Important Contacts

Department Chair

• Professor Mete Soner

The Undergraduate Office

Director of Undergraduate Studies

• Professor Alain Kornhauser

Undergraduate Administrator

• Caitlin Daley 

Academic Advisors

All ORFE department offices are located in Sherrerd Hall, with the exception of Prof. Ramon van Handel whose office is in Fine Hall.

An Introduction to the Department

Humankind has always faced significant challenges.  With today's technology, however, we have the ability to collect vast amounts of data to illuminate the problems and opportunities we face.  More importantly, we now can apply an array of robust analytical tools to deepen our understanding of the issues, to help us make better decisions and to develop solutions that will enhance our quality-of-life.

While collecting vast amounts of data is becoming easier, collecting the right data remains very expensive. How does one get from vast data to the right data?  How should one analyze, understand and appropriately address uncertainty that is present in essentially all societal endeavors? And, how can one exploit these resources to make the best possible decisions and best contribute to an enhanced quality of life?  These are precisely the questions that form the core of the Department of Operations Research and Financial Engineering. Problems of this kind have appeared for a long time in the understanding of logistics and management, energy, the environment, agriculture, the military and micro- and macro-economics.  As our ability to handle more data and complexity has expanded, so has our ability to more deeply address traditional applications and venture to tackle new ones in finance, health care, biology and most aspects of our society.  The basic mathematical modeling and analytic skills that lie at the heart of ORFE are now essential prerequisites to almost any quantitative discipline, including areas such as data science and statistics, information technology, energy resource management, health care, risk management, and many others.  We view ORFE as the ideal quantitative education for the modern world, whether you see your future as a business leader, health care provider, legal/policy/political professional, data scientist, quantitative analyst, or academic researcher. Even poets might find something to love in ORFE.

ORFE is the intersection of six closely interconnected applied mathematical disciplines and application areas that lie at the heart of all activities in the Department: operations research, financial engineering, machine learning, optimization, statistics and probability.  Each of these areas is described in more detail below.

Operations Research

The field of operations research traces its foundations to World War II, although it includes dimensions that go as far back as the early days of the telephone at the beginning of the 20th century. It uses mathematical models and optimization methods to rigorously address quantitative problems in business and management, logistics, health care, energy systems, telecommunications, and transportation. The most common theme that runs through these problems is the efficient management of resources, where resources may be natural resources, economic resources, informational resources, equipment, people, and/or physical facilities. Students in operations research may follow a management track into business or consulting, or a more technical path into teaching, research, or software development. There is a burgeoning marketplace for sophisticated methods to schedule airlines and railroads or to optimize supply chains for large manufacturing and retail enterprises. 

Financial Engineering

Financial engineering uses mathematical models of financial markets to design innovative financial instruments and strategies to meet the specific needs of individuals and corporations, including managing risks and cash flows. To this end, financial engineers analyze and ultimately manage risks within an integrated framework, as compared with traditional piecemeal approaches. Often, new instruments are constructed based on the specific requirements of the investor. Solving these problems in a principled manner requires a combination of diverse analytical methods from applied mathematics, probability and statistics, stochastic processes and stochastic calculus, optimization, financial economics, and computation skills. Students grounded in financial engineering are in great demand: they find jobs on Wall Street and with traditional financial companies such as banks, insurance companies, mutual funds, and financial consulting companies, as well as in the CFO office within mainline corporations.

Machine Learning

Machine Learning is a scientific field that aims to design computer programs and algorithms that can automatically learn from data and improve with experience. Machine Learning is routinely used to make fast and accurate predictions and decisions in problems whose scale is too large for humans to handle. Machine learning lies behind many recent technological advances such as self-driving cars, intelligent personal assistants, web search, credit risk analysis, customer preferences analysis and even genomics. At ORFE, we use mathematical tools to develop and analyze new Machine Learning algorithms to discover hidden structure in massive datasets and to make optimal decisions in the face of uncertainty and limited information.

Optimization

Optimization is everywhere. Whether one talks about optimizing revenue, finding the best route to visit all the major cities of the United States, or optimizing the parameters of your new algorithm, there is common thread: one wants to design procedures that can rapidly and accurately optimize a given function of interest. The area of mathematical programming is concerned with the design and analysis of such procedures. At ORFE we are particularly interested in large-scale time-varying optimization problems, involving substantial uncertainty and variability. Indeed these problems are more and more important in our "Big Data" era. We also pay special attention to the interplay between optimization and the domain specificity of the applications, whether it is finance, statistics or machine learning. The cutting-edge domain specific algorithms for large-scale optimization are nowadays critical to the success of most IT companies, including Google's search engine or Netflix's recommendation system.

