The Department of Operations Research and Financial Engineering (ORFE) at Princeton University offers a comprehensive and cutting-edge educational experience. This Princeton Course Guide provides essential information for prospective and current undergraduate students, detailing the academic program, faculty, research opportunities, and career pathways within ORFE. Whether you’re considering applying or are already enrolled, this guide will help you navigate your academic journey in this dynamic field.
Key Contacts for Princeton ORFE Course Guidance
Navigating your academic path is easier with the right contacts. Here are the key individuals and offices within the Princeton ORFE department to assist you:
Department Leadership
For overarching departmental matters and strategic direction:
Department Chair
Reach out to the Department Chair for insights into the department’s vision and broader academic inquiries.
Undergraduate Academic Support
For all undergraduate-related academic and administrative questions, the Undergraduate Office is your primary resource:
Director of Undergraduate Studies
The Director of Undergraduate Studies offers guidance on curriculum, course selection, and academic planning within the ORFE undergraduate program.
Undergraduate Administrator
The Undergraduate Administrator handles administrative tasks, forms, and logistical support for undergraduate students.
Academic Advisers for ORFE Undergraduates
Personalized academic advising is a cornerstone of the Princeton ORFE experience. Each class year is assigned dedicated faculty advisers, categorized by student last name, ensuring tailored support:
Class of ’25
| Student | Adviser |
|---|---|
| A-E | Prof. Boris Hanin |
| F-K | Prof. Ludovic Tangpi |
| L-R | Prof. Matias Cattaneo |
| S-Z | Prof. Ronnie Sircar |
Class of ’26
| Student | Adviser |
|---|---|
| A-H | Prof. Liza Rebrova |
| I-M | Prof. Bartolomeo Stellato |
| N-S | Prof. Emma Hubert |
| T-Z | Prof. Daniel Rigobon |
Class of ’27
| Student | Adviser |
|---|---|
| A-E | Prof. Jason Klusowski |
| F-L | Prof. Alain Kornhauser |
| M-R | Prof. Jianqing Fan |
| S-Z | Prof. Ioannis Akrotirianakis |
Note: All ORFE department offices are located in Sherrerd Hall, except for Prof. Ramon van Handel, whose office is in Fine Hall.
Introduction to Princeton’s Department of Operations Research and Financial Engineering
The Princeton University Department of Operations Research and Financial Engineering (ORFE) stands at the forefront of quantitative education for the modern world. In an era defined by vast data and complex challenges, ORFE equips students with the analytical tools and mathematical modeling skills necessary to make impactful decisions and develop innovative solutions across diverse sectors.
ORFE bridges six interconnected disciplines: operations research, financial engineering, machine learning, optimization, statistics, and probability. This interdisciplinary approach ensures graduates are well-prepared for careers in data science, finance, technology, healthcare, and beyond. The program fosters a deep understanding of how to leverage data, address uncertainty, and optimize resources to enhance quality of life and solve real-world problems.
Image alt text: Sherrerd Hall at Princeton University, the location of the Operations Research and Financial Engineering (ORFE) department offices, excluding Professor Ramon van Handel’s office.
Exploring the Core Disciplines within ORFE
Operations Research: Optimizing Efficiency and Resource Management
Operations Research (OR) is a field focused on using mathematical and analytical methods to improve decision-making and efficiency in complex systems. Originating from World War II and evolving since the early 20th century, OR employs optimization techniques to address challenges in business, logistics, healthcare, energy, and transportation. The central theme in OR is the effective management of resources, be they natural, economic, informational, or human. Graduates with an operations research background are sought after in business, consulting, research, and software development roles, particularly in industries requiring sophisticated resource management and optimization strategies.
Financial Engineering: Innovation in Finance and Risk Management
Financial Engineering applies mathematical models to financial markets to create innovative financial instruments and strategies. This field is crucial for managing risk and cash flow for individuals and corporations. Financial engineers excel in risk analysis and management, developing new financial products tailored to investor needs. The discipline integrates applied mathematics, probability, statistics, stochastic processes, optimization, and financial economics. Graduates are highly recruited by Wall Street firms, banks, insurance companies, and corporate finance departments, filling roles that demand expertise in financial modeling and risk management.
