598

Fall 2023 Part of Term 1

Part of Term 1
Aug 21-Dec 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 fall 2023
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
69375	Online	AO2	ARRANGED	n.a.	n.a.	Willis, C	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Foundations of Data Curation Section Info: This course is only for students that are in the online Computer Science MCS Program. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
49221	Online	CC1	ARRANGED	n.a.	n.a.	Farivar, R	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Cloud Computing Capstone Section Info: This course is only for students that are in the online Computer Science MCS/MCS-DS Program. 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. Not intended for First Time Freshman students.
42377	Lecture	CG	11:00AM -12:15PM	TR	Siebel Center for Comp Sci	Gunter, C	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Sec and Priv for IoT in Homes Section Info: 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 First Time Freshman students.
46988	Lecture-Discussion	CM	9:30AM -10:45AM	TR	Sidney Lu Mech Engr Bldg	Mendis, C	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: ML for Compilers & Architect. Section Info: Machine Learning for Compilers and Architecture This course will explore how modern machine learning techniques are used in compilers and in computer architecture for systems decision making. We will first go through the basics of modern deep learning techniques including primers on different neural network architectures and the basics of sequential decision making. Then, we will cover how these techniques are used in the context of systems decision making including compiler optimizations, auto-tuning, performance modelling, performance aware neural architecture search, hardware systems design etc. We will go through recent papers on each topic to understand and to critically evaluate the latest developments in this space. This course will help prepare students for independent research in the covered topic areas. Prerequisites: Following are preferred courses. CS421 or CS426, CS 433 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 First Time Freshman students.
70734	Lecture	DEL	3:30PM -4:45PM	TR	Location Pending	Delgosha, P	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Methods & Algor. in Lg. Graphs Section Info: Many modern data arrives in a form that is best represented by combinatorial structures such as graphs, rather than classical time series. Graphs show up in various examples and applications, ranging from social networks and internet graphs to biological data. Modeling the interaction between objects as a graph allows us to better understand, analyze, and predict the behavior of such networks. Such understanding is crucial in subsequent applications, including but not limited to estimation, learning, data compression, and community detection. The focus of this course is to study mathematical tools to analyze graphs, specifically random graphs as models for large graphical data. We further employ this analysis to discuss several applications such as epidemiology and learning. This is a graduate level course which is open to graduate students with a good level of mathematical maturity and a strong background in probability, as well as some basic background in graph theory. In this course, the students will develop skills in evaluating research papers and are expected to conduct a final research project. https://cs.illinois.edu/academics/courses/CS598DEL 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 Computer Science or Bioinformatics major(s). Not intended for First Time Freshman students.
49222	Lecture-Discussion	HPN	1:00PM -2:20PM	WF	Electrical & Computer Eng Bldg	Mittal, R	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: High-Speed/Progrmable Networks Section Info: The ever-increasing demand for higher performance, new functionality, and flexibility has given rise to radical new designs for networking infrastructure, that not only unleash exciting new opportunities, but also challenge conventional wisdom. The goal of this course is to introduce students to such recent research and industrial advancements in networking. In each lecture, we will discuss one or two recent papers that propose (or use) unconventional new designs for network stack, network interface cards, or switches. The papers are systems oriented, focusing on practical challenges associated with designing and implementing such network systems, and cover latest topics such as programmable switches, kernel-bypass networking, RDMA, and smart NICs. Prerequisites : ECE/CS 438 (Communication Networks). Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to Computer Science or Bioinformatics major(s). Not intended for MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
36002	Lecture-Discussion	KCC	2:00PM -3:15PM	TR	Siebel Center for Comp Sci	Chang, K Huang, J	Part of Term: 1 Date Range: 08/21/23-12/06/23 Special Approval: Instructor Approval Required Credit: 4 hours Section Title: Understanding LLMs AKA ChatGPT Section Info: Understanding and Using Large Language Models AKA ChatGPT- for Search Engines. Large language models (LLMs) have transformed the field of natural language processing and established a new paradigm upon foundation models for aritificial intelligence. ChatGPT has created real buzz of new applications and possibilities -- as well as wild hype and fear. Nevertheless, our understanding of LLMs-- as they are large and complex-- are limited. What can it do? How does it work? What can we use it for and how? This class will take a laboratory and collaborative approach to study by doing research: We will benchmark the behavior of LLMs and exploit its applications. As our focus, we will study search engine-- How LLM can help our knowledge acquisition? Format: hands-on, small-group research. Objective: Publishable research papers. Pre-requisites: Research experience in NLP/ML/DM (e.g., having a publication or submission in ACL/EMNLP or similar venues)-- please verify with instructor. 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.