Statistics

Statistics is the science of learning from data. Learning what? Well, almost anything: Statistics are behind drug discovery, political campaigns, climate policies, genetic screenings, financial portfolio management and quality control to name only a few. If your senior thesis involves data, statistics will be your best friend. Statistics is a universal framework to make decisions based on data. In the “Big Data” era, statistics is more relevant than ever: it allows us to make predictions and understand the inherent uncertainty associated to these predictions. Moreover, using modern computer resources, statisticians have been able to contemplate richer models that fit better to reality and make ground breaking scientific discoveries, for example in understanding the human genome.

Hal Varian, the chief economist of Google, has famously said that the statisticians have the sexiest job of the 21st century. One advantage of working in statistics is that you can combine your interest with almost any other field in science, health, technology, or business. Most organizations now collect huge amounts of data and need statisticians to extract relevant information from them.

Probability

Probability theory, also known as stochastics, is the mathematics of randomness and uncertainty.  Probabilistic models lie at the heart of any application that involves uncertain outcomes, be it the arrival of patients and the availability of beds in hospitals, the fluctuations of financial markets, the spread of epidemics, the inheritance of genetic traits, and numerous other applications that span engineering and the sciences.  They also provide the fundamental framework for modeling the structure of complex data, and form the foundation of statistical methods for analyzing text, speech, and biological data.  At the same time, randomness can serve as an important resource for solving otherwise intractable problems, including simulation methods for estimating risks or for tracking and prediction in uncertain systems, random measurements for exploring customer preferences and for speeding up data acquisition in signal processing, and stochastic optimization algorithms for making optimal decisions on the basis of noisy data. Probability theory provides the tools needed to design and analyze such models and methods and to rigorously understand their behavior.

Overview of the Undergraduate Academic Program

In addition to meeting the usual Engineering School requirements, students will complete courses in the following four groups:

• Department’s core requirements (4 courses) – These form the intellectual foundation of our field, and cover statistics, probability, stochastic processes, and optimization.
• Departmental electives (10 or 11 courses depending on senior thesis choice).  These are additional courses that either extend and broaden the core, or expose the student to a significant problem area or application closely related to the core program.

Independent Research

• Senior Thesis - ORF 498 and ORF 499.  This is a full-year effort that involves a major application of the techniques in the program applied to a topic that a student chooses in consultation with a faculty advisor. Students interested in a full-year senior thesis must be willing to spend the appropriate time during both the fall and the spring semesters.  Students will enroll in ORF 498 in the fall semester, and ORF 499 in the spring, each at one credit.
  OR
• Senior Independent Project - ORF 497 (Spring semester only) - A short progress report will be due in December, (Dean’s Date) to make sure that the students have chosen a suitable topic and matched up with their advisor.  The final outcome will be a report due at the end of the spring semester.  The report will fully describe the research project and its results. Typically projects will take approximately 30 to 50 pages.  Students who take the independent project will need to take an additional 400-level ORFE departmental elective.

A minimum of 7 courses from the humanities and social sciences. These courses must include 1 course in 4 of the following areas:

• Epistemology and Cognition (EC)
• Ethical Thought and Moral Values (EM)
• Historical Analysis (HA)
• Literature and the Arts (LA)
• Social Analysis (SA)
• Foreign Language (107/108 level or above)
• Culture and Difference (CD)

The University requires that 36 courses be taken to graduate.  The School of Engineering requires math achievement through Math (MAT 103, 104, 201 and 202, or equivalent), Computer Science proficiency (COS 126), Chemistry (CHM 201 or 207) and Physics (PHY 103, 104).  Ten courses required to meet the engineering school requirement of 36 courses (more if the student places out of any of the SEAS requirements).  Students are free to select these courses from any department, and are expected to design an academic program with the assigned advisor that produces a balanced education.  No course may count under more than one heading, under any circumstances.

Each student selects and schedules courses in consultation with the assigned academic advisor and the Director of Undergraduate Studies. While generally no exceptions in the core courses are allowed, students may design specialized programs through their choice of departmental electives.  Specialized programs must be approved by the Director of Undergraduate Studies, and students should write to [email protected] to initiate this conversation.