Machine Learning: Data-Driven Insights and Predictive Modeling
Machine Learning (ML) is a dynamic field focused on developing algorithms that enable computers to learn from data, improving performance over time without explicit programming. ML powers advancements like self-driving cars, search engines, and personalized assistants. Within ORFE, the focus is on the mathematical foundations of machine learning, developing algorithms to extract insights from large datasets and make optimal decisions under uncertainty. ORFE graduates are well-positioned to contribute to technological advancements and data-driven decision-making across various industries.
Optimization: The Science of Finding the Best Solutions
Optimization is a fundamental discipline concerned with finding the best possible solution from a set of alternatives. In ORFE, optimization focuses on developing and analyzing algorithms to efficiently solve complex problems, from maximizing revenue to optimizing algorithmic parameters. The department emphasizes large-scale, time-varying optimization problems relevant to the “Big Data” era. Cutting-edge optimization algorithms are essential for numerous applications, including search engines, recommendation systems, and logistics, making it a highly valuable skill set for ORFE graduates.
Statistics: Extracting Knowledge from Data
Statistics is the science of learning from data, providing a framework for making informed decisions across diverse fields. From drug discovery to policy making, statistics is vital for data-driven insights. In the “Big Data” era, statistical expertise is increasingly critical for prediction and understanding uncertainty. ORFE’s statistics curriculum prepares students to analyze complex datasets and extract meaningful information, making them highly sought after in research, business analytics, and various data-intensive roles.
Probability: Modeling Randomness and Uncertainty
Probability theory, or stochastics, provides the mathematical language for randomness and uncertainty. It underpins models in diverse applications ranging from healthcare and finance to epidemiology and genetics. Probability is also fundamental to modeling complex data and statistical methods for analyzing text, speech, and biological data. ORFE emphasizes the application of probability theory to solve complex problems through simulation, risk estimation, and stochastic optimization, equipping students with essential tools for quantitative analysis in uncertain environments.
Overview of the Princeton ORFE Undergraduate Academic Program
The Princeton course guide for ORFE undergraduates outlines a rigorous curriculum designed to provide a strong foundation in the core disciplines and allow for specialization through electives. Students must fulfill Engineering School requirements in addition to the ORFE departmental requirements, which are categorized into four key groups:
- Department Core Requirements (4 courses): These foundational courses cover statistics, probability, stochastic processes, and optimization, providing the essential intellectual framework for the field. Completion of core requirements is mandatory before undertaking independent research.
- Departmental Electives (10 or 11 courses): Electives allow students to deepen their knowledge in core areas or explore related applications. The number of electives depends on the senior independent work option chosen.
- Humanities and Social Sciences (Minimum 7 courses): These courses ensure a broad education, covering areas like ethics, history, literature, social analysis, and foreign languages.
- Engineering School Requirements: These include mathematics, computer science, chemistry, and physics prerequisites, ensuring a strong technical foundation.
Independent Research Opportunities in ORFE
Independent research is a significant component of the Princeton ORFE undergraduate experience, offering opportunities for in-depth exploration of specific topics.
Senior Thesis (ORF 498 & ORF 499): A Year-Long Research Project
The Senior Thesis is a full-year endeavor where students apply ORFE techniques to a chosen topic under faculty guidance. It involves rigorous research, culminating in a substantial thesis report and presentation. Students enroll in ORF 498 in the fall and ORF 499 in the spring, each carrying one credit.
Senior Independent Project (ORF 497): A Semester-Long Research Project
The Senior Independent Project, taken in the spring semester, offers a shorter research experience. Students produce a research report and present their findings. Choosing this option requires students to take an additional 400-level ORFE elective to fulfill the departmental elective requirement.
