Katie Jurewicz

Independent research for PhD dissertation requirement under direction of a member of the Graduate Faculty.Offered for variable credit, 1-15 credit hours, maximum of 30 credit hours.Prerequisite(s): Approval of major adviser and advisory committee.

Research and thesis for master's students.Offered for variable credit, 1-6 credit hours, maximum of 6 credit hours.Prerequisite(s): Approval of major adviser.

Special projects and independent study.Offered for variable credit, 1-6 credit hours, maximum of 6 credit hours.Prerequisite(s): Permission of instructor.

Study abroad experience led by CEAT faculty with a goal of developing a deeper understanding of cultural values and perspectives outside of the United States related to engineering, architecture, technology.Prerequisite(s): Sophomore standing and permission of the Associate Dean of Academics and the Study Abroad Office.

Problems of society relating to technology and added problems stemming from their solution. Minimal reliance on mathematics; for engineering and non-engineering students.Offered for variable credit, 1-3 credit hours, maximum of 9 credit hours.

The overall goal of this course is to provide an applied overview to statistical learning for real industrial engineering problems using R programming. Topics In this course cover advanced linear and non-linear methods of statistical learning such as multivariate regression, mixed-effects regression, advanced logit regression, clustering methods, generalized additive models, tree-based methods, support vector machines. and Bayesian methods. May not be used for degree credit with IEM 5783.Prerequisite(s): ENGR 1412 with grade of 'C' or better and IEM 3703 with grade of 'C' or better and IEM 4013 with grade of 'C' or better.

The overall goal of this course is to provide an applied overview to statistical learning for real industrial engineering problems using R programming. Topics In this course cover advanced linear and non-linear methods of statistical learning such as multivariate regression, mixed-effects regression, advanced logit regression, clustering methods, generalized additive models, tree-based methods, support vector machines. and Bayesian methods. May not be used for degree credit with IEM 4783.Prerequisite(s): IEM 5003 and IEM 5013.

Independent research for PhD dissertation requirement under direction of a member of the Graduate Faculty.Offered for variable credit, 1-15 credit hours, maximum of 30 credit hours.Prerequisite(s): Approval of major adviser and advisory committee.

Research and thesis for master's students.Offered for variable credit, 1-6 credit hours, maximum of 6 credit hours.Prerequisite(s): Approval of major adviser.

Independent research for PhD dissertation requirement under direction of a member of the Graduate Faculty.Offered for variable credit, 1-15 credit hours, maximum of 30 credit hours.Prerequisite(s): Approval of major adviser and advisory committee.

Design-focused course that introduces students to human factors engineering and human-centered design, provides an overview of human anatomy and psychology theories, how the human body and its limitations affect engineering design, and then discuss how human factors-driven designs lead to a reduction of human error in complex systems. Topics primarily cover cognitive human factors theories including visual detection, signal detection theory, multiple resource theory, memory and decision making, human error, multitasking, cognitive limitations and how to design displays, controls, automation and other complex systems based on users' cognitive abilities.Prerequisite(s): IEM 3813 with grade of 'C' or better.

Design-focused that introduces students to human factors engineering & human-centered design; provides an overview of human anatomy and psychological theories, how the human body & its limitations affect engineering design & then discuss how human factors-driven design lead to a reduction of human error in complex systems. Topics primarily cover cognitive human factors theories including visual detection, signal detection theory, multiple resource theory, memory & decision making, human error, multitasking, cognitive limitations & how to design displays, controls, automation, & other complex systems based on users' cognitive abilities.Prerequisite(s): IEM 3813 or equivalent.

Design-focused that introduces students to human factors engineering & human-centered design; provides an overview of human anatomy and psychological theories, how the human body & its limitations affect engineering design & then discuss how human factors-driven design lead to a reduction of human error in complex systems. Topics primarily cover cognitive human factors theories including visual detection, signal detection theory, multiple resource theory, memory & decision making, human error, multitasking, cognitive limitations & how to design displays, controls, automation, & other complex systems based on users' cognitive abilities.Prerequisite(s): IEM 3813 or equivalent.

Research and thesis for master's students.Offered for variable credit, 1-6 credit hours, maximum of 6 credit hours.Prerequisite(s): Approval of major adviser.