Graduate Courses

Graduate courses may be taken for the purpose of scholarly enrichment, and ORF graduate courses will apply as departmental electives.  To enroll, one must complete the “Permission for Undergraduates to Enroll in Graduate Courses” form with signatures from the course instructor, and either the academic adviser or the director of undergraduate studies.  The completed form is submitted to the Office of the Registrar during open enrollment period.  Graduate courses will not count toward departmental GPA computations. The necessary form may be found via the Registrar.

ABET Accredited Engineering Degrees

The Department of Operations Research and Financial Engineering does not offer ABET-accredited engineering degrees.  This has never been a problem for ORFE majors in their pursuit of post-graduate opportunities, and our B.S.E. degrees are fully accredited under Princeton University’s overall accreditation. If, however, it is important that you major in an ABET-accredited program of study (e.g. for eligibility for an outside scholarship), please take this into account when choosing your major.  Further information on which Princeton program of study are ABET-accredited, please consult the Undergraduate Announcement.

Academic Program Details

SEAS Requirements

MAT 103, 104; COS 126; CHM 201 or 207; PHY 103 or 105, 104 or 106; MAT 201 or 203, 202 or 204 or 217

ORFE Requirements

Core Program

ORF 245 Fundamentals of Statistics**

A first introduction to probability and statistics. This course will provide background to understand and produce rigorous statistical analysis including estimation, confidence intervals, hypothesis testing and regression. Applicability and limitations of these methods will be illustrated in the light of modern data sets and manipulation of the statistical software R. Precepts are based on real data analysis. Prerequisite: MAT 201 (taken concurrently is acceptable), or equivalent.

ORF 307 Optimization

This course focuses on analytical and computational tools for optimization. We will introduce least-squares optimization with multiple objectives and constraints. We will also discuss linear optimization modeling, duality, the simplex method, degeneracy, interior point methods and network flow optimization. Finally, we will cover integer programming and branch-and-bound algorithms. A broad spectrum of real-world applications in engineering, finance and statistics is presented. Prerequisite: MAT 202 or 204. Basic computer programming knowledge, often achieved through taking COS 126, is suggested.

ORF 309 Probability and Stochastic Systems

An introduction to probability and its applications. Topics include: basic principles of probability; Lifetimes and reliability, Poisson processes; random walks; Brownian motion; branching processes; Markov chains Prerequisite: MAT 201. MAT 203, or MAT 216.

ORF 335 Introduction to Financial Mathematics (also ECO 364)

Financial Mathematics is concerned with designing and analyzing products that improve the efficiency of markets and create mechanisms for reducing risk. This course develops quantitative methods for these goals: the notions of arbitrage and risk-neutral pricing in discrete time, specific models such as Black-Scholes and Heston in continuous time, and calibration to market data. Credit derivatives, the term structure of interest rates, and robust techniques in the context of volatility options will be discussed, as well as lessons from the financial crisis. Prerequisites: MAT 104 and ORF 309.

Department Electives (10 or 11 Courses)

The departmental electives represent courses that further develop a student’s skills in mathematical modeling either by a more in-depth investigation of core methodologies, applying these skills in specific areas of application, or learning about closely related technologies.  Students must choose ten courses from the following list, with the following constraints:

• There must be at least four courses from ORFE.
• There can be no more than three courses from any one department (excluding ORFE). In the case of a cross-listed course, the home department of the course (PSY 360, e.g.), as opposed to the department with which it is cross-listed, defines the limit of three.

All 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

Students often wish to follow a theme in their selection of courses.  Below are a few possible themes:

• 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, 302; MOL 345; ORF 350, 401, 418
• Statistics: ORF 311, 350, 409, 418, 405, 467

Junior Independent Work (Optional)

Students selecting Junior Independent Work need to prepare a proposal including the following:

1. Title
2. 100 word abstract/literature. Abstract will describe the problem, why it is important, why you want to study it and what you hope to have accomplished by the end of the semester.
3. "Syllabus" containing weekly readings and work plan.  What you will focus on and what you plan to accomplish in each week of the semester.
4. Table of contents of the final report that you will prepare that will form the basis of the grade that you will receive.
5. Find a faculty member who will supervise your project.  Students are welcome to work with non-ORFE Princeton faculty. 
6. Obtain Director of Undergraduate Studies’ signature, approving your proposal. 

The proposal and signed Junior Independent Study Form must be sent to the Undergraduate Administrator ([email protected]) prior to the first day of classes.  The requirements for satisfactory completion of the study (such as reports, examinations, etc.) are set by the faculty mentor, not the Department, and the student should have a clear understanding about them when the work is begun.

