CS 598

Spring 2022 Part of Term 1

Part of Term 1
Jan 18-May 4

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 2022
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
44818	Lecture-Discussion	CG	11:00AM -12:15PM	TR	1035 Campus Instructional Facility	Gunter, C	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Sec and Priv for home IoT Section Info: Title: CS598CG Security and Privacy for Home IoT Description: The Internet of Things (IoT) for homes describes an increasing deployment of networked devices located in homes and able to collect sensor inputs, communicate via local networks and the internet, and actuate events within the home. Examples include networked thermostats, fire alarms, security systems, and home health monitoring systems. They also include smart speakers and appliances. The course will explore the security and privacy ramifications of such devices covering both issues with the devices themselves and their broader ecosystem of smartphones and cloud systems. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
58502	Lecture-Discussion	CTO	2:00PM -3:15PM	TR	1302 Siebel Center for Comp Sci	Khurana, D	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Quantum Cryptography Section Info: This course will begin with a brief introduction to quantum computing, and then discuss the influence of quantum computing on cryptography. We will cover: 1. Quantum attacks on classical cryptography 2. Building cryptography resilient to quantum attacks 3. Protocols that use quantum resources, such as quantum key distribution, copy-protection and quantum money 4. Interactive proofs with quantum devices No prior background in quantum information/quantum physics/mechanics or in cryptography will be assumed, although students are expected to be familiar with basic concepts in the theory of computation (P vs NP, Turing Machines). Additional information will be available on the course website: http://www.dakshitakhurana.com/quantum-cryptography.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.
43732	Online	DLH	ARRANGED	n.a.	n.a.	Sun, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Deep Lrng for Healthcare Section Info: This course covers deep learning (DL) methods, healthcare data, and applications using DL methods. The courses include activities such as video lectures, self-guided programming labs, homework assignments (both written and programming), and a large project. You are expected to learn deep learning models such as deep neural networks, convolutional neural networks, recurrent neural networks, autoencoder, attention models, graph neural networks and deep generative learning. You will also get a chance to learn 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. Besides learning DL algorithms, the course will focus on hands-on experiences for data scientists and machine learning engineers to implement various practical healthcare models on diverse medical data. You will learn popular deep learning frameworks like PyTorch, and data science software like Jupyter Notebook. Basic machine learning will be helpful but not strictly required. You should have good programming skills in Python and good understanding in linear algebra and calculus. You should also have sufficient system knowledge such as using Linux and setting up programming environments on the cloud. This section is for "on campus" students. This course will be taught on the coursera platform. Students taking CS courses on the Coursera platform for the first time must take additional steps to correctly setup their Coursera account and complete a brief onboarding course to gain access to the course. Students who enroll in this course must read “Instructions to access CS courses delivered on Coursera platform” available at http://go.cs.illinois.edu/CSregister, failure to follow these instructions will result in late course access. 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.
65866	Online	DSO	ARRANGED	n.a.	n.a.	Park, T	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Advanced Bayesian Modeling Section Info: This course is only for students that are in the Computer Science Online MCS Program. ProctorU fees may apply. Restricted to Graduate - Urbana-Champaign. Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC. 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. Not intended for First Time Freshman students.
31662	Lecture-Discussion	EKS	3:00PM -4:15PM	TR	1111 Siebel Center for Comp Sci	Soltanaghai, E	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Smart cities, homes, & beyond Section Info: This course explores the principles and practice of smart physical places and things (Smart). 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. 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, paper critiques, and overview of the commercial landscapes for the topics covered in class. 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.