Note: Students choosing the Senior Independent Project will need to take eleven departmental electives instead of ten.
To graduate, students must complete 36 courses as required by the University, fulfill SEAS requirements, meet ORFE core and elective requirements, and achieve a minimum Departmental GPA of 2.0. No course can count towards more than one requirement category. Course selection and scheduling are done in consultation with assigned academic advisors and the Director of Undergraduate Studies.
Image alt text: Princeton ORFE alumnus Daniel Nash ’03, now a Director at Avery Dennison, pictured in a collaborative environment, highlighting the teamwork and problem-solving skills developed in the ORFE program.
Graduate Course Enrollment for Undergraduates
Undergraduates seeking advanced scholarly enrichment can enroll in graduate-level ORFE courses, which count as departmental electives. Enrollment requires instructor and advisor/Director of Undergraduate Studies approval via a permission form submitted to the Registrar. Graduate courses do not contribute to the departmental GPA.
ABET Accreditation Information
The Princeton ORFE Department does not offer ABET-accredited engineering degrees. However, this has not hindered graduates’ postgraduate opportunities as Princeton University’s B.S.E. degrees are fully accredited. Students requiring an ABET-accredited program for specific scholarships should consult the Undergraduate Announcement for ABET-accredited programs at Princeton.
Princeton ORFE Academic Program Details: Course Requirements
This section of the Princeton course guide details the specific course requirements for the ORFE undergraduate program.
School of Engineering and Applied Science (SEAS) Requirements
All engineering students must complete foundational courses in:
- Mathematics: MAT 103, 104; MAT 201 or 203, 202 or 204 or 217
- Computer Science: COS 126
- Chemistry: CHM 201 or 207
- Physics: PHY 103 or 105, 104 or 106
Operations Research and Financial Engineering (ORFE) Requirements
Core Program: Essential ORFE Courses
The ORFE core curriculum provides the fundamental knowledge base for all students:
ORF 245: Fundamentals of Statistics
An introductory course to probability and statistics, covering estimation, confidence intervals, hypothesis testing, and regression. The course emphasizes practical application using statistical software and real-world data analysis. Prerequisite: MAT 201 (may be taken concurrently) or equivalent.
ORF 307: Optimization
Focuses on analytical and computational tools for optimization, including least-squares optimization, linear optimization, duality, simplex method, integer programming, and network flow optimization. Applications span engineering, finance, and statistics. Prerequisite: MAT 202 or 204. Basic programming knowledge suggested.
ORF 309: Probability and Stochastic Systems
Introduces probability theory and its applications, including Poisson processes, random walks, Brownian motion, Markov chains, and reliability theory. Prerequisite: MAT 201, MAT 203, or MAT 216.
ORF 335: Introduction to Financial Mathematics (also ECO 364)
Explores quantitative methods in financial markets, covering arbitrage, risk-neutral pricing in discrete time, Black-Scholes and Heston models in continuous time, and calibration to market data. Prerequisite: MAT 104 and ORF 309.
Departmental Electives: Specialization and Breadth
Students must choose ten or eleven departmental electives to further specialize or broaden their ORFE education, adhering to these constraints:
- Minimum of four courses from ORFE.
- Maximum of three courses from any single department (excluding ORFE).