Senior Independent Work

Senior Thesis (ORF 498 and ORF 499)

The Thesis begins in the spring semester in the junior year, with a selection process to ensure that students are well matched with an adviser.

Students will enroll in ORF 498, Senior Independent Research Foundations, in the fall semester of the senior year, and ORF 499, Senior Thesis, during the spring.  The research during both terms is accompanied by class time – with an emphasis on research groups – to facilitate the independent work.

Four reports are required throughout the year, leading up to the final thesis report, which is due in April.  The first report is due in September, the second in October, the third in December, and the fourth in February. The research results will be presented to faculty and fellow students at the end of the spring term. For more information, refer to the senior thesis guide.

OR

Senior Independent Project (ORF 497, Spring Semester Only)

Students will choose an adviser during junior year, spring semester, based on the usual arrangement – attending presentations by the ORFE faculty, reading the Faculty Interests Guide and meeting with individual faculty members.  Students will select a broad topic, and rank faculty according to their preferences.

A short progress report will be due by Dean’s Date of the fall semester, to make sure that the students have chosen a suitable topic and are matched up with their adviser.  The final outcome will be a report due at the end of the spring semester.  The report will fully describe the research project and its results. Typically projects will be 30 to 50 pages.   If selecting this option, students will need to take an additional 400-level ORF course, for a total of eleven electives.

Typical Course Schedule

Parentheses signify alternate schedule.

Departmental GPA, Honors and Prizes

Departmental GPA

Prior to graduation, the department will calculate each student's Departmental Grade Point Average (GPA).  The Departmental GPA is computed as the average of 15 grades.  For students choosing the Senior Thesis (ORF 498/499), the 15 grades are: ORF 498, 499, the ten highest graded qualified departmentals, plus each of the core courses except ORF 245.  To be a qualified departmental the courses must satisfy the following constraints:  must be a member of the departmental electives list in the Academic Guide, no more than three can be from one department.  Each of these courses must have been taken for a grade at Princeton (meaning no PDF).  Courses taken at other institutions will not contribute to the GPA.  If 15 graded courses meeting the criteria do not exist because some requirement was met with courses taken at other institutions, then other technical graded courses taken at Princeton will be used to calculate a GPA.

For students choosing the 1-semester Senior Independent Research option it is the same as above with an additional ORF 4XX departmental, totaling 11 departmentals.  Note, while two 200-level courses may be included among the Departmental Electives, no 200-level courses are counted in the departmental GPA (except COS 217, COS 226, and MAE 206).

A minimum 2.0 Departmental GPA is required for graduation. The departmental GPA is a significant factor but not the sole determinant of honors.  The overall academic quality of the entire ORFE class and academic performance in individual classes are also taken into account in the faculty's decision in awarding honors.

Graduation Requirement

Core Courses and Departmental Electives may not be taken on a PDF (Pass, D, Fail) basis.  Equivalent versions may be taken at other universities but require prior formal written permission.  Grades received in courses taken at other universities are not included in the computation of the departmental GPA.  A passing grade must be received in each ORFE Core Course and each Departmental Elective.   To graduate, a student must satisfy the University's 36 course requirement, the SEAS requirements for Physics, Chemistry, Math and Computer Science, ORFE's Core and Departmental Elective requirement and achieve a Departmental GPA of 2.0 or greater.

Honors

Highest Honors, High Honors and Honors are awarded by the ORFE faculty to those graduating seniors who have demonstrated the highest levels of academic achievement.  Departmental GPA is the major criterion; however, each student's overall academic record and the overall quality of the graduating class are also considered in the awarding of these honors.

Prizes

The ORFE faculty also awards a few individual prizes for specific superior achievement in a few defined categories.

Other Program Opportunities

Certificate or Minor Programs

The following certificate or minor programs complement well with ORFE and are thus popular with students:

• Minor in Environmental Studies
• Minor in Statistics and Machine Learning
• Minor in Computer Science
• Minor in Finance
• Certificate in Robotics and Intelligent Systems
• Certificate in Sustainable Energy 
• Certificate in Applied and Computational Mathematics
• Certificate in Optimization and Quantitative Decision Science (housed in ORFE, directed by Professor Ahmadi)