41496	Lecture-Discussion	EVS	9:30AM -10:45AM	TR	1302 Siebel Center for Comp Sci	Solomonik, E	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Tensor Computations Section Info: The applications and numerical methods for problems involving tensors have grown widely in recent years. Tensor contractions and decompositions are prevalent in scientific computing (especially in computational chemistry and physics) as well as data mining and machine learning. This course will go into depth on core fundamentals in numerical linear algebra and numerical optimization relevant to tensors. We will introduce diagrammatic notation to study tensor networks and tensor decomposition algorithms. Further, the course will provide algebraic formulations of graph and combinatorial algorithms using sparse matrices and tensors. The use of tensors for algorithmic analysis will also be studied, including bilinear algorithms for matrix multiplication and convolution. Going beyond computational complexity, the course will analyze algorithms in terms of arithmetic intensity, parallelism, and communication cost. Prerequisites: familiarity with parallel programming, numerical linear algebra, and algorithms (e.g. CS 420, 450, 473). 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.
40099	Lecture-Discussion	FTD	12:30PM -1:45PM	WF	1109 Siebel Center for Comp Sci	Ren, L	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Fault-Tolerant Dist Algorithms Section Info: Fault-tolerant distributed computing is the paradigm where multiple computing nodes or participants jointly carry out a computational task to avoid any single point of failure or trusted central authority. A fault-tolerant distributed algorithm is expected to deliver its intended results or guarantees despite that a number of participants experience failures or behave maliciously. This course covers classic results and recent advances in fault-tolerant distributed algorithms. The course studies important problems in distributed algorithms and cryptography, including broadcast, agreement, state machine replication, blockchains, shared registers, and threshold cryptography; different models and assumptions regarding shared memory vs. message passing, message delivery and delay, fault pattern, cryptography, and setup; optimal fault-tolerant thresholds, efficiency bounds, and impossibility results for different problems under various combinations of settings; common building blocks and algorithm design techniques including cryptographic primitives, leader-based approach, randomization, quorum systems; influential and state-of-the-art algorithms such as Paxos, PBFT, Nakamoto's Bitcoin protocol. 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.
43773	Lecture-Discussion	GUI	2:00PM -3:15PM	TR	0216 Siebel Center for Comp Sci	Gui, L	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Efficient & Predictive Vision Section Info: Much of existing work in computer vision has focused on learning models of high accuracy. However, in real-world, resource-constrained scenarios, such as AR/VR, autonomous driving, and robots, being able to reduce latency and predict the environment’s dynamics is equally important. This advanced graduate course will cover foundation principles and recent progress of learning efficient and predictive models and their applications in domains such as vision, robotics, and NLP. We will investigate state-of-the-art approaches and a wide range of recent research topics, such as improving trade-off between accuracy and efficiency, predicting human motion/video/trajectory, anticipating action/event/intention, etc., in single- and multi-agent settings, and with multi-modal data. We will cover both the theoretical foundations and the techniques to build such practical systems, e.g., model compression, knowledge distillation, sequential modelling, predictive learning, multi-modal learning, etc. The goal of this course is to give students the background and skills necessary to perform research in computer vision and its application domains. Students will read and present recent papers and perform a final research project. Prerequisites: Familiar with computer vision and machine learning. If you have not taken courses covering this material, consult with the 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. Not intended for First Time Freshman students.
39664	Lecture-Discussion	HJ	2:00PM -3:15PM	WF	1103 Siebel Center for Comp Sci	Ji, H	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Knowledge-driven Nat Lang Gen Section Info: In this course we will teach machines to describe knowledge they have learned from data. We will develop a set of intelligent systems which can transform structured knowledge bases into natural language, which is an opposite direction of Information Extraction. The topics will cover both of the conventional template filling based approaches and modern neural networks based generation approaches. We will dive deep into various technical components: how to represent knowledge, how to feed knowledge into a generation model, how to evaluate generation results? We will do three project-based assignments and a final term project interesting applications including: 1. News image and video caption generation to describe entities and events 2. Generate scripts for news videos 3. Generate scientific ideas and write technical papers 4. Write technical reviews from various perspectives 5. Write a bio for all kinds of professionals 6. Write a news article about scientific discovery results and perspectives about events 7. Write a history book 8. Make Alexa smarter by feeding information from background and real-time news streams 9. Or the other way around: Generate paitings, news videos and yoga instructional videos This is an advanced graduate-level course, and the prerequisites include Natural Language Processing and Machine Learning course. 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.