72124	Lecture-Discussion	KKH	9:30AM -10:45AM	MW	Campus Instructional Facility	Hauser, K	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: ADV Comp. Topics in Robotics Section Info: Advanced computational topics in robotics A graduate survey course on robotics, focusing on mathematic foundations, algorithms, machine learning, and integrating software and hardware systems. Lecture topics will include physics simulation, collision checking, motion planning, probabilistic filtering and tracking, 3D perception, and robot learning. Students will read current academic papers and carry out a semester-long, team-based project. Special restrictions: no limits on CS and non-CS enrollment. Prerequisite CS 225. https://cs598kkh2022.web.illinois.edu/ 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 First Time Freshman students.
62086	Lecture-Discussion	KMC	11:00AM -12:15PM	TR	Siebel Center for Comp Sci	Cunningham, K Lewis, C	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Found. for Comp. Edu. Research Section Info: Introduction to computing education research, including: relevant cognitive, social, and cultural theories; assessment and evaluation of computing learning and attitudes; major research findings and pedagogical approaches; and current state of the field. For CS on campus MCS students only. Restriction(s): Restricted to Graduate - Urbana-Champaign.
35989	Lecture-Discussion	KPH	11:00AM -12:15PM	TR	Siebel Center for Comp Sci	Cunningham, K Lewis, C	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Foundations for Comp. Ed Res. Section Info: Introduction to computing education research, including: relevant cognitive, social, and cultural theories; assessment and evaluation of computing learning and attitudes; major research findings and pedagogical approaches; and current state of the field. PhD students only Restriction(s): Restricted to Graduate - Urbana-Champaign.
55647	Online	KSA	9:30AM -10:45AM	TR	n.a.	Saha, K	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Social Computing Section Info: This course aims to develop an understanding of online social systems. Focusing on a combination of sociological foundations and recent advances in HCI, NLP, and human-centered AI, we will learn to understand, build, and evaluate social computing systems. The course will be structured in a seminar style, having weekly readings on a variety of topics, where students will read and critique research papers, lead and engage in class discussions, and provide and receive constructive peer feedback. Students will also work in groups for research projects. This course is particularly suitable for folks who are topically interested and willing to take a project-based and seminar-based class on reading and critiquing research papers. There are no explicit prerequisites for the course, but knowledge of statistics, natural language processing, and machine learning, and proficiency in programming (Java, python, R, etc.) would be helpful. This course will be taught synchronously online. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70361	Lecture-Discussion	LAZ	11:00AM -12:15PM	WF	Digital Computer Laboratory	Lazebnik, S	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Vision Section Info: This is an advanced graduate seminar attempting to address meta-level issues in computer vision research (and in AI more generally). The format will consist of a few introductory lectures by the instructor, followed by student-led presentations in groups of up to three. Instead of focusing on cutting-edge technical topics, as is typical, this seminar will take a “big picture” view of the research landscape and answer questions including, but not limited to, the following. What “classical” papers from the history of the field are still relevant today? What will count as a “classic” several decades from now? What ideas and practices from other fields (neuroscience, cognitive science, social science, etc.) should inform our research? How are breakthrough technologies such as generative models and large language models likely to affect society, and how should their development be directed or regulated? What will be the roles of academic vs. industry researchers going forward? Etc. Apart from the group presentation, requirements will include a final project or paper, peer grading, and classroom participation. Interest in active in-person discussion is a must! Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
57715	Lecture-Discussion	MS	11:00AM -12:15PM	TR	Digital Computer Laboratory	Kang, D	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: ML + Data Systems Section Info: This course will explore the intersections between machine learning (ML) and data systems. The first half of the course will cover modern methods of using ML to analyze unstructured data (images, video, audio, text). The second half of the course will cover the data systems used to manage the training and deployments of ML. This course will be research-oriented and discussion-based: most classes will be based on research papers. There will be a final project, which will either be: 1) a novel research contribution, 2) reproducing existing work, with thorough documentation, or 3) a synthesis of the literature in a final report and blog post. Prerequisites: CS 411 (databases) and CS 440 (ML). Or equivalent experience for both. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
