IE 598

Spring 2026 All Classes

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Credit: 1 TO 4 hours.

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

Approved for Letter and S/U grading. May be repeated in the same or separate terms if topics vary.

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IE 598 class schedule data for spring 2026
CRN	Type	Section	Time	Day	Location	Instructor	Section Details
66113	Lecture	AMR	8:00AM -9:20AM	TR	106B6 Engineering Hall	Kim, I	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Augmented and Mixed Reality Section Info: This course provides a comprehensive overview of AR/MR in healthcare by balancing theory, hands-on programming, and discussion of contemporary issues. The theoretical component traces the evolution of immersive simulation—from early Virtual Reality (VR) frameworks to today’s AI-enhanced Extended Reality (XR) systems. Students will critically examine how continued advances in computing (including Machine Learning and Digital Twin) are being integrated into AR/MR and how these technologies support human–AI collaboration in clinical contexts. On the practical side, students will develop core computational skills in AR/MR, including computer vision, spatial computing, user interaction design, and display technologies. Programming assignments will use Python for device-agnostic algorithms, alongside exposure to Unity engine (C#) and Xcode (Swift) for platform-specific development. Students will also be exposed to leading hardware, including the Magic Leap 2, Apple Vision Pro, and Ray-Ban Meta glasses, to observe real-world healthcare applications in action. The course concludes with a survey of emerging topics and debates—drawing on recent works presented at IEEE ISMAR and related venues—to prepare students to innovate at the intersection of simulation, AI, and immersive technology. Restriction(s): Restricted to Graduate - Urbana-Champaign.
70691	Lecture-Discussion	GTO	3:30PM -4:50PM	TR	209 David Kinley Hall	Garg, J	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Game Theory and Optimization Section Info: Prerequisites: IE 310 or equivalent; basic knowledge of optimization, probability, and linear algebra; mathematical maturity. This course explores topics at the intersection of game theory, economics, and optimization, with a focus on real-world applications such as matching medical residents to hospitals, allocating students to schools, assigning seats in courses, kidney exchange, public housing allocation, online ad auctions, and task allocation. We will study foundational concepts in game theory, mechanism design, and market-based resource allocation, including Nash equilibrium, correlated equilibrium, bargaining, Shapley value, core stability, and competitive equilibrium. These models naturally give rise to a variety of optimization problems, including linear, convex, non-convex, and complementarity formulations. A central emphasis will be on the computational and optimization techniques used to model and solve these problems effectively and efficiently. Restriction(s): Restricted to Graduate - Urbana-Champaign.
47494	Lecture	OU	2:00PM -3:20PM	TR	206 Transportation Building	Hanasusanto, G	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 4 hours Section Title: Optimization Under Uncertainty Section Info: Prerequisites: IE 411, IE 511, and MATH 464 or equivalent. Description: A wide variety of decision-making problems in engineering, science, and economics involve uncertain parameters whose values are unknown to the decision maker when the decisions are made. The underlying uncertainty of these problems may arise from incomplete data, measurement errors, or the inherently stochastic nature of the respective problems. Ignoring this uncertainty can lead to inferior solutions that perform poorly in practice. The goal of this course is to introduce optimization models and methodologies that address uncertainty-affected decision problems. The course will introduce fundamental techniques from stochastic programming, robust optimization, and distributionally robust optimization. The theory will be motivated through concrete examples from production planning, supply chain management, project management, portfolio selection, and machine learning. Restriction(s): Restricted to Graduate - Urbana-Champaign.