43810	Lecture-Discussion	JBR	12:30PM -1:45PM	TR	1302 Siebel Center for Comp Sci	Jabbarvand Behrouz, R	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: ML for Software Engineering Section Info: Objectives: The purpose of this course is to help students explore and understand the applications of machine learning to solve real-world software engineering problems. Students will become familiar and obtain knowledge about (1) fundamentals and advanced topics in software engineering as well as (2) how machine learning and data mining techniques can be used at different stages of software development to ensure quality and reliability of software Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
65118	Lecture-Discussion	LCE	5:00PM -6:15PM	TR	1109 Siebel Center for Comp Sci	Adve, V	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Lang & Compilers for Edge Comp Section Info: This course will be a literature review of recent papers on compilers and optimization techniques for resource-constrained heterogeneous parallel systems used in edge computing. Such systems increasingly use specialized, domain-specific accelerators to deliver the necessary performance and energy efficiency under tight resource constraints. We will begin with a brief discussion of example domains important in edge computing applications, e.g., computer vision, image processing, speech processing, SLAM, and autonomous control systems. The course will then cover relevant papers in the areas of compilers, optimization techniques, and accuracy-performance-energy tradeoffs for such systems. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
62819	Online	LHO	ARRANGED	n.a.	n.a.	Sun, J	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Deep Learning For Healthcare Section Info: This course covers deep learning (DL) methods, healthcare data, and applications using DL methods. The courses include activities such as video lectures, self-guided programming labs, homework assignments (both written and programming), and a large project. You are expected to learn deep learning models such as deep neural networks, convolutional neural networks, recurrent neural networks, autoencoder, attention models, graph neural networks and deep generative learning. You will also get a chance to learn 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. Besides learning DL algorithms, the course will focus on hands-on experiences for data scientists and machine learning engineers to implement various practical healthcare models on diverse medical data. You will learn popular deep learning frameworks like PyTorch, and data science software like Jupyter Notebook. Basic machine learning will be helpful but not strictly required. You should have good programming skills in Python and good understanding in linear algebra and calculus. You should also have sufficient system knowledge such as using Linux and setting up programming environments on the cloud. This course is only for students that are in the Computer Science MCS-DS Program. Additional ProctorU fees may apply. Restriction(s): Restricted to MCS:Computer Sci Online -UIUC, MCS:Computer Sci Online -UIUC, or NDEG:Computer Science Onl-UIUC.
40659	Lecture-Discussion	LMZ	9:30AM -10:50AM	TR	1103 Siebel Center for Comp Sci	Zhang, L	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: ADV Software Test. & Debugging Section Info: The purpose of this course is to teach the principles and practices of software testing and debugging. We will together explore advanced testing and debugging techniques to detect, diagnose, localize, and fix software bugs for real-world software systems from various application domains. This course will not only expose students to the cutting-edge research of software testing and debugging, but will also encourage students to explore the bidirectional synergy between software testing/debugging and other research areas, such as formal methods (FM), programming languages (PL), machine learning (ML), and security. This is a research-oriented seminar course with a major course project, including topics on: Guided random test generation Symbolic execution Specification-based test generation Fuzz testing Oracle inference Human-assisted bug detection Regression testing Failure analysis and cause reduction Fault localization Automated program repair Unified debugging Testing and debugging for non-traditional application domains (e.g., flaky tests, as well as ML, FM, and DB systems) 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.
69011	Lecture-Discussion	MEB	3:30PM -4:45PM	TR	1302 Siebel Center for Comp Sci	El-Kebir, M	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Computational Cancer Genomics Section Info: Title: Computational cancer genomics This course focuses on recent algorithmic methods in cancer genomics, including somatic variant calling, phylogeny inference and identification of driver mutations. Students will study the underlying principles of these methods and the application of these methods to cancer genomics data. This course is appropriate for graduate students in computer science, bioengineering, mathematics and statistics. Familiarity with basic statistics, probability and algorithms 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 First Time Freshman students.
