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M.Eng. in Operations Research and Information Engineering

The Master of Engineering (M.Eng.) in Operations Research and Information Engineering program is a full-time, professionally focused degree that enhances traditional math, science, and engineering skills while emphasizing their real-world application to practical challenges.

Format

In-Person

Page Contents

Note: This page provides a general overview. For complete and accurate information, please refer to our M.Eng. Handbook consult the M.Eng. Student Services Coordinator. For current course offerings and information, refer to the Cornell University Registrar: Courses of Study.

Degree Requirements

The general requirements for a M.Eng. degree in Operations Research and Information Engineering include:

  • A minimum of 30 credit hours of approved technical coursework, all taken for a letter grade, with the exception of the required colloquia, while enrolled in the M.Eng. program.
  • A minimum of 9 letter-graded school credit hours in approved technical engineering coursework (not including the M.Eng. project, colloquium/practicum).
  • An engineering design project for 5-8 credits, depending on the student’s concentration, resulting in a final presentation and written report.
  • An approved program of courses as outlined in the M.Eng. Student Handbook, satisfying the student’s concentration requirements. Each student is responsible for submitting a study plan of courses approved by their advisor, and carry a course load that enables them to complete the M.Eng. Program without unnecessary delay.
  • Minimum semester M.Eng. GPA of 2.50, and a minimum GPA of 2.50 across all school courses.
  • No grade below a C- in every graded course taken.

Within this general framework, students must fulfill Operation Research and Information Engineering core and concentration requirements. Core requirements consist of successfully completing 12 or more letter-graded credit hours among the courses, including:

  • 8 or more credit hours in school courses;
  • 3 or more credit hours in each of the following categories:
    • Optimization Modeling
    • Stochastic Modeling
    • Data Science and Statistical Modeling

NOTE: Additional course requirements may apply depending on the student’s concentration.

Prerequisites

Before beginning the M.Eng. program in Operation Research and Information Engineering, all M.Eng. students must provide certificate or transcript verification that they have successfully completed the following coursework at a degree granting institution:

1. A standard engineering calculus sequence, including linear algebra (with eigenvalues and eigenvectors), and vector calculus, similar in content and rigor to Cornell’s MATH 1910, MATH 1920, and MATH 2940.

2. An introductory engineering probability and statistics course that covers theoretical fundamentals and is similar in content and rigor to Cornell’s ENGRD 2700.

3. An intermediate-level computer programming course in a general programming language such as C++, Java, or Python, similar in content and rigor to Cornell’s ENGRD 2110. Courses that entail programming applications, but where programming is not the primary focus are not acceptable substitutes. Courses in statistical modeling languages, such as R and SAS, are not acceptable substitutes by themselves.

ENGRD 2700 and ENGRD 2110 are offered each semester and during the summer at Cornell. Information is available at the Summer Session Office, B20 Day Hall, (607) 255-4987, or online. Prerequisite coursework completed more than five years prior to the start of the M.Eng. program must be retaken or reinforced in an approved manner.

Please note that the certain concentrations have additional prerequisites. Failure to satisfy program prerequisites will jeopardize a student’s academic standing at Cornell and may result in a mandatory concentration change or (in extreme cases) a leave of absence until all prerequisites are met.

Core Courses: Optimization Modeling

  • ORIE 5300

    Optimization I (Fall, 4 credit)

  • ORIE 5310

    Optimization II (Spring, 4 credit)

  • ORIE 5370

    Optimization Modeling in Finance (Spring, 3 credit)

  • ORIE 5570

    Reinforcement Learning with Operations Research Applications (Spring, 3 credit)

  • ORIE 5126

    Principles of Supply Chain Management (Spring, 3 credit)

  • ORIE 5330 (last offered FA23)

    Discrete Models (Fall, 4 credit)

  • ORIE 5340

    Applications of Opt: Modeling and Computation (Fall, 4 credit)