46983	Lecture-Discussion	PEN	9:30AM -10:45AM	TR	Loomis Laboratory	Peng, H	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Efficiency in NLP Section Info: Large-scale deep learning models have profoundly shaped the landscape of natural language processing (NLP) and AI. However, the rising computational demands of these systems have not only elevated the entry barriers to cutting-edge research, but also raised environmental concerns. Recognizing these challenges, the research community is intensively working towards enhancing the efficiency of these large models, aiming to make them more accessible for practitioners with limited resources and to address environmental concerns. In this course, we will thoroughly explore the recent progress in this area, with a focus on NLP. Though this course is primarily designed for graduate students, motivated undergraduates with suitable backgrounds are also welcome. Prior research experience in related fields (such as natural language processing, machine learning, vision, etc.) and proficiency in Python and modern deep learning frameworks are assumed. The course format includes introductory lectures by the instructor, student-led presentations and discussions of research papers, and a fun group project. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
55918	Lecture	PPM	12:30PM -1:45PM	TR	Everitt Laboratory	Rauchwerger, L	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Parallel Programming Models Section Info: Topic: Parallel programming with migratable objects. This course will teach and explore a method for parallel programming that can be used to program multicore desktop (with and without accelerators), small clusters, as well as petascale/exascale computers, with the same programming model. The model is based on the idea of over-decomposing the computation into a large number of interacting objects, mostly independent of the number of processors, and to empower an intelligent runtime system decide where and when the objects execute. Pre-requisite: No specific course requirements. Good sequential programming experience in C++ and/or Java. Prerequisite CS 225. 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 First Time Freshman students.
70683	Online	PSO	ARRANGED	n.a.	n.a.	Liang, F	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Practical Statistical Learning Section Info: This section is intended for the online MCS program only. 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.
49224	Lecture	RAP	12:30PM -1:45PM	WF	Siebel Center for Comp Sci	Alagappan, R	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Cloud Stor Sys: Theory&Practic 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). Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
40106	Lecture-Discussion	SFS	3:30PM -4:45PM	TR	Siebel Center for Comp Sci	Sultana, S	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Computing & Global Developmnt Section Info: Computing and Global Development is a course that examines the intersection of computing technologies and international development. It explores how computing can be used to address global challenges, such as poverty, inequality, and climate change. The course draws on a variety of academic disciplines, including Information and Communication Technology and Development (ICTD), Human-Computer Interaction (HCI), Development Sociology, Science and Technology Studies (STS), and political economy. The course also teaches students how to design and evaluate ICT-based interventions for development. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
73072	Lecture-Discussion	SMC	12:30PM -1:45PM	TR	Campus Instructional Facility	Chandrasekharan, E Cheng, T Cobb, C Sterman, S	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Research Methods in HCI Section Info: HCI research involves conceiving, designing, performing, analyzing data and reporting the results of experiments in HCI contexts and evaluating interactive technologies in engineering. Topics include defining the research question, selecting experimental objects, tasks, and participants, the ethical protection of subjects, selecting an experimental design, threats to validity, the collection and analysis of both qualitative and quantitative data, and reporting experimental research in publications. This is a course designed primarily for PhD students in the Interactive Computing area. It may additionally be appropriate for PhD students in other areas, or advanced master’s students seeking to enter graduate research (with instructor approval). This course does not assume prior research experience or experience in HCI, but will also be useful to students who have experience. We welcome a range of students! This section will address practical and philosophical approaches to research in HCI. We will cover a breadth of methods for data collection and analysis, and fundamental HCI research skills such as defining research questions, designing research evaluations, and providing and receiving critique. Key learning goals will include building an individual research identity and developing research community norms. Students will choose a subset of methods in which to gain individual depth, and begin to develop an individual research identity situated within the broader context of HCI. Students will work in groups to engage with HCI methods, receive and provide feedback, propose and defend research ideas, and engage with scholars and scholarly work in HCI. This section is for CS on campus MCS students only; There will be no undergrad overrides for this section. Restriction(s): Restricted to Graduate - Urbana-Champaign.