List of Departmental Electives:
- ORF 311 – Stochastic Optimization and Machine Learning in Finance
- ORF 350 – Analysis of Big Data
- ORF 363/COS 323 – Computing and Optimization for the Physical and Social Sciences
- ORF 375/376 – Junior Independent Work
- ORF 387 – Networks
- ORF 401 – Electronic Commerce
- ORF 405 – Regression and Applied Time Series
- ORF 407 – Fundamentals of Queueing Theory
- ORF 409 – Introduction to Monte Carlo Simulation
- ORF 418 – Optimal Learning
- ORF 435 – Financial Risk and Wealth Management
- ORF 445 – High Frequency Markets: Models and Data Analysis
- ORF 455 – Energy and Commodities Markets
- ORF 467 – Transportation Systems Analysis
- ORF 473/474 – Special Topics in Operations Research and Financial Engineering
- APC 350/MAT 322 – Introduction to Differential Equations
- CEE 304 – Environmental Engineering and Energy
- CEE 460 – Risk Analysis
- CHM 301 – Organic Chemistry I
- CHM 304 – Organic Chemistry II
- COS 217 – Introduction to Programming Systems
- COS 226 – Algorithms and Data Structures
- COS 423 – Theory of Algorithms
- COS 485 – Neural Networks: Theory and Applications
- ECE 301 – Designing Real Systems
- ECE 381 – Networks: Friends, Money and Bytes
- ECE 473 – Elements of Decentralized Finance
- ECE 486 – Transmission and Compression of Information
- ECO 310 – Microeconomic Theory: A Mathematical Approach
- ECO 311 – Macroeconomics: A Mathematical Approach
- ECO 312 – Econometrics: A Mathematical Approach
- ECO 317 – The Economics of Uncertainty
- ECO 332 – Economics of Health and Health Care
- ECO 341 – Public Finance
- ECO 342 – Money and Banking
- ECO 361 – Financial Accounting
- ECO 362 – Financial Investments
- ECO 363 – Corporate Finance and Financial Institutions
- ECO 418 – Strategy and Information
- ECO 462 – Portfolio Theory and Asset Management
- ECO 464 – Corporate Restructuring
- ECO 466 – Fixed Income, Options and Derivatives: Models and Applications
- ECO 467 – Institutional Finance, Trading and Markets
- EEB 324 – Theoretical Ecology
- EEB 325 – Mathematical Modeling in Biology and Medicine
- ENV 302 – Practical Models for Environmental Systems
- MAE 206 – Introduction to Engineering Dynamics
- MAE 305 / MAT 391 / EGR 305 / CBE 305- Mathematics in Engineering I OR MAT 427, (both may not be taken due to similar content)
- MAE 306/MAT 392 – Mathematics in Engineering II
- MAE 345 – Introduction to Robotics
- MAE 433 – Automatic Control Systems
- MAE 434 – Modern Control
- MAT 320 – Introduction to Real Analysis
- MAT 375/COS342 – Introduction to Graph Theory
- MAT 377/APC 377 – Combinatorial Mathematics
- MAT 378 – Theory of Games
- MAT 385 – Probability Theory
- MAT 427 – Ordinary Differential Equations
- MAT 486 – Random Process
- MAT 522/APC522 – Introduction to Partial Differential Equations
- MOL 345/CHM 345 – Biochemistry
- NEU 437/MOL 437/PSY 437– Computational Neuroscience: Computing with Populations of Neurons
- NEU 330/PSY330 – Computational Modeling of Psychological Function
- PSY 360/COS 360: Computational Models of Cognition
Suggested Thematic Course Selections:
Students can specialize by choosing electives around themes such as:
- Applied Mathematics: MAT 375, 378, 320; ORF 405
- Engineering Systems: ORF 363, 409, 467; COS 226; ECE 301; MAE 433
- Financial Engineering: ORF 311, 350, 405, 435; ECO 362
- Information Sciences: ORF 401, 418; COS 217, 226
- Machine Learning: COS 217, 226; ORF 350, 407, 418
- Pre-Med/Health Care: CHM 301, 304; MOL 345; ORF 350, 401, 418
- Statistics: ORF 311, 350, 409, 418, 405, 467
Junior and Senior Independent Work in Princeton ORFE
Junior Independent Work (Optional)
Junior Independent Work is an option for students to engage in research early in their academic career. Students need to submit a proposal including:
- Title
- 100-word abstract describing the problem, its importance, and expected outcomes.
- Weekly syllabus and work plan.
- Table of contents for the final report.
- Faculty supervisor agreement.
Proposals and signed Junior Independent Study Forms must be submitted to the Undergraduate Administrator before the first day of classes. Faculty advisors set the requirements for satisfactory completion.