65117	Lecture-Discussion	RK	3:30PM -4:45PM	WF	0216 Siebel Center for Comp Sci	Kumar, R	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: HCI for ML Section Info: Explores the use of data-driven methods to support creative design processes by examining recent work in human computer-interaction, product design, cognitive science, machine learning, graphics, vision, and natural language processing. Students will read and discuss recent papers from these fields, and work in teams on a multi-week project to build data-driven tools to solve real-world design problems. Practical data mining and machine learning knowledge is emphasized: crowdsourcing and web scraping, model and feature selection, parameter tuning. The course has no formal prerequisites, but students should be algorithmically and programmatically mature. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
68083	Lecture-Discussion	SC	3:30PM -4:45PM	TR	1109 Siebel Center for Comp Sci	Chandrasekharan, E	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Social Computing 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 HCI, NLP, and human-centered AI, we will learn to understand, build, and evaluate social computing systems. Through this course, students will read and critique high-impact research papers, lead and engage in class discussions, provide and receive constructive peer-feedback, and execute a new research project for their final paper. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.
31668	Lecture-Discussion	SG	2:00PM -3:20PM	TR	1109 Siebel Center for Comp Sci	Ghose, S	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Arch. for Mobile & Edge Comp. Section Info: Mobile computing platforms (e.g., smartphones, tablets, wearables, processors in autonomous vehicles) have enabled a revolutionary change in society over the last two decades. Much of this success is a result of a fundamental shift in architectural design, with new architectures focusing on lowering energy consumption, improving the handling of data, and specializing for target platforms. These architectures are continuing to transform, as the relationship between data centers, networks, and mobile platforms changes, and as applications continue to deal with increasing amounts of data. This course will cover key enabling technologies and current research challenges for mobile computer architectures. Topics include smartphone architectures and hardware components, memory and storage systems for mobile platforms, system-on-chip integration, emerging applications, and edge computing platforms. The course will be taught using a combination of lectures and paper readings, and students will be expected to present research papers and complete a substantial final project. It is strongly suggested that students take: CS 433 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 First Time Freshman students.
39666	Lecture-Discussion	TLR	11:00AM -12:15PM	TR	1109 Siebel Center for Comp Sci	Ringer, T	Part of Term: 1 Date Range: 01/18/22-05/04/22 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 here: https://dependenttyp.es/classes/598sp2022.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.
60198	Lecture-Discussion	XU	3:30PM -4:45PM	TR	2233 Everitt Laboratory	Xu, T	Part of Term: 1 Date Range: 01/18/22-05/04/22 Credit: 4 hours Section Title: Reliability of Cloud-Scale Sys Section Info: The purpose of this course is to teach the principles and practices of reliability engineering in modern "cloud-scale" systems, and expose students to the research of software and system reliability. We will look at how large-scale systems fail in the real world, and we will study the state-of-the-art reliability techniques and practices, including those widely adopted in industry and new ideas proposed by academia. We will be going over the following topics: * Availability * Hardware faults * Software defects * Misconfigurations * Operation mistakes * Network disruptions * Bug detection * Software testing * Failure testing and chaos engineering * Load tests and drain tests * Monitoring * Recovery * Tracing * Diagnosis This is a research-oriented seminar course with a major course project. Students will get familiar with the technical results as well as with the process of doing research in software testing, analysis, and analytics. The aim is to help students start research in this field or apply its results in their ongoing research. The course readings will include classic papers and current state-of-the-art work. Students will read papers ahead of time, participate in discussions, present at least one paper during the course, and do a research project in small teams or individually. Students will also write a paper describing their project and present their work at the end of the course. In past offerings of this course, a number of students submitted and published papers in various conferences or workshops based on their course projects. Grading will be based on participation, presentation, and project. The final project will contribute the most to the grades. Prerequisites: Students should have basic knowledge of software engineering and programming languages. If you are not sure whether you can attend this course, please consult the instructor. Restriction(s): Restricted to Graduate - Urbana-Champaign. Not intended for First Time Freshman students.