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Concepts and methods of design of experiments for quality design, improvement and control. Simple comparative experiments, including concepts of randomization and blocking, and analysis of variance techniques; factorial and fractional factorial designs; Taguchi's concepts and methods; second-order designs; response surface methodology. Engineering applications and case studies. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: IE 300.

Operations research techniques applied to problems in manufacturing and distribution. Single and multi-stage lot sizing problems, scheduling and sequencing problems, and performance evaluation of manufacturing systems. 3 undergraduate hours. 3 or 4 graduate hours. Prerequisite: IE 310.

Teaches students both the core concepts and underlying mechanics of, step by step, message by message, bit for bit, exactly how trillions of dollars in notional value are automatically traded daily around the globe, whether it is stocks, bonds, options, futures, currencies, crypto, etc. High Frequency Trading will provide students with an exciting introduction both to the modern world of automated finance and to many exciting technologies that power it. Where does the "actual" real-time price of a particular asset come from at any point in time? How exactly is it being calculated and by who or what? Is there even a single price or are there multiple, and are any of those prices actually correct? Just how fast can modern traders process market data or execute trades and how do they accomplish this? 4 undergraduate hours. 4 graduate hours. Credit is not given toward graduation for: Credit is not given if student received credit in IE 498/IE 598 Electronic Trading or IE 498/IE 598 High Frequency Trading. Prerequisite: Should have an understanding of programming and data structures and be proficient in coding in at least one programming language (typically python, C/C++, java, javascript, etc). Students who have taken CS 225 would have the requisite knowledge, but it is not required students have taken this course.

Same as EPSY 456 and PSYC 456. See EPSY 456.

Finite dimensional convex optimization problems; characterization of optimal solutions; iterative algorithms for differentiable and nondifferentiable problems; distributed optimization algorithms; robust problems and solutions; applications of convex optimization models. Prerequisite: ECE 490 or IE 411; MATH 416; MATH 444.

This course will focus on statistical aspects analyzing network data. It will review illustrative problems relating to aggregation of information, decision-making, and inference tasks over various graphical models and networks. Prerequisite: MATH 412. ISE graduate students and students enrolled in the Master of Science in Advanced Analytics (MCAA) are eligible to take the course.

Presentation and discussion of significant developments in industrial engineering. Approved for S/U grading only. May be repeated.