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The Master in Operations Research and Information Engineering (ORIE) curriculum will prepare you to harness data to make strategic business decisions.

You learn the fundamentals of operations research and master the computational tools you need to provide business intelligence on a large scale. In an educational innovation unique to Cornell Tech, you'll also participate in an immersive Studio experience, in which you'll develop your team-building and leadership skills while developing a new product idea in response to the strategic needs of a real organization and create your own startup.

 

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Technical Courses

Applied Machine Learning

CS 5785/ORIE 5750/ECE 5414 3.00

Learn and apply key concepts of modeling, analysis and validation from Machine Learning, Data Mining and Signal Processing to analyze and extract meaning from data. Implement algorithms and perform experiments on images, text, audio and mobile sensor measurements. Gain working knowledge of supervised and unsupervised techniques including classification, regression, clustering, feature selection, association rule mining, and dimensionality reduction.

HCI & Design

CS 5682/INFO 6410 3.00

Human-Computer Interaction (HCI) and design theory and techniques. Methods for designing, prototyping, and evaluating user interfaces to computing applications. Basics of visual design, graphic design, and interaction design. Understanding human capabilities, interface technology, interface design methods, prototyping tools, and interface evaluation tools and techniques.

Learning and Decision Making

CS 5726 3.00

This course covers the analysis of data for making decisions with applications to electronic commerce, AI and intelligent agents, business analytics, and personalized medicine. The focus will be on learning good and automated decision policies, inferring causal effects of potential decisions, and interactive and intelligent systems that learn through acting and act to learn. Topics include A/B testing, sequential decision making and bandits, decision theory, risk minimization and generalization, Markov decision processes, reinforcement learning, analysis of observational data, instrumental variable analysis, and algorithmic fairness of personalized decision policies. Students are expected to have taken a first course in machine learning and have working knowledge of calculus, probability, and linear algebra as well as a modern scripting language such as Python.

Modeling Under Uncertainty

ORIE 5530 3.00

In this course, we will learn how to model randomness, analyze its impact and make optimal decisions when it is present. We will cover stochastic modeling techniques, statistical principles, simulation, and decision-making under uncertainty. Using applications, we will demonstrate how we can use statistical principles to gain insight from data generated by systems with randomness. We will use simulation models to assess the performance of such systems and gauge how it changes in response to our decisions. We will introduce and use stochastic modeling techniques, such as Markov chains and Brownian motion, to build models of random phenomena and use these to gain understanding and guide decisions. As well as covering theoretical concepts, the course will put substantial emphasis in computational implementation of both simulation and decision-making problems.

Natural Language Processing

CS 5740 3.00

This course constitutes an introduction to natural language processing (NLP), the goal of which is to enable computers to use human languages as input, output, or both. NLP is at the heart of many of today’s most exciting technological achievements, including machine translation, automatic conversational assistants and Internet search. Possible topics include summarization, machine translation, sentiment analysis and information extraction as well as methods for handling the underlying phenomena (e.g., syntactic analysis, word sense disambiguation, and discourse analysis).

Optimization Methods

ORIE 5380/CS 5727 3.00

This course covers algorithmic and computational tools for solving optimization problems with the goal of providing decision-support for business intelligence. We will cover the fundamentals of linear, integer and nonlinear optimization. We will emphasize optimization as a large-scale computational tool, and show how to link programming languages, such as Python, Java and C++, with optimization software, such as Gurobi and CPLEX, to develop industrial-strength decision-support systems. We will demonstrate how to incorporate uncertainty into optimization problems. Throughout the course, we will cover a variety of modern applications and show how to deploy large-scale optimization models.

Parallel and Distributed Computing

CS 5460 3.00

This course is an introduction to parallel and distributed computing systems. Topics include models, organization, algorithms and libraries for parallel and distributed computing systems.

Service Systems and Online Markets

ORIE 5132 3.00

This course covers online and physical service systems with a focus on designing and managing them. When designing service systems, we need to determine the sales channels to use when offering services, design incentive mechanisms and make tactical capacity decisions. When operating service systems, we need to make rea-time pricing decisions, allocate capacity between different needs, make product recommendations, and forecast demand and customer behavior. Depending on the application setting, the capacity we manage can be physical, such as seats on an airplane to be allocated to passengers with different willingness to pay amounts, or digital, such as visitors on a webpage to be allocated between advertisers. We will cover ideas from revenue management, experimentation for demand learning, auctions, mechanism design and network theory.

Studio & Interdisciplinary Courses

Becoming a Leader in the Digital World

TECH 5000 1.00

In each class, students focus on building skills needed for effective entrepreneurial leadership in a digital world and build on understanding how to maximize the positive economic, social, and cultural impact of digital businesses and products.

