Princeton Preview

Class of 2023

Presented by Prof. Alain Kornhauser

Why ORFE

  • Study and work on challenging and relevant problems.
  • Learn and apply mathematical & computational skills to address interesting, useful and timely applications.
    • These skills are recognized and rewarded in the marketplace by employers & top graduate schools.
    • They will make you a better Leader.

Marketable skills

  • Probability: Modeling & understanding of uncertainty.
  • Statistics: Quantifying uncertainty.
  • Optimization: MOdeling & understanding of the tradeoffs associated with the good fortun of having alternatives (and choosing among them even though they are uncertain).
    • These skills are recognized and rewarded in the marketplace by employers & top graduate schools.
    • They will make you a better Leader.

Skills are Focused on Improving Societal Challenges

  • Operations Research

    • Logistics & Transportation
    • Energy Systems
    • Telecommunications & eCommerce
    • Health Care
  • Financial Engineering

    • Risk Management
    • Investment Strategies
    • Financial Instruments
    • Economic Stimulation
  • Machine Learning

    • Real-time Decision Systems
    • Addressing High Dimensional Problems (aka "Big Data")

Freshman Year

  • Fall: 4 Courses

    • Math
    • Physics
    • Chemistry
    • Writing (or Frosh Seminar or ???)
  • Spring: 5 Courses

    • Math
    • Physics
    • Statistics (ORF 245)
    • Frosh Seminar (or Writing or ???)
    • Other

Core Classes

  • ORF 245 - Fundamentals of Statistics
  • ORF 307 - Optimization
  • ORF 309 - Probability & Stochastic Processes
  • ORF 335 - Introduction to Financial Engineering

Ten Departmental Electives

From... ORF 311 – Stochastic Optimization and Machine Learning in Finance (previously - Optimization Under Uncertainty), ORF 350 – Analysis of Big Data, ORF 360 – Decision Modeling in Business Analytics, ORF 363 – Computing and Optimization for the Physical and Social Sciences, ORF 375/376 - Junior Independent Work, ORF 401 - Electronic Commerce , ORF 405 – Regression and Applied Tim, Series, ORF 406 - Statistical Design of Experiments, ORF 407 – Fundamentals of Queueing Theory, ORF 409 - Introduction to Monte Carlo Simulation, ORF 411 – Sequential Decision Analytics and Modeling, ORF 417 - Dynamic Programming, ORF 418 - Optimal Learning, ORF 435 - Financial Risk Management, ORF 455 – Energy and Commodities Markets, ORF 467 – Transportation Systems Analysis, ORF 473/474 - Special Topics in Operations Research and Financial Engineering, CEE 304 – Environmental Engineering and Energy, CEE 460 - Risk Analysis , CHM 303 – Organic Chemistry I, CHM 304 – Organic Chemistry II, COS 217 - Introduction to Programming Systems, COS 226 - Algorithms and Data Structures, COS 323 - Computing for the Physical and Social Sciences, COS 340 - Reasoning about Computation, COS 402 - Artificial Intelligence and Machine Learning, COS 423 - Theory of Algorithms, COS 485 – Neural Networks: Theory and Application, ECO 310 - Microeconomic Theory: A Mathematical Approach, ECO 311/312 – Macroeconomics: 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: Models and Applications, ECO 467 - Institutional Finance, EEB 324 – Theoretical Ecology, ELE 301 – Designing Real Systems, ELE 381 – Networks: Friends, Money and Bytes, ELE 486 - Digital Communication and Networks, ENV 302 – Practical Models for Environmental Systems, MAE 206 – Introduction to Engineering Dynamics, MAE 433 - Automatic Control Systems, MAE 434 – Modern Control, MAT 320 - Introduction to Real Analysis, MAT 322/APC 350 - Methods in Partial Differential Equations, MAT 375 - Introduction to Graph Theory, MAT 377 - Combinatorial Mathematics, MAT 378 - Theory of Games, MAT 385 - Probability Theory, MAT 391/MAE 305 - Mathematics in Engineering I or MAT 427, (both may not be taken because content is too similar), MAT 392/MAE 306 - Mathematics in Engineering II, MAT 427 - Ordinary Differential Equations, MAT 486 - Random Process, MAT 522 - Introduction to Partial Differential Equations, MOL 345 – Biochemistry, NEU 437 – Computational Neuroscience, NEU 330 – Computational Modeling of Psychological Function

Some Common Tracks

  • Information Sciences

    • ORF 401 – eCommerce
    • ORF 411 – Sequential Decision Analytics Modeling
    • ORF 418 – Optimal Learning
    • COS 217 – Introduction to Programming Systems
    • COS 226 – Algorithms & Data Structures
    • COS 425 – Database Systems
  • Engineering Systems

    • ORF 409 – Intro to Monte Carlo Simulation
    • ORF 411 – Sequential Decision Analytics Modeling
    • ORF 467 – Transportation Systems Analysis
    • ORF 417 – Dynamic Programming
    • MAE 433 – Automatic Control Systems
    • ELE 485 – Signal Analysis and Communication Systems

More Common Tracks

  • Applied Mathematics

    • MAT 375 – Intro to Graph Theory
    • MAT 378 – Theory of Games
    • MAT 321 – Numerical Methods
    • MAE 406 – Partial Differential Equations
    • ORF 405 – Regression and Applied Time Series
  • Financial Engineering

    • ORF 311 – Stochastic Optimization and Machine Learning in Finance
    • ORF 350 – Analysis of Big Data
    • ORF 405 – Regression and Applied Time Series
    • ORF 435 – Financial Risk Management
    • ECO 362 – Financial Investments
    • ECO 465 – Financial Derivatives
  • Machine Learning

  • Statistics
  • Pre-Med/Health Care

Selected Senior Theses

Recent Graduates