Taking Courses at Other Schools

Students may take courses at other schools during the summer, during a semester off, or during a year abroad.  The rules are slightly different depending on the activity.  If you are considering taking a replacement course during the summer or a semester off, you need to obtain a Transfer Course Approval Form, from The Dean of the College website, where you describe the course that is being taken, and what course at Princeton is being replaced.  This is particularly important if you want the course to count toward some requirement.  Read the form carefully.  It requires that you obtain the approval of the home department (or the professor that teaches the equivalent Princeton course) for the course.  For example, if you wish to take a replacement course for MAE 305, you should get the signature of the instructor teaching MAE 305 or the Director of Undergraduate Studies from MAE.  A replacement for an ORFE course requires the signature of the Director of Undergraduate Studies of ORFE. Approval that the course is equivalent or better than the Princeton course, that is to replace, must be obtained by the Professor teaching the Princeton course. Once received approval can be granted by the Director of Undergraduate Studies. Please provide a syllabus or at least a detailed description of the course before obtaining approval.

If you wish to take a semester or a year abroad, you need to obtain the signature of the Director of Undergraduate Studies of ORFE, in addition to the approval of the course of study committee.

Note, core courses (except for ORF 245) may not be taken at other schools without special dispensation by the Director of Undergraduate Studies.  They must be taken at Princeton and must be taken “graded”.  Departmental electives, if approved before taking, may be taken at other schools; however, the grade earned will not be included in any GPA calculation.

Faculty Research Interests

Amirali Ahmadi, Professor 

Optimization: algebraic methods in optimization, semidefinite programming, polynomial optimization.  Computational aspects of dynamics and control: Optimization-based Lyapunov theory for verification of dynamical systems.  Control-oriented learning: Learning dynamical systems from trajectory observations subject to side information.  Algorithms and complexity: Computational complexity in numerical optimization, convex relaxations in combinatorial optimization.  I am also interested in applications of these tools to semialgebraic problems in systems theory, machine learning, robotics, and economics. 

Rene A. Carmona, Professor  

Stochastic analysis, stochastic control and stochastic games, especially mean field games, reinforcement learning.  High Frequency markets, environmental finance and energy and commodity markets. 

Matias D. Cattaneo, Professor 

Econometric theory and mathematical statistics; program evaluation and treatment effects; machine learning, nonparametric and semiparametric methods; high-dimensional inference; applications to social and behavioral sciences. 

Jianqing Fan, Professor 

Research interests focus on statistical machine learning and big data and their applications in finance, social, biological science and health sciences. They include statistical theory and methods for high-dimensional statistical inference, neural networks, factor modeling, dynamic pricing, reinforcement learning, item ranking, among others. 

Boris Hanin, Assistant Professor 

I am interested in the theory of deep learning. Using tools from probability and mathematical physics I try to understand key empirical phenomena related to optimization, implicit bias, and generalization in neural networks as well as to give practical and provable prescriptions for architecture selection and hyper parameter tuning.  

Emma Hubert, Assistant Professor 

The research in Applied Mathematics I have been conducting with my co-authors during the past few years, has been motivated so far by two main words: interactions and incentives. These two terms have to be related in the context of my work to the Economics field, and in particular when focusing on the behavior of economic actors. The term interaction thus refer to interaction between consumers, workers, more generally called Agents, through their decisions, their actions, the price they pay for a service, or even through an external parameter. When looking at interactions, the aim is generally to find an equilibrium in this game between the Agents, so that no one has an interest in deviating. This so-called Nash equilibrium can be extended in the case of a continuum of Agents, and then reaches a more recent theory borrowed from Physics, the Mean-Field Games. The term incentives also have an economic content, and refer to the Contract Theory, and more precisely to Principal-Agents problems. In this case, an Agent (he) is delegated to act on behalf of a Principal (she). The Principal's purpose is to find appropriate incentives, in the form of a contract, to encourage the Agent to act in her interest. When considering these issues from the mathematical point of view, the behavior of Agents, in an uncertain environment and in continuous-time, can be modelled as a stochastic control problem. My research so far is thus a continuous path oscillating between Principal-Agent problems, Nash equilibria and Mean-Field Games, using recently introduced and state of the art tools in stochastic control. We study applications to Energy, Epidemiology and Finance. 

Jason Klusowski, Assistant Professor 

I am broadly interested in statistical and algorithmic problems that arise in modern data applications. Specifically, my research seeks to describe the tension between flexibility, computability, and accuracy of large-scale predictive models, such as decision trees and neural networks.