Senior Independent Work: Thesis or Project
Senior year offers two paths for independent work:
Senior Thesis (ORF 498 & ORF 499)
A two-semester research undertaking involving in-depth study and a substantial thesis. It starts in the spring of junior year with advisor selection and culminates in a final thesis report and presentation in senior year.
Senior Independent Project (ORF 497)
A one-semester research project in the spring of senior year, requiring a shorter report and presentation. Students opting for the project must take an additional 400-level ORFE elective. A progress report is due in December of the fall semester.
Typical Course Schedule for ORFE Undergraduates
This sample Princeton course guide schedule illustrates a typical four-year plan for ORFE undergraduates. Note: Junior independent work requires completing core courses beforehand.
FIRST YEAR
| Fall | Spring |
|---|---|
| 1. CHM 201/207-General Chemistry | 1. COS 126 Gen. Comp. Sci. |
| 2. MAT 104 Calculus | 2. MAT 201 Multivariance Calc |
| 3. PHY 103 General Physics | 3. PHY 104 General Physics 2 |
| 4. Writing Requirements | 4. (ORF 245 Fund. of Statistics) |
| 5. | 5. |
SOPHOMORE YEAR
| Fall | Spring |
|---|---|
| 1. ORF 245 Fund. of Statistics | 1. ORF 307 Optimization |
| 2. MAT 202 Linear Algebra Appl. | 2. ECO 310 Microecon Theory |
| 3. Departmental Elective | 3. (ORF 309 Probability/Stat Systems) |
| 4. (ORF 309 Probability/Stat Systems) | 4. (ORF 335 Intro Fin. Mathematics) |
| 5. (ECO 310 Microecon Theory) | 5. |
JUNIOR YEAR
| Fall | Spring |
|---|---|
| 1. ORF 309 Probability/Stat Systems | 1. ORF 335 Intro Fin. Mathematics |
| 2. Departmental Electives | 2. Departmental Electives |
| 3. Departmental Electives | 3. Departmental Electives |
| 4. | 4. Departmental Electives |
| 5. | 5. |
SENIOR YEAR
| Fall | Spring |
|---|---|
| 1. ORF 498 Sr. Ind. Res. Foundat. | 1. ORF 499 Sr. Thesis |
| 2. Departmental Electives | 2. Departmental Electives |
| 3. Departmental Electives | 3. (ORF 497) |
| 4. | 4. (11th Departmental Elective) |
| 5. | 5. |
Departmental GPA, Honors, and Prizes in ORFE
Departmental GPA Calculation
The Departmental GPA is a key metric, calculated from 15 grades: ORF 498, 499 (for thesis students) or ORF 497 and an additional 400-level elective (for project students), the ten highest graded departmental electives, and core courses (excluding ORF 245). Qualified departmentals are from the elective list with a maximum of three from one department (excluding ORFE). A minimum 2.0 Departmental GPA is required for graduation.
Graduation Requirements for ORFE Majors
- Minimum 2.0 Departmental GPA.
- Passing grades in all ORFE core courses and departmental electives.
- Completion of 36 University courses.
- Fulfillment of SEAS requirements.
- Fulfillment of ORFE core and departmental elective requirements.
Honors and Awards
- Honors: Highest Honors, High Honors, and Honors are awarded based on Departmental GPA, overall academic record, and class performance.
- Prizes: Individual prizes are awarded for specific achievements in defined categories.
Additional Program Opportunities for ORFE Students
Certificate and Minor Programs
ORFE students frequently enhance their education with complementary certificate or minor programs:
- Minor in Environmental Studies
- Minor in Statistics and Machine Learning
- Minor in Computer Science
- Minor in Finance
- Minor in Robotics
- Minor in Sustainable Energy
- Minor in Applied and Computational Mathematics
- Certificate in Optimization and Quantitative Decision Science (housed in ORFE)
External Course Enrollment
Students can take courses at other institutions during summers or semesters off, requiring a Transfer Course Approval Form. Core courses (except ORF 245) generally cannot be taken externally without special permission. Approved departmental electives taken externally do not count towards the departmental GPA. Semester or year abroad requires Director of Undergraduate Studies approval.