Big Data in Marketing

NBAY 6030 1.00

This course is concerned with the new institutions that support marketing. The study of these institutions is important for several reasons. First, marketing managers need to decide who to partner with, and typically that means that they have to assemble what the industry calls a “stack” of marketing technology and advertising technology partners and suppliers. Second, the entrepreneurs who build these institutions need to know how they fit together in an ecosystem, how they collaborate and how they compete. Third there is a tension between the largest of these institutions, platforms like Google, Amazon, and Facebook, that offer themselves as fully integrated systems for the practice of data-driven marketing, and the rest of the institutional space, which contains large specialized firms such as Salesforce.com and smaller niche specialists. Marketers and investors should have a point of view on whether, or to what extent, the future lies with the giants of big data, sometimes called walled gardens, or the more open system of data flows among the niches. In sum, the goal of the course is to introduce the institutions within which students of today will make their careers. Data science is being deployed within institutional settings, and it is vital to know how the system of firms works together.

Business Fundamentals

NBAY 5500 1.00

This course is an introduction to fundamental concepts in business management – strategy, finance and financial accounting, marketing, organizational design, operations management, and negotiations – that are crucial knowledge for any entrepreneur and/or product manager. The course will help you learn the ‘language of business,’ and prepare you to take business electives in the near term and to run your own firm or product unit in the not-too-distant future, after you graduate from Cornell Tech. Business Fundamentals is not just economics, psychology, sociology, or mathematics, but draws from all of these disciplines. As a result, some of the concepts may sound familiar to some of you, but we will focus on understanding how they are applied to real-world business problems. In order to do so, we will use business cases and exercises in addition to lectures. Analyzing a case study and the resulting deductive learning will help you think in a different way, and will teach you to be comfortable with ambiguity, uncertainty, and contingencies, which are inevitable realities of business life. The general structure of the course is to introduce core concepts in each business area – strategy, finance/accounting, marketing, organizational design, operations management, and negotiations – through a lecture and a reading and then to apply those concepts in analyzing a custom-written case that draws from current business news.

Design Thinking

NBAY 5180 1.00

This hands-on course will prepare you to be future innovators by teaching you Design Thinking, the human-centered design methodology pioneered by IDEO and Stanford d.school founder, David Kelley. You will work on a team with peers from other disciplines so as to experience the importance of “radical collaboration.” All teams will work on the same challenge, and you will be asked to design an innovative solution to this complex problem.

Digital Marketing

NBAY 6090 1.50

This course introduces students to fundamental concepts in digital marketing and prepares them for roles as a marketer, entrepreneur or product manager. Students will be exposed to an overview of the major players in the advertising and digital industries, as well as a variety of tools commonly found in start-ups and technology firms. Course material will be covered with a mixture of case studies, lectures, and guest speakers.

Law for Non-Lawyers (for non-LLM students)

LAW 6673 1.00

This class introduces the principal legal issues involved in starting, managing and operating a technology-oriented business by entrepreneurs. It is intended to provide non-law students with an understanding of many of the laws and regulations to which developing businesses in the United States tech sector are typically subject—from the time an entrepreneur conceives and begins to build a business, implements a business plan, and obtains financing, to when she begins operations in anticipation of managing a mature company and considering possible exit strategies. The instructor, a former corporate partner in a large New York City law firm, will adopt the role of a general counsel to a start-up company advising his client/students about how laws and regulations affect their businesses at various stages of development, as well as about typical key contractual terms and negotiating strategies. Practicing lawyers will serve as guest lecturers. The course is designed to impart an understanding not only about substantive areas of the law that intersect with tech businesses but also about the role that lawyers should—and should not—play in burgeoning business enterprises. Students will gain insights into how lawyers approach business problems and the benefits and limitations of such a perspective.

Product Management

CS 5093 1.00

This studio-based course helps students learn about and develop product management (PM) skills by putting those abilities immediately to use on their Startup Studio projects. In each session, students learn about a different aspect of product management, product design, or technology development, then practice applying it to their Startup Studio projects, working in the Studio with their project teams and with the help and critique of the practitioner instructors and sometimes visiting practitioners. By the end of the semester, students will have developed and practiced many of the fundamental product management skills required to develop new technology products, and their Startup Studio projects will have greatly benefited from the practice.

Product Studio (required)

CS 5999 3.00

In Product Studio you and a team of your classmates will respond to a “Company Challenge” by developing, testing, and presenting a new product or business idea. Previous challenges have been posed by organizations as diverse as the Robin Hood Foundation, Uber, Weight Watchers and Bloomberg.

Startup & Product Ideas

TECH 5100 1.00

This studio-based course helps students develop their ability to imagine, recognize, develop and improve startup ideas. In each class, students learn a different approach to product ideation or product critique, then practice that approach, working in many different teams — often with the advice of visiting entrepreneurs, VCs, domain experts, and other practitioners. Students invent and explore hundreds of startup ideas, and help each other evaluate and improve those ideas. By the end of the course, students self-organize into co-founding teams around specific startup ideas that they will pursue in Startup Studio the following semester.

Startup Studio

TECH 5910 3.00

In Startup Studio you and a team of your classmates will develop your own new product or startup idea. You’ll experience the entire process, from developing your idea, to prototyping and testing, to pitching to investors. You can even apply for a Startup Award that will provide funding and other support to help you turn your Startup Studio project to a real business.