Cornell Financial Engineering Manhattan at Cornell Tech

Financial Engineering Concentration and Cornell Financial Engineering Manhattan (CFEM)

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CFEM Future of Finance Conference on September 22nd - You won't want to miss it!

Last year's conference was a smashing success and sold out weeks before the event! With the support of our sponsor, Rebellion Research, this event is shaping up to be even more impressive with new panels and content added to reflect the innovation in the financial industry.

From senior academics to seasoned Wall Streeters, Fintech founders, and AI experts, we are bringing together amazing talent to exchange ideas and share opinions. 

See the full agenda as it is being finalized.

Do not miss this unique event! Join us on September 22nd on the Cornell Tech campus in New York City.  Reserve your spot (capacity is limited, registration closes soon).

Fall 2023 Event Lineup (all CFEM Events)

8/18 FE Bootcamp I

8/29 CFEM Orientation

9/9 FE Bootcamp II

9/12 Alumni Happy Hour

9/22 Future of Finance Conference

9/26 CFEM and UBS AI & Data Research Seminars: Daniel Wu (Vanguard)

10/31 CFEM and UBS AI & Data Research Seminars: Achintya Gopal (Bloomberg)

11/21: CFEM and UBS AI & Data Research Seminars: Thom Li (NYU)

The new semester has begun, and our students are so excited to be back! CFEM Orientation, as well as Financial Engineering Bootcamps I and II, have kicked off the academic year in fashion. Read more in the latest news!

CFEM Testimonials

"CFEM is a community where you can not only gain solid academic experience, but also exchange state-of-the-art insights with outstanding practitioners. You will stand at the cutting edge of the industry." - Hubery Wang, MFE Class of 2019

"The MFE program will equip you with the tools and knowledge you need to succeed. You can tailor your coursework to a specific career path while also building knowledge in fundamental areas." - Sagar Mehta, MFE Class of 2020

Check out our latest brochure highlighting Cornell MFE over the past 10 years (see side menu under "Alumni Testimonials")!

Formally recognized as the Master in Engineering (MEng) with Financial Concentration in the School of Operations Research and Information Engineering (ORIE), Cornell MFE is a career-oriented and application-focused degree that takes you beyond textbook quantitative finance (Cornell MFE info). With its flexible curriculum that encourages the study of data science, optimization, analytics, and computing, in addition to a broad range of courses in finance, the program has a rich history of providing the relevant and practical coursework in line with the demands of the financial industry. Graduates of the program have a U.S. degree in an approved science, technology, engineering, or mathematics (STEM) field (see information on OPT STEM Extension from the Office of Global Learning). 

With Cornell MFE, your career is guaranteed to begin in the classroom (Apply to Cornell MFE).

CFEM Historical Timeline: 1989-Cornell faculty pioneers the discipline of Financial Engineering; 1995-Financial Engineering is formalized within ORIE; 2007-CFEM established in New York City; 2008-First class of Cornell MFEs graduated from CFEM; 2015-Added dedicated Career Development support; 2016-CFEM launches Financial Data Science Certificate; 2017-CFEM moves to newly-built Cornell Tech; 2018-Financial Data Science grows in popularity, career development becomes a requirement; 2019-Added Financial Data Science faculty and additional career counselor

Cornell MFE consists of (3) semesters (Fall-Spring-Fall) and allows for a summer internship after the first year. All students begin their studies on our scenic Ithaca campus, and they will complete their studies at Cornell Financial Engineering Manhattan in the heart of New York City.

CFEM Two Semesters in Ithaca and One Semester in NYC

For the price of one program, students have a diversity of settings to experience the full range of Cornell University.

Structured to offer a flexible curriculum, Cornell MFE allows students to focus on a career track of their choice. Some of the most popular career tracks include:

  • Trading
  • Quantitative Portfolio Management
  • Financial Data Science/Fintech
  • Financial Risk Management

CFEM Curriculum with four modules: Stochastic Modeling, Optimization Modeling, Data Science and Statistical Modeling, and Financial Applications

To complete the ORIE Core Requirements, students must take a certain minimum number of credit hours from three modeling and data science modules (see chart above). While the selection of qualifying courses in each module is broad, each course is strategically hand-picked across various departments to offer students the knowledge most sought after in the field of quantitative finance. In addition, students must take certain credit-hours from the Financial Applications module for completion of the financial engineering concentration (MFE). Financial Applications Module includes courses from the Johnson Graduate School of Management and Cornell Financial Engineering Manhattan (CFEM).

CFEM, established in 2007 as a satellite New York City campus for Cornell MFEs, serves to connect our students with alumni and other practitioners working in the field of quantitative finance. 

Most of the CFEM coursework is taught by practitioners. Our practitioner lecturers work in the same field as the courses they teach (see full list of faculty). CFEM courses change year to year in response to the fast-paced needs of the financial industry. For specific qualifying courses, please see the ORIE MEng Handbook (pages 6-7 for ORIE Core and pages 11-12 for Financial Applications). Please email us for a copy of the handbook.

The Financial Data Science Certificate (FDSC) is integrated in the curriculum and is designed specifically for students who are interested in deepening their knowledge of machine learning and data science applications. FDSC coursework equips students with the knowledge that brings immediate value to an organization. Upon completion, students will have solid backgrounds in each of the following:

Theory: Understand the value-added and potential uses of data science in finance  

Data: Collect/scrape data and create data environment  

Application: Apply algorithms and extract insight

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