CS 598

Fall 2022 All Classes

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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 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
69375	Online	AO2	ARRANGED	n.a.	n.a.	Renear, A	Part of Term: 1 Date Range: 08/22/22-12/07/22 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/MCS-DS Program. Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC.
49223	Lecture-Discussion	AWG	3:30PM -4:45PM	TR	1043 Sidney Lu Mech Engr Bldg	Ganesan, A	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: ML for Systems Section Info: Machine learning has been recently transforming the way we build computer systems. For example, fundamental mechanisms in computer systems such as indexing, scheduling, query processing, and caching are being replaced by learned components. This course will explore such recent advancements in using ML techniques for building systems. The course will be based on two pillars: (i) reading papers and (ii) doing a research project. We will read and review two papers each week from systems, databases, and networking conferences. Students will also form a small team (2-3 members) to do a sizable research project. A good project involves exploring a new idea or conducting an in-depth study. Course prerequisite: 1. An undergraduate course in one of operating systems (CS 423), distributed systems (CS 425), databases (CS 411), or computer networks (CS 438). 2. A preliminary background in machine learning. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
52616	Lecture-Discussion	BRC	2:00PM -3:15PM	TR	1103 Siebel Center for Comp Sci	Ray Chaudhury, B	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Comp. Social Choice Theory Section Info: This course introduces students to the field of social choice theory and analysis of multi-agent systems. At a high-level, we cover the theoretical foundations of collective decision making in numerous systems that involve a set of agents with heterogeneous preferences. This will involve topics in voting theory, fair-division (discrete and continuous), markets, preference elicitation, and stable matchings. We will also discuss concepts and algorithmic results that lie at the intersection of all the aforementioned topics. It is strongly suggested that students have the prerequisite course of CS 473. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
49221	Online	CC1	ARRANGED	n.a.	n.a.	Farivar, R Kudaligama, V	Part of Term: 1 Date Range: 08/22/22-12/07/22 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 MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
46988	Lecture-Discussion	CM	9:30AM -10:45AM	TR	1105 Siebel Center for Comp Sci	Mendis, C	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: ML for Compilers & Architect. Section Info: ML 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. 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.
70734	Lecture	DEL	3:30PM -4:45PM	TR	1105 Siebel Center for Comp Sci	Delgosha, P	Part of Term: 1 Date Range: 08/22/22-12/07/22 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. Not intended for First Time Freshman students.
58261	Lecture-Discussion	DH	2:00PM -3:15PM	WF	1302 Siebel Center for Comp Sci	Heath, D	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Secure Computation Section Info: Description: This course covers secure multiparty computation (MPC), a suprisingly powerful branch of cryptography that allows mutually untrusting parties to work together to securely run programs on private data. We will discuss both the theory and the practice of this emerging technology. Our discussion will cover computing on encrypted data, zero-knowledge proofs, oblivious RAM, and more. Suggested Prerequisites: CS 374, CS 361. Mathematical maturity is required; participants will be expected to read and write formal definitions/proofs, and to read and present research papers. Basic background on probability theory is recommended. Formal cryptography background is explicitly not required. Restriction(s): Restricted to Graduate - Urbana-Champaign.
64615	Lecture-Discussion	EKS	11:00AM -12:15PM	TR	1302 Siebel Center for Comp Sci	Soltanaghai, E	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Smart cities, homes, & beyond Section Info: This course explores the principles and practice of smart physical places and things. New devices have been added to cities, homes, factories, cars, and even to humans (inside and out), hoping that this influx of technology will help us solve pressing societal issues in all facets of life such as energy, personal health, environment, or safety. The challenges, however, remain in designing and scaling the hardware platforms, networking protocols, and machine perception algorithms to enable this new class of computing. This course will cover state-of-the-art research papers that address various visions of the future platforms supporting smart and connected systems. It will also stress the cyber physical aspects of these systems, providing safe, secure, and efficient interaction with the physical world. This course will offer significant hands-on experience through a semester-long project and paper critiques (course website: https://elahe.web.illinois.edu/598EKS.html 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.