  • ORIE 5350

    Intro to Game Theory (Fall, 4 credit)

  • CS 5223

    Numerical Analysis: Linear and Nonlinear Problems (Spring, 4 credit)

  • ECE 5280

    Optimal System Analysis and Design (Fall, 4 credit)

  • SYSEN 6800

    Computational Optimization (Fall, 4 credit)

Core Courses: Stochastic Modeling

  • ORIE 5100

    Manufacturing Systems Design: A Consulting Boot Camp (Fall, 4 credit)

  • ORIE 5500†

    Engineering Prob and Stat II (Fall, 4 credit)

  • ORIE 5510†††

    Intro to Engineering Stochastic Processes I (Spring, 4 credit)

  • ORIE 5580

    Simulation Modeling and Analysis (Fall, 4 credit)

  • ORIE 5581

    Monte Carlo Simulation (Fall, 2 credit)

  • ORIE 5582

    Monte Carlo Methods in FE (Spring, 2 credit)

  • ORIE 5600

    Financial Engineering with Stochastic Calculus I (Fall, 4 credit)

  • ORIE 5570

    Reinforcement Learning with Operations Research Applications (Spring, 3 credit)

  • ORIE 5126

    Principles of Supply Chain Management (Spring, 4 credit)

  • ORIE 5130

    Service System Modeling and Design (Fall, 4 credit)

  • ORIE 5610

    Financial Engineering with Stochastic Calculus II
    (Spring, 4 credit)

  • ORIE 5630†

    OR Tools for Financial Engineering (Fall, 4 credit)

  • ORIE 5650

    Quantitative Methods of Financial Risk Mgmt (Fall, 3 credit)

  • ECE 5110

    Random Signals in Comm. and Signal Processing (Fall, 4 credit)

  • STSCI 5500

    Engineering Probability and Statistics II (Fall, 4 credit)

† DA/FE: Not Permitted
†† FE: Only permitted for Stochastic Core
††† FE: Only permitted as General Credit

Core Course: Data Science and Statistical Modeling

  • ORIE 5550

    Applied Time Series Analysis (Spring, 4 credit)

  • ORIE 5630††

    OR Tools for Financial Engineering (Fall, 4 credit)

  • ORIE 5640

    Stats for Financial Engineering (Spring, 4 credit)

  • ORIE 5740††

    Statistical Data Mining I (Spring, 4 credit)

  • ORIE 5741

    Learning with Big Messy Data (Spring, 4 credit)

  • ORIE 5742

    Info Theory, Probabilistic Modeling, and Deep Learning with Scientific and Financial Apps (S 3 cr)

  • SYSEN 6880***

    Industrial Big Data Analytics & Machine Learning (Spring, 4 credit)

  • SYSEN 6888*

    Deep Learning (Spring, 4 credit)

  • CS 5780***

    Intro to Machine Learning (Fall, Spring 4 credit)

  • CS5782*

    Deep Learning (Spring, 4 credit)

  • CS 5789

    Introduction to Reinforcement Learning (Fall, 3 credit)

  • ECE 5420***

    Fundamentals of Machine Learning (Spring, 4 credit)

  • STSCI 5030

    Linear Models with Matrices (Fall, 4 credit)

  • STSCI 5090†

    Theory of Statistics (Fall, Spring 4 credit)

  • STSCI 5740**

    Data Mining and Machine Learning (Fall, 4 credit)

* Choose 1 of these 2 classes
** Choose 1 of these 2 classes
*** Choose 1 of these 3 classes
† DA/FE: Not Permitted
†† FE: Only permitted for Stochastic Core

Eligibility to Waive One Core Area

Students who have previously completed six or more letter-graded credits of school core course work in a particular area (including undergraduate-level courses that co-meet with those listed), three or more of which must be in school courses (e.g., a Cornell undergraduate who has already taken the ORIE 3300/3310 sequence) may elect to waive that area. A student may waive at most one of the school core areas and must indicate on his or her study plan the previously completed courses being used to satisfy the waiver requirement. (The credit hours from these previously taken courses do not count as credit hours towards the M.Eng. degree.) Students opting to waive an school core area must still complete 8 or more letter-graded credit hours among the allowed courses in the core category lists, including:

• 6 or more credit hours in school courses; and
• 3 or more credit hours in each of the two non-waived categories.