Independent research for PhD dissertation requirement under direction of a member of the Graduate Faculty.Offered for variable credit, 1-15 credit hours, maximum of 30 credit hours.Prerequisite(s): Approval of major adviser and advisory committee.

Professionally supervised experience in real life problem solving involving industrial projects for which the student assumes a degree of professional responsibility. Activities approved in advance by the instructor. May consist of full- or part-time engineering experience, on-campus or in industry, or both, either individually or as a responsible group member. Periodic reports both oral and written required as specified by the adviser.Offered for variable credit, 1-3 credit hours, maximum of 4 credit hours.Prerequisite(s): Consent of IEM adviser and satisfactory completion of at least 12 hours of IEM 3000- or IEM 4000-level courses.

The overall goal of this course is to provide an applied overview to statistical learning for real industrial engineering problems using R programming. Topics In this course cover advanced linear and non-linear methods of statistical learning such as multivariate regression, mixed-effects regression, advanced logit regression, clustering methods, generalized additive models, tree-based methods, support vector machines. and Bayesian methods. May not be used for degree credit with IEM 5783.Prerequisite(s): ENGR 1412 with grade of 'C' or better and IEM 3703 with grade of 'C' or better and IEM 4013 with grade of 'C' or better.

The overall goal of this course is to provide an applied overview to statistical learning for real industrial engineering problems using R programming. Topics In this course cover advanced linear and non-linear methods of statistical learning such as multivariate regression, mixed-effects regression, advanced logit regression, clustering methods, generalized additive models, tree-based methods, support vector machines. and Bayesian methods. May not be used for degree credit with IEM 4783.Prerequisite(s): IEM 5003 and IEM 5013.

The overall goal of this course is to provide an applied overview to statistical learning for real industrial engineering problems using R programming. Topics In this course cover advanced linear and non-linear methods of statistical learning such as multivariate regression, mixed-effects regression, advanced logit regression, clustering methods, generalized additive models, tree-based methods, support vector machines. and Bayesian methods. May not be used for degree credit with IEM 4783.Prerequisite(s): IEM 5003 and IEM 5013.

Independent research for PhD dissertation requirement under direction of a member of the Graduate Faculty.Offered for variable credit, 1-15 credit hours, maximum of 30 credit hours.Prerequisite(s): Approval of major adviser and advisory committee.

Research and thesis for master's students.Offered for variable credit, 1-6 credit hours, maximum of 6 credit hours.Prerequisite(s): Approval of major adviser.

Independent research for PhD dissertation requirement under direction of a member of the Graduate Faculty.Offered for variable credit, 1-15 credit hours, maximum of 30 credit hours.Prerequisite(s): Approval of major adviser and advisory committee.

Design-focused course that introduces students to human factors engineering and human-centered design, provides an overview of human anatomy and psychology theories, how the human body and its limitations affect engineering design, and then discuss how human factors-driven designs lead to a reduction of human error in complex systems. Topics primarily cover cognitive human factors theories including visual detection, signal detection theory, multiple resource theory, memory and decision making, human error, multitasking, cognitive limitations and how to design displays, controls, automation and other complex systems based on users' cognitive abilities.Prerequisite(s): IEM 3813 with grade of 'C' or better.

Design-focused that introduces students to human factors engineering & human-centered design; provides an overview of human anatomy and psychological theories, how the human body & its limitations affect engineering design & then discuss how human factors-driven design lead to a reduction of human error in complex systems. Topics primarily cover cognitive human factors theories including visual detection, signal detection theory, multiple resource theory, memory & decision making, human error, multitasking, cognitive limitations & how to design displays, controls, automation, & other complex systems based on users' cognitive abilities.Prerequisite(s): IEM 3813 or equivalent.

Introduces students to enterprise/production systems from the perspective of industrial engineering. As a part of this introduction, the basic concepts and issues involved in professional practice will be discussed. Useful analytical methods and practices for collecting and working with data will be presented. Additionally, modern applications of industrial engineering practices will be introduced. After completion of this class, students will have the ability to describe and apply various industrial engineering methods in the manufacturing and service industries.Prerequisite(s): ENGR 1111 with grade of 'C' or better and MATH 2144 with grade of 'C' or better.