Princeton ORFE Faculty Research Interests
The ORFE department boasts faculty with diverse and impactful research interests, offering students opportunities to engage with cutting-edge work.
- Amirali Ahmadi: Optimization, computational aspects of dynamics and control, algorithms and complexity, applications in systems theory, machine learning, robotics, and economics.
- Rene A. Carmona: Stochastic analysis, stochastic control and games, reinforcement learning, high-frequency markets, environmental finance, energy and commodity markets.
- Matias D. Cattaneo: Econometric theory, mathematical statistics, program evaluation, machine learning, nonparametric methods, high-dimensional inference, applications to social sciences.
- Jianqing Fan: Statistical machine learning, big data, high-dimensional inference, neural networks, factor modeling, dynamic pricing, reinforcement learning, applications in finance and health sciences.
- Boris Hanin: Theory of deep learning, optimization, generalization in neural networks, architecture selection, and hyperparameter tuning.
- Emma Hubert: Applied mathematics, interactions and incentives, economics, stochastic control, mean-field games, applications to energy, epidemiology, and finance.
- Jason Klusowski: Statistical and algorithmic problems in data applications, large-scale predictive models, decision trees, and neural networks.
- Alain L. Kornhauser: Autonomous vehicles, computer vision, collision-free driving, accident risk quantification, human-computer interfaces for SmartDrivingCars, autonomousTaxi systems.
- Sanjeev Kulkarni: Statistical pattern recognition, machine learning, applied probability, information theory, signal and image processing, blockchain, and cryptocurrencies.
- William A. Massey: Resource sharing services and systems, queueing theory, Markov processes, stochastic networks, optimal control, Monte Carlo simulation.
- John M. Mulvey: Optimization under uncertainty, financial planning systems, decentralized optimization, hedge fund optimization, machine learning in finance.
- Liza Rebrova: High-dimensional probability, matrix and tensor methods, randomized algorithms, compressive sensing, random matrix theory, mathematics of data.
- Ronnie Sircar: Financial mathematics, stochastic models for volatility, optimal investment and hedging, financial data analysis, credit risk, dynamic game theory, energy and commodity markets.
- Mete Soner: Mathematical theory of optimal control, decisions under uncertainty, stochastic optimization, applications in economics, finance, and high-dimensional computation.
- Bartolomeo Stellato: Data-driven optimization, machine learning, optimal control, real-time control, transportation, finance, robotics, autonomous vehicles.
- Ludovic Tangpi: Financial mathematics, risk management, stochastic analysis, probability theory, statistical and numerical methods in finance.
Image alt text: Princeton ORFE faculty member Professor Alain L. Kornhauser, known for his research in Autonomous Vehicles and SmartDrivingCars.
Research and Teaching Studios at Princeton ORFE
ORFE students benefit from access to specialized research and teaching studios:
- Financial Econometrics Studio: Focuses on quantitative finance problems using statistical techniques and financial economic theory, including derivative valuation, portfolio allocation, and risk management.
- Statistics Studio: Studies statistical theory and methods, particularly high-dimensional statistics, biostatistics, and large-scale statistical computing, with applications in machine learning and data analysis.
- Transportation Center: Conducts research on information and decision engineering technologies to improve transportation-related decision-making.
Career Paths for Princeton ORFE Graduates
A Princeton course guide to ORFE careers highlights the diverse and impactful paths alumni pursue. ORFE graduates are highly sought after in various industries due to their strong analytical and problem-solving skills. Here are examples from ORFE alumni:
Diverse Career Trajectories of ORFE Alumni
- Finance: Investment banking, trading, asset management, hedge funds, financial analysis (Morgan Stanley, Goldman Sachs, Bridgewater Associates, Jane Street, etc.)