43665	Online	GGC	9:30AM -10:45AM	MW	n.a.	Chacko, G	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Computational Scientometrics Section Info: This graduate course in scientometrics will focus on bibliometric techniques. It will also feature invited lectures in history of science, sociology, and statistics. Beyond reviews and discussion of the research literature that uses bibliometric techniques, student teams will be required to design and execute a research project, and generate publishable findings. Emphasis will be placed on (i) creating an interdisciplinary environment where questions and interpretation are considered and students with relatively weak programming skills are not disadvantaged (ii) the use of open source computing tools. Students will be evaluated according to their level of participation, their presentations, and the quality of their draft and final reports. Instructors will engage with interested students even after the course to submit and publish research findings in respected venues. http://tandy.cs.illinois.edu/bibliometrics.html Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
49222	Lecture	HPN	12:00PM -1:20PM	WF	3020 Electrical & Computer Eng Bldg	Mittal, R	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: High-Speed/Progrmable Networks Section Info: Topic: Emerging Programming Paradigms. A new generation of applications is changing the nature of programming with the need for scalability, parallelism, distribution, and mobility. Moreover, web applications require context awareness; cloud computing requires balancing availability, consistency and reliability; sensor networks use broadcast messages and have limited computational resources; and cyberphysical systems must also specify real-time control. The course will cover actor languages and related programming paradigms to address these challenges. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
70878	Lecture	JH	12:30PM -1:45PM	TR	0216 Siebel Center for Comp Sci	Han, J	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Text Mining: A New Paradigm Section Info: This course is on text mining, with a focus on principles and methods for mining structured knowledge from massive unstructured text. It will cover the following topics: • Representation learning for text mining • Unsupervised and weakly supervised text embedding and topic discovery • Phrase mining, parsing and fine-grained information extraction • Taxonomy construction, expansion, and enhancement • Text classifications, text summarization, and multi-dimensional text analysis • Text-rich information networks and knowledge graphs https://wiki.illinois.edu/wiki/display/cs591han/CS+598%3A+Text+Mining%3A+A+New+Paradigm Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
72124	Lecture-Discussion	KKH	9:30AM -10:45AM	MW	2101 Everitt Laboratory	Hauser, K	Part of Term: 1 Date Range: 08/22/22-12/07/22 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.
40107	Lecture-Discussion	LTL	3:30PM -4:45PM	TR	4025 Campus Instructional Facility	Wang, Y	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Learning to learn Section Info: There has been a recent resurgence of interest in learning to learn, or meta-learning. In the standard machine learning paradigm, a model is trained on a set of examples and is specialized for the single task it is trained for. By contrast, meta-learning is performed on a set of tasks and leverages prior experiences when tackling a new task. This course will cover foundation principles, historical perspective, and recent progress of meta-learning. We will position meta-learning with respect to related areas, such as transfer learning, multi-task learning, and continual learning. The course will also discuss various applications of meta-learning in the fields of computer vision, natural language processing, reinforcement learning, and robotics. Students will be required to present and critique research papers and perform a related research project. By the end of the course, students will be able to understand and implement the state-of-the-art meta-learning algorithms and be ready to conduct research in this direction. https://yxw.web.illinois.edu/course/CS598LTL.html 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.
55918	Lecture	PPM	12:30PM -1:45PM	TR	2233 Everitt Laboratory	Rauchwerger, L	Part of Term: 1 Date Range: 08/22/22-12/07/22 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: 08/22/22-12/07/22 Credit: 4 hours Section Title: Practical Statistical Learning Restriction(s): Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC.
49224	Lecture	RAP	12:30PM -1:45PM	WF	2018 Campus Instructional Facility	Alagappan, R	Part of Term: 1 Date Range: 08/22/22-12/07/22 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.