In addition, students opting to waive an school Core area are still subject to all of the credit hour requirements and all of the concentration requirements.

  • Earn 1 colloquium credit with a passing grade (S). Students in the Financial Engineering concentration must complete ORIE 5210 during the Cornell Financial Engineering Manhattan semester. All other Operation Research and Information Engineering M.Eng. students must complete ORIE 9100 during the spring semester (or an approved substitute).
  • Earn 1 career practicum credit with a passing grade (S). Students in the Financial Engineering concentration must complete ORIE 5215 during the first fall semester. All other Operation Research Information Engineering M.Eng. students must complete ORIE 5915 during the fall semester. Spring admits to the Financial Engineering concentration who did not take ORIE 5215 during the previous fall semester must work with the M.Eng. director and the Cornell Financial Engineering Manhattan director to devise a suitable plan to satisfy this requirement.
  • Successfully complete the project preparation course ORIE 5110 or ORIE 5100 during the fall semester. Exception: This requirement is waived for students in the Financial Engineering concentration.
  • Resolve any incomplete course grade within one semester of the submission of the incomplete.

M.Eng. Design Project

Each Operations Research and Information Engineering M.Eng. student must complete an approved team-based engineering design project. M.Eng. projects typically have industrial, financial, or government organizations as clients and/or sponsors. The format and timeline for M.Eng. projects varies by concentration or minor, as does the manner in which students are assigned to project teams. The specific goals and expectations for M.Eng. projects will be presented prior to team assignment. In all cases, a final written report must be submitted and signed by the faculty advisor, and a final oral presentation must be made to the client organization before the project requirement is considered fulfilled.

Students in the Strategic Operations concentration undertake team-based project work as part of the practicum and do not enroll in a separate project course to satisfy the M.Eng. project requirement.

In all other cases, students should enroll in project courses each semester according to the concentration information below. A final written report must be submitted and signed by the faculty project advisors, and a final oral presentation must be made to the partner organization. Full commitment, participation, and teamwork are expected of all students.

Concentration:

  • Applied Operations Research, Information Technology, or Data Analytics ; ORIE 5980 – 1 credit (Fall)  ORIE 5981 – 4 or 5 credits (Spring)
  • Financial Engineering; ORIE 5220 – 5 credits (Fall)
  • Manufacturing and Industrial Engineering; ORIE 5910 – 5 credits (total across two terms – Fall) ORIE 5911 – 5 credits (total across two terms – Spring)
  • Systems Engineering minor; SYSEN 5900 – 3 credits (Fall) SYSEN 5900 – 3 credits (Spring)

Concentrations

  • Applied Operations Research

    The Applied Operations Research concentration is the most general of the concentrations and allows the most flexibility with respect to elective courses. The concentration is most appropriate for students with undergraduate degrees in Operations Research and Information Engineering who want to increase the depth and breadth of their exposure to operations research and its applications, and for those with undergraduate degrees in other fields who want to gain a solid foundation in the theory and practice of operations research.

  • Data Analytics

    The Data Analytics concentration focuses on the theory and tools needed to make fact-based, data-driven decisions associated with the development, pricing, promotion, and distribution of ideas, goods, and services. The required course work for this concentration consists of three complementary areas that are collectively essential for effective data analysis.