Alain L. Kornhauser, Professor 

Development and application of operations research and other analytical techniques in various aspects of Autonomous Vehicles, aka "SmartDrivingCars", including  

• The fundamental design of computer vision techniques for for the rapid classification and identification of the driving environment, especially “deep learning convolutional neural networks”, 
• Analysis and classification of collision-free driving scenarios,  
• Quantification of accident risk and the investigation, formulation and design of "pay-as-you-drive, pay-as-the-car-drives" insurance,  
• Investigation and creative design of the human-computer interfaces for SmartDrivingCars 
• Operational and feasibility analyses of autonomousTaxi (aTaxi) systems  

Sanjeev Kulkarni, Professor 

Statistical pattern recognition, nonparametric estimation, machine learning, applied probability, information theory, signal and image processing, blockchain and cryptocurrencies 

William A. Massey, Professor  

Theory and applications related to the efficient design of resource sharing services and systems. Motivating examples include communication networks, transportation services, and healthcare systems. Relevant technical topics include the theory of queues as well as Markov processes with time varying rates, stochastic networks, the optimal control of dynamical systems, and Monte-Carlo simulation. 

John M. Mulvey, Professor 

Expert on optimization under uncertainty, with emphasis on financial planning applications.  Develops strategic planning systems for financial organizations, such as American Express, Towers Perrin - Tillinghast, Merrill Lynch and Siemens. Interested in the optimization of large organizations by means of decentralized optimization methods.  Current research involves optimizing multi-strategy hedge funds.  Apply novel methods in machine learning to financial planning systems.   

Liza Rebrova, Assistant Professor 

High-dimensional probability, matrix and tensor methods, randomized algorithms in linear algebra and optimization, compressive sensing, non-asymptotic random matrix theory, mathematics of data. 

Ronnie Sircar, Professor 

Financial Mathematics & Engineering; stochastic models, especially for market volatility; optimal investment and hedging strategies; analysis of financial data; credit risk; dynamic game theory and oligopoly models; energy and commodities markets; reliability of the electricity grid under increased use of solar and wind technologies. 

Mete Soner, Professor  

Mathematical theory of optimal control and decisions under uncertainty, and applications of stochastic optimization techniques in economics, financial economics and quantitative finance, and high-dimensional computational problems.   

Bartolomeo Stellato, Assistant Professor 

Data-driven theory and computational tools for mathematical optimization, machine learning, and optimal control. Applications include real-time control of fast dynamical systems, transportation, finance, robotics, and autonomous vehicles. 

Ludovic Tangpi, Associate Professor 

Financial mathematics (risk management, model uncertainty, optimal investment); Stochastic analysis; stochastic control, Probability theory (optimal transportation, functional inequalities); Statistical and numerical methods in finance (data analysis, numerical simulations). 

Ramon van Handel, Associate Professor (on sabbatical in AY2024-25) 

My primary interests lie in several areas of (mostly pure) mathematics: probability theory, analysis, geometry, and their interactions. I am particularly fascinated by the development of principles and methods that explain the common structure in a variety of pure and applied mathematical problems. 

Research and Teaching Studios 

Students in the department often participate in the research interests of the faculty, and may take advantage of the facilities used for this work.  This includes:

• The Financial Econometrics Studio works on a variety of quantitative problems from finance using statistical techniques and financial economic theory.  These include valuation of financial derivatives, optimal portfolio allocation, risk modeling and management, volatility estimation, modeling and analysis of financial data, and simulation of financial system.  The studio also studies financial econometrics theory and provides various fundamental insights into the statistical inference problems from financial markets. 
• The Statistics Studio studies statistics theory and methods with focus on high-dimensional statistics, biostatistics, nonparametric techniques, and large-scale statistical computing.  The studio engages cutting-edge research on statistical modeling from various problems in machine learning, biomedical studies, computational biology, analysis of Big Data, network data, and financial data.  It designs statistical methods and algorithms for these modeling issues and provides fundamental understanding and theoretical foundation on statistical and computational efficiency of these methods and algorithms.  This expands into other scientific frontiers where the statistics discipline is useful. 
• The Transportation Center conducts research on information and decision engineering technologies and how these technologies can be used to improve transportation related decision making. 

Career Paths

Updates from ORFE Graduates

Meghan Fehlig ‘02

I'm working as a transportation engineer in the transportation planning department at Parsons Brinckerhoff (Princeton, NJ office). I became interested in this field after taking Prof. Kornhauser's transportation course and doing senior thesis research on the traffic woes of the Route 1 Corridor. Rarely a day goes by that I don't rely on something I learned during my thesis research or ORFE courses, be it traffic condition analysis, general research skills, MS Excel work, or NJ