- Technology: Data science, machine learning, software engineering, analytics (Google, Microsoft, start-ups)
- Consulting: Management consulting, technology consulting, financial consulting (McKinsey, BCG, Accenture, Cornerstone Research)
- Operations Research and Analytics: Supply chain management, logistics, transportation, healthcare operations
- Academia and Research: PhD programs, postdoctoral positions, research roles in universities and institutions
- Government and Non-profit: Public policy, government agencies, international organizations (World Bank, Environmental Defense Fund)
- Entrepreneurship: Start-up founders, technology ventures
Alumni Spotlights: Real-World Success Stories
Meghan Fehlig ‘02
Image alt text: Princeton ORFE alumna Meghan Fehlig ’02, currently working as a Transportation Engineer at Parsons Brinckerhoff in Princeton, NJ.
Meghan is a Transportation Engineer at Parsons Brinckerhoff, applying her ORFE background to traffic analysis, research, and market-based transportation solutions.
Daniel Nash ‘03
Image alt text: Princeton ORFE alumnus Daniel Nash ’03, serving as Director of New Growth Platforms at Avery Dennison, leveraging ORFE skills in business strategy and market analysis.
Daniel is Director of New Growth Platforms at Avery Dennison, utilizing his ORFE education to identify and build new business opportunities, particularly in healthcare.
Katherine Milkman ‘04
Image alt text: Princeton ORFE alumna Katherine Milkman ’04, now a Professor at the Wharton School of the University of Pennsylvania, specializing in decision-making and behavioral economics.
Katherine is a Professor at the Wharton School, researching decision-making and behavioral economics, applying her ORFE foundation to understand and improve human choices.
Kimberly Mattson ‘05
Image alt text: Princeton ORFE alumna Kimberly Mattson ’05, working as an Investment Associate at Bridgewater Associates, applying ORFE concepts in portfolio structuring and risk management.
Kimberly is an Investment Associate at Bridgewater Associates, applying ORFE principles to portfolio structuring, risk management, and Monte Carlo simulations in finance.
Jacqueline Ng ‘06
Image alt text: Princeton ORFE alumna Jacqueline Ng ’06, currently in a Strategy and Finance role at Microsoft, utilizing her ORFE background in technology and business strategy.
Jacqueline works in strategy and finance at Microsoft, leveraging her ORFE degree for quantitative thinking and problem-solving in the technology sector.
Raj Hathiramani ‘07
Image alt text: Princeton ORFE alumnus Raj Hathiramani ’07, with experience at Google in sales strategy and Citadel in finance, currently pursuing an MBA at Wharton.
Raj, with experience at Citadel and Google, is pursuing an MBA at Wharton, applying his ORFE skills in finance, analytics, and business strategy.
Zachary Kurz ‘08
Image alt text: Princeton ORFE alumnus Zachary Kurz ’08, working as an Investment Banking Analyst at Morgan Stanley, applying his ORFE math and programming skills in finance.
Zachary is an Investment Banking Analyst at Morgan Stanley, utilizing his ORFE math and programming skills in mergers and acquisitions.
Jonathan Lange ‘09
Image alt text: Princeton ORFE alumnus Jonathan Lange ’09, currently working in Private Equity at Bain Capital, applying his ORFE education in financial modeling and valuation.
Jonathan is in private equity at Bain Capital, applying his ORFE education in financial modeling and operations optimization.
Kate Hsih ‘10
Image alt text: Princeton ORFE alumna Kate Hsih ’10, with a background in global health and medical anthropology, demonstrating the broad applicability of an ORFE degree.
Kate has worked in global health and is pursuing medical anthropology studies, highlighting the broad applicability of ORFE in diverse fields.
Chetan Narain ‘11
Chetan works at Google on Search Ads Quality, using his ORFE background in engineering, computer science, and mathematics for ad prediction and data analysis.
Nathan & Natalie Keys ‘12
Image alt text: Princeton ORFE alumni Nathan and Natalie Keys ’12, working in technology and data analysis, showcasing the versatility of an ORFE education in diverse tech roles.