    In addition to satisfying the core requirements, students  must complete three or more approved elective courses for at least 9 credit hours in total. The three courses must be comprised of one or more additional courses from the Data Science and Statistical Modeling category, and one or more courses from the Data Analytics Electives list, with no more than 3 credit hours from Cornell SC Johnson College of Business (or equivalent) courses. This 13 structure is designed to ensure that students gain a solid foundation that spans statistical theory, data technology, and data-driven analysis and strategy.

  • Financial Engineering

    The Financial Engineering concentration prepares students for careers that involve the quantitative analysis and management of financial instruments and risk. Such jobs frequently involve:

    (1) mathematical modeling and analysis of stocks, bonds, options, currency exchange rates, and other structured products

    (2) developing quantitative models to help corporations understand and manage their exposure to risk, and/or

    (3) implementing algorithms to monitor, price, and/or trade financial instruments. Unlike other concentrations, Financial Engineering is specifically designed to be a three-semester program (Fall-Spring-Fall), with the third (i.e., second fall) semester taking place at Cornell Financial Engineering Manhattan in New York City.

  • Information Technology

    The Information Technology concentration prepares students to participate in the development, acquisition, and integration of information systems (particularly those embodying operation research approaches) to ensure that strategic business needs are satisfied. Students who elect this concentration will be introduced to the essentials of information technology and ways to bring it to bear in enterprise environments to assist real decision making.

  • Manufacturing and Industrial Engineering

    Students prepare to use their operations research skills to great effect in manufacturing environments. This concentration covers all aspects of the design, production, and distribution of goods and services, as well as the fundamentals of modern manufacturing technology, and the use of computers for design, analysis and management of manufacturing processes.

  • Strategic Operations

    The keystone of the Strategic Operations concentration (commonly called the Semester in Strategic Operations) is the strategic operations immersion offered by the Cornell SC Johnson College of Business. This intensive “supercourse” occupies the majority of the spring semester and provides a comprehensive treatment of how business and operations strategies are aligned and executed for success, including product design, logistics, quality control, corporate organization, employee organization and compensation, marketing, and globalization.

    Semester in Strategic Operations instruction is primarily project and case oriented, based more on interactive discussion than lecture. The course material is integrated with company site visits and team-based project work with industry partners. The Operations Research and Information Engineering M.Eng. project requirement is fulfilled within the context of the Semester in Strategic Operations framework.

Electives: Data Analytics

  • CS 5320

    Introduction to Database Systems (Fall, 3 credit)

  • CS 5670

    Introduction to Computer Vision (Spring, 4 credit)

  • CS 5700

    Foundations of Artificial Intelligence (Fall, 3 credit)

  • CS 5740

    Natural Language Processing (Fall, 4 credit)

  • ECE 5250

    Digital Signal Processing and Statistical Inference (Fall, 4 credit)

  • INFO 5100

    Visual Data Analytics for the Web (Fall, 3 credit)

  • INFO 5556

    Business Intelligence Systems (Fall, 4 credit)

  • NBA 6200

    Marketing Research (Spring, 3 credit)

  • NBA 6390

    Data-driven Marketing (Fall, 1.5 credit)

  • ORIE 5100

    Manufacturing Systems Design: A Consulting Boot Camp (Fall, 4 credit)

  • ORIE 5270

    Big Data Technologies (Spring, 2 credit)

  • ORIE 5570

    Reinforcement Learning with Operations Research Applications (Fall, 3 credit)

  • ORIE 5580

    Simulation Modeling and Analysis (Fall, 4 credit)

  • ORIE 5581

    Monte Carlo Simulation (Fall, 2 credit)

  • STSCI 5045

    Python Programming and its Applications in Statistics (Spring, 3 credit)

  • STSCI 5065

    Big Data Management and Analysis (Spring, 3 credit)

  • STSCI 5100

    Statistical Sampling (Fall, 4 credit)

  • STSCI 5160

    Categorical Data (Fall, 3 credit)

  • STSCI 5520

    Statistical Computing (Spring, 4 credit)

  • STSCI 5750

    Understanding Machine Learning (Spring, 4 credit)

