Class of 2023 Princeton Preview

1. Class of 2023Princeton Preview Presented by Prof. Alain Kornhauser Department Representative For more info see orfe.princeton.edu

2. 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.

3. Marketable Skills • Probability: Modeling & understanding of uncertainty. • Statistics: Quantifying uncertainty. • Optimization: Modeling & understanding of the tradeoffs associated with the good fortune 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.

4. 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”)

5. 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

6. Core Classes • ORF 245 – Fundamentals of Statistics • ORF 307 – Optimization • ORF 309 – Probability & Stochastic Processes • ORF 335 – Introduction to Financial Engineering

7. Ten Department 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

8. 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

9. 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

10. More Common Tracks • Machine Learning • COS 217 – Intro to Graph Theory • COS 226 – Theory of Games • ORF 350 – Analysis of Big Data • ORF 407 – Fundamentals of Queueing Theory • ORF 411 – Sequential Decision Analytics Modeling • ORF 418 – Optimal Learning • Statistics • ORF 311 – Stochastic Optimization and Machine Learning in Finance • ORF 350 – Analysis of Big Data • ORF 405 – Regression and Applied Time Series • ORF 409 – Intro to Monte Carlo Simulation • ORF 418 – Optimal Learning • ORF 467 – Transportation Systems Analysis

11. More Common Tracks • Pre-Med/Health Care • CHM 303 – Organic Chemistry I • CHM 304 – Organic Chemistry II • MOL 345 – BioChemistry • ORF 350 – Analysis of Big Data • ORF 401 – eCommerce • ORF 411 – Sequential Decision Analytics Modeling • ORF 418 – Optimal Learning

12. Selected Senior Theses • Eileen Lee’14 – Uncovering Systematic Corruption in the ER: An Empirical Analysis of Motor Vehicle-Related Hospital Bills and their Impacts on Insurance Companies • Adam Esquer’14 -The Real Moneyball: Modelling Baseball Salary Arbitration • Chad Cowden’17-Default Prediction of Commerical Real Estate Properties through the use of Support Vector Machines • Stephanie Lubiak’11 – Neighborhood Nukes: Great for America? Great for the Environment? Great for Al Qaeda? • Serena Jeon’17– Walking on Wall Street in Heels: A Quantitative and Sociological Approach to Gender and Recruitment in Finance • A. Hill Wyrough, Jr.’14 – A National Disaggregate Transportation Demand Model for the Analysis of Autonomous Taxi Systems • Ian Kinn’17– Seeking Relief: Making the Most of Pitchers in the Modern Era of Major League Baseball • Walid Marfouk’17– Fashion Police: Application of Convolutional Neural Networks to Single-Step Apparel Recognition on Social Media in Scarce Training Data Contexts

13. Recent Graduates • Graduate Schools: Harvard, Stanford, Cornell, Georgia Tech, Texas A&M, U. of Kentucky (Med School), U. of Calif. Berkeley • Banks & Investment Firms: Goldman Sachs, Morgan Stanley, JP Morgan Chase & Co, Barclays, BlackRock, Credit Suisse • Industries: Aspect Medical Systems, Parsons Airbnb, Walt Disney, Abercrombie, Google, IBM • Management/Economic Consulting: Mercer, Accenture, McKinsey, Bates

14. Recent Graduates

15. Questions / Discussion For more info see orfe.princeton.edu