59671	Lecture	SG	9:30AM -10:50AM	TR	2015 Electrical & Computer Eng Bldg	Gupta, S	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Learning-Based Robotics Section Info: This course will introduce students to recent developments in the area of learning-based robotics. The course will start with an overview of background material from relevant subfields: computer vision, machine learning, robotics and control theory. Next, we will discuss advanced techniques for learning policies for robots, such as model-free reinforcement learning with function approximators, model learning, model-based RL with learned models, imitation learning, inverse reinforcement learning, self-supervised learning, exploration, and hierarchical reinforcement learning. These advanced techniques will be covered via recent research papers that develop and validate them. The course will conclude with case-studies on robotic navigation, and manipulation from recent papers. Project work as part of the course will provide a flavor of research in this new emerging area. Prerequisites: Understanding of basic concepts in artificial intelligence, and machine learning. Students must have taken at least one of the following courses: ECE 448 / CS 440 (Introduction to Artificial Intelligence), ECE 544NA (Pattern Recognition), ECE 549 / CS 543 (Computer Vision). 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 MCS:Computer Sci Online -UIUC. Not intended for First Time Freshman students.
43668	Lecture-Discussion	SML	11:00AM -12:15PM	MW	1214 Siebel Center for Comp Sci	Olson, L	Part of Term: 1 Date Range: 08/22/22-12/07/22 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.
59663	Lecture-Discussion	TLR	11:00AM -12:15PM	TR	1310 Digital Computer Laboratory	Ringer, T	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Proof Automation Section Info: What will it take to build a world in which programmers of all skill levels can formally prove the absence of costly or dangerous bugs in software systems? This seminar will explore technologies for automating formal proofs about software systems in proof assistants like Coq, Lean, or Isabelle/HOL. Example topics include languages for proof automation, automated theorem proving for proof assistants, neural proof synthesis, and building usable automation. These topics will be explored through a combination of paper reading and collaborative exploration of code artifacts. Background in functional programming is expected; background in proof assistants is not needed. The website can be found at https://dependenttyp.es/classes/598fa2022.html. 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.
70199	Lecture-Discussion	TMC	11:00AM -12:15PM	WF	1302 Siebel Center for Comp Sci	Chan, T	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Fine-Grained Algorithms Section Info: In undergraduate algorithms classes, you have studied classical problems such as all-pairs shortest paths, longest common subsequence, edit distance, 3SUM, subset sum, triangles in graphs, etc. Have you ever wondered whether the textbook algorithms you have learned could be improved, or whether they are in fact the best possible? Here, we are interested not just in determining whether the problems are polynomial-time solvable, but in their "fine-grained" complexity (quadratic time? cubic time? etc.). We will describe the latest theoretical techniques for obtaining (slightly) improved algorithms for these classical problems and their variants (in general as well as important special cases). We will also prove conditional lower bounds via reductions that relate the fine-grained complexity of one problem to another. 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.
66318	Lecture	UCP	2:00PM -3:15PM	WF	1035 Campus Instructional Facility	Cobb, C	Part of Term: 1 Date Range: 08/22/22-12/07/22 Credit: 4 hours Section Title: Usable Cybersecurity & Priv Section Info: Usable Security and Privacy sits at the intersection of Human-Computer Interaction (HCI) and traditional Security and Privacy (S&P). Usable S&P research asks questions like: Why aren’t more people taking advantage of this awesome, secure system we’ve implemented? What are people actually concerned about? How can we create technologies that address their concerns? And/or how can we get people to be concerned about the S&P problems we think are important (and behave accordingly)? In short – what needs to happen in order to reduce and/or mitigate real-world S&P risks in practice? The course will assume only a basic prior understanding of S&P and HCI; however, students whose primary research interests are in these areas are especially encouraged to enroll. Please contact the instructor if you have questions regarding the material or concerns about whether your background is suitable for the course. More information about the course is available at https://sites.google.com/illinois.edu/ usablespcs598cc 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.