Natalie is in data analysis and programming, while Nathan is in marketing analytics and technology development, both applying their ORFE skills in technology start-ups.
Bryton Shang ‘12
Image alt text: Princeton ORFE alumnus Bryton Shang ’12, Founder of Aquabyte and Forbes 30 Under 30 honoree, leveraging ORFE knowledge in AI and aquaculture technology.
Bryton is the founder of Aquabyte, using computer vision and AI in aquaculture, applying his ORFE knowledge in algorithmic trading and tech entrepreneurship.
Anna Zhao ‘12
Image alt text: Princeton ORFE alumna Anna Zhao ’12, working in Basketball Operations at the NBA League Office, applying her ORFE skills in sports analytics and operations.
Anna works in Basketball Operations at the NBA League Office, applying her ORFE skills in probability and statistical analysis to sports data.
Malavika Balachandran ‘13
Image alt text: Princeton ORFE alumna Malavika Balachandran ’13, working as an Analyst in the Securities Division at Goldman Sachs, applying her ORFE financial math knowledge in derivatives.
Malavika is an Analyst at Goldman Sachs, using her ORFE financial mathematics background in structuring derivative products.
Adam Esquer ‘14
Image alt text: Princeton ORFE alumnus Adam Esquer ’14, working in Research and Development for the Tampa Bay Rays, applying ORFE analytics in the sports industry.
Adam works in Research and Development for the Tampa Bay Rays, applying ORFE’s analytical and technical skills in the sports industry.
Chris McCord ’15
Image alt text: Princeton ORFE alumnus Chris McCord ’15, holding a PhD in Operations Research from MIT and currently a Visiting Lecturer at Princeton ORFE.
Chris, with a PhD in Operations Research from MIT, is a Visiting Lecturer at Princeton ORFE, demonstrating a path in academia after ORFE.
Michelle Scharfstein ’15
Image alt text: Princeton ORFE alumna Michelle Scharfstein ’15, working as a Software Engineer at Google, utilizing her ORFE skills in data analytics and software development.
Michelle is a Software Engineer at Google, applying her ORFE skills in data analytics, statistical modeling, and software development.
Lydia Liu ’17
Image alt text: Princeton ORFE alumna Lydia Liu ’17, now an Assistant Professor at Princeton University, focusing her research on machine learning and algorithmic decision-making.
Lydia is now an Assistant Professor at Princeton, focusing her research on machine learning and algorithmic decision-making, showcasing an academic career path directly from ORFE.
Class of 2023 Senior Thesis Titles: Innovation and Research
The Senior Thesis titles from the Class of 2023 showcase the breadth and depth of research undertaken by ORFE undergraduates, reflecting diverse interests and applications of ORFE principles. The titles range from financial markets and algorithmic trading to social issues, healthcare, and environmental sustainability, demonstrating the versatility of an ORFE education.
(List of thesis titles omitted for brevity, original document contains full list)
Class of 2023 Post-Graduate Plans: Diverse Career Destinations
The post-graduate plans of the Class of 2023 further illustrate the wide array of career paths available to ORFE graduates. They have joined leading companies in finance, consulting, technology, and are pursuing advanced degrees at top universities, reflecting the strong marketability and academic preparation provided by the Princeton ORFE program.
(List of post-graduate plans omitted for brevity, original document contains full list)
Conclusion: Your Path with the Princeton ORFE Course Guide
This Princeton course guide has provided a detailed overview of the Operations Research and Financial Engineering department at Princeton University. From core courses and elective options to research opportunities and diverse career paths, ORFE offers a rigorous and rewarding academic experience. Whether you aim for Wall Street, Silicon Valley, academia, or beyond, a Princeton ORFE education equips you with the analytical and quantitative skills to excel and lead in a data-driven world. Explore the curriculum, connect with faculty, and discover how ORFE can be your launchpad to a successful and impactful future.