43668	Lecture-Discussion	SML	11:00AM -12:15PM	MW	Siebel Center for Comp Sci	Olson, L West, M	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Scientific Machine Learning Section Info: Familiarity with introductory numerical methods (e.g., CS 357 or TAM 470) and the basics of machine learning and neural networks (e.g., CS 446). 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. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
49828	Lecture-Discussion	SPH	12:30PM -1:45PM	TR	Campus Instructional Facility	Chandrasekharan, E Cheng, T Cobb, C Sterman, S	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Research Methods in HCI Section Info: HCI research involves conceiving, designing, performing, analyzing data and reporting the results of experiments in HCI contexts and evaluating interactive technologies in engineering. Topics include defining the research question, selecting experimental objects, tasks, and participants, the ethical protection of subjects, selecting an experimental design, threats to validity, the collection and analysis of both qualitative and quantitative data, and reporting experimental research in publications. This is a course designed primarily for PhD students in the Interactive Computing area. It may additionally be appropriate for PhD students in other areas, or advanced master’s students seeking to enter graduate research (with instructor approval). This course does not assume prior research experience or experience in HCI, but will also be useful to students who have experience. We welcome a range of students! This section will address practical and philosophical approaches to research in HCI. We will cover a breadth of methods for data collection and analysis, and fundamental HCI research skills such as defining research questions, designing research evaluations, and providing and receiving critique. Key learning goals will include building an individual research identity and developing research community norms. Students will choose a subset of methods in which to gain individual depth, and begin to develop an individual research identity situated within the broader context of HCI. Students will work in groups to engage with HCI methods, receive and provide feedback, propose and defend research ideas, and engage with scholars and scholarly work in HCI. CS and I-school PhD students; There will be no undergrad overrides for this section. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70199	Lecture-Discussion	TMC	11:00AM -12:15PM	WF	Loomis Laboratory	Chan, T	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Advanced Data Structures Section Info: This is a CS theory/algorithms course, covering selected topics in data structures, which go beyond what are typically taught in 2nd and 3rd-year undergraduate classes. Potential topics include: balanced search trees, priority queues (e.g., Fibonacci heaps), amortized analysis, the union-find problem, hashing, geometric data structures (e.g., range searching), approximate nearest neighbor search (e.g., locality-sensitive hashing), bit-packing techniques (e.g., fusion trees and succinct data structures), persistent data structures, dynamic graph algorithms (e.g., dynamic connectivity and shortest paths), distance oracles, strings and text indexing (e.g., suffix trees), I/O-efficient data structures, and (conditional) lower bounds. Prerequisites: a course like CS 473 or equivalent is recommended, but not required; algorithmically mature undergraduates are welcomed. 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 First Time Freshman students.
42393	Lecture-Discussion	WSI	12:30PM -1:45PM	WF	Siebel Center for Comp Sci	Vasisht, D	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: ADV Wireless Networks and IOT Section Info: This graduate-level seminar will cover the latest research in the domain of wireless networks and the Internet of Things. We will discuss basic principles in wireless networks and how wireless networks must evolve for the new context of the Internet of Things. Students will experience and possibly build applications on top of novel IoT platforms in digital healthcare, data-driven agriculture, ocean sensing, autonomous vehicles, security, satellites, and others. In this class, we will also have guest lectures from experts in academia and/or industry on a subset of the themes described below to get deeper into how the research problems that we are discussing impact the world out there. Preferred pre-reqs: One of CS 438, CS 439, CS 437, CS 435 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 First Time Freshman students.
43669	Lecture-Discussion	YL	2:00PM -3:15PM	WF	Everitt Laboratory	Li, Y	Part of Term: 1 Date Range: 08/21/23-12/06/23 Credit: 4 hours Section Title: Special Top. in Robot Learning Section Info: This course introduces students to recent developments in the field of robot learning. The curriculum begins with an overview of background material from relevant subfields, including computer vision, machine learning, robotics, and control theory. Next, the course delves into advanced techniques for advancing robots’ capabilities, such as model-free reinforcement learning with function approximators, model learning and model-based RL, imitation learning, inverse reinforcement learning, self-supervised representation learning, and the integration of large foundational models. These advanced techniques will be examined through recent research papers that develop and validate them. The course concludes with case studies on robotic manipulation and navigation from recent research. Participating in project work as part of the course will offer students an insight into research in this emerging field. Prerequisites: Understanding of basic concepts in artificial intelligence and machine learning. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.