Electives: Financial Engineering

  • NBA 5060/5090

    Financial Statement Analysis sequence (Fall, Spring 1.5 credit. Taken by itself, NBA 5060 does not qualify)

  • NBA 5090

    Advanced Financial Statement Analysis (Fall, Spring 1.5 credit)

  • NBA 5420

    Investment and Portfolio Management (Spring 1.5 credit)

  • NBA 5430

    Financial Markets and Institutions (Fall, 3 credit)

  • NBA 5540

    International Finance (Fall, 3 credit)

  • NBA 5550

    Fixed-Income Securities and Interest-Rate Options (Fall, 3 credit)

  • NBA 5600

    Demystifying Big Data and FinTech (Fall, 1.5 credit)

  • NBA 5980

    Behavioral Finance (Spring, 1.5 credit)

  • NBA 6060

    Evaluating Capital Investment Projects (Fall, 1.5 credit)

  • NBA 6560

    Valuation Principles (Spring, 1.5 credit)

  • NBA 6730

    Derivatives Securities, Part I (Fall, 1.5 credit)

  • NBA 6740

    Derivatives Securities, Part II (Fall, 1.5 credit)

  • ORIE 5230*

    Quantitative Trading Strategies (Fall, 2 credit)

  • ORIE 5240*

    Bond Math and Mortgage-Backed Securities (Fall, 2 credit)

  • ORIE 5252 thru ORIE 5257*

    Special Topics in FE I thru VI (Fall, 2 credit)

  • ORIE 5258

    Python for Finance (Fall, 1.5 credit) for first-year students in Ithaca only

  • ORIE 5259

    Market Microstructure & Algorithmic Trading: Theory and Practice (Spring, 1.5 credit)

  • ORIE 5610**

    Financial Engineering with Stochastic Calculus II (Spring, 4 credit)

  • ORIE 5650**

    Quantitative Methods of Financial Risk Mgmt (Fall, 3 credit)

* CFEM only
** Choose between Elective and Stochastic Core

Financial Data Science Certificate

Financial Engineering  concentrators interested in data science may complement their studies with the Financial Data Science Certificate by fulfilling the requirements listed below. All courses must be successfully completed for letter graded credit (audits and S/U are not acceptable). The framework is designed to enable students to add value to an organization immediately by cultivating critical skills in big data collection, manipulation, storage, and access, as well as machine learning theory, and algorithm implementation and evaluation.

Financial Data Science Certificate (all are required):

  • At least one of the following courses:
    • ORIE 5740 – Statistical Data Mining I (Spring, 4 credit)
    • ORIE 5741 – Learning with Big Messy Data (Spring, 4 credit)
    • CS 5780 – Intro to Machine Learning (Spring, Fall 4 credit)
    • CS 5786 – Machine Learning for Data Science
    • ECE 5420 – Fundamentals of Machine Learning (Spring, 4 credit)
    • STSCI 5740 – Data Mining and Machine Learning (Fall, 4 credit)
  • ORIE 5270 – Big Data Technologies (Spring, 2 credit)
  • Must complete an FDS-eligible Special Topics course (Fall 2 credit at CFEM)
  • Must complete an FDS-eligible project for ORIE 5220 (Fall 5 credit at CFEM)
  • Special Workshops (non-credit-bearing at CFEM)

Financial Data Science Certificate Courses

  • ORIE 5740

    Statistical Data Mining I (Spring, 4 credit)

  • ORIE 5741

    Learning with Big Messy Data (Spring, 4 credit)

  • CS 5780

    Intro to Machine Learning (Spring, Fall 4 credit)

  • ECE 5420

    Fundamentals of Machine Learning (Spring, 4 credit)

  • STSCI 5740

    Data Mining and Machine Learning (Spring, 4 credit)

  • ORIE 5270

    Big Data Technologies (Spring, 2 credit. Required for Certificate).