Program Overview

Industrial Engineering and Management Sciences

The Industrial Engineering and Management Sciences department offers various programs, including undergraduate and graduate degrees.

Undergraduate Program

The undergraduate program in Industrial Engineering provides students with a comprehensive education in the field. The program includes:

• Industrial Engineering Major (BS Degree)
• Curriculum
  • Degree Requirements
• Advising and Mentoring
• Changing Majors to Industrial Engineering
• Student Organizations & Professional Societies
• Extracurricular Activities
• BSIE Goals & Outcomes
• Client Project Challenge
  • Submissions
  • Important Dates

PhD Program

The PhD program in Industrial Engineering is designed for students who wish to pursue advanced research in the field. The program includes:

• PhD in Industrial Engineering
• Prospective Graduate Students
  • Recent Alumni Placement
• Curriculum & Focus Areas
• PhD Admissions
  • Admission Criteria & Background
  • Application Procedures
  • Preparation for Graduate Study
  • Funding Information
  • Frequently Asked Questions
  • Applicant Information Weekend

Professional MS Programs

The department also offers Professional MS Programs for students who wish to pursue a master's degree in Industrial Engineering.

Courses

The department offers a variety of courses, including:

• IEMS 315: Stochastic Models
  • Prerequisites: IEMS 302, COMP_SCI 150, and ES_APPM 245; Co-requisite: IEMS 303
  • Description: Fundamental concepts of probability theory; modeling and analysis of systems having random dynamics, and in particular, queueing systems.
  • Learning Objectives:
    • Students will understand why the relative-frequency view of probability cannot serve as the basis of a successful theory, and the reasons for the definition of probability via the three axioms
    • Students will understand that "variance matters," namely, the need to account for stochasticity in the analysis of systems
    • Students will understand the practical need to model systems' dynamics using the Markov property
    • Students will be able to model systems as Markov chains (in discrete and continuous time)
    • Students will understand the concept of steady state, and how to compute it for Markov chains in discrete and continuous time
    • Students will study the basic principles of queueing theory, in particular, Little's law, PASTA, and the tradeoffs between efficiency (in terms of servers' utilization) and quality of service (in terms of waiting times in queue)
  • Topics:
    • Review of probability theory and fundamental limit theorems for sequences of random variables
    • General stochastic processes (definition and in practice)
    • Discrete-time Markov chains
    • The Poisson process
    • Continuous-time Markov chains
    • Introduction to queueing theory
  • Materials: Class notes are distributed

Research

The department is involved in various research areas, including:

• Applied Statistics & Statistical Learning
• Financial Engineering
• Healthcare Engineering
• Optimization
• Computational Social Science
• Logistics & Operations
• Stochastic Analysis & Simulation

People

The department consists of:

• Faculty
  • Core Faculty
  • Emeritus Faculty
  • Affiliated Faculty
  • Faculty Awards & Honors
• Advisory Board
• Graduate Students
  • PhD Graduates (2000 on)
  • PhD Students on the Job Market
• Staff
• Researchers
• Visitors
• Distinguished Alumni

News & Events

The department hosts various events, including:

• News
• All Events
• Department Seminars
• Center for Optimization and Statistical Learning Seminars
• Wasserstrom Lecture Series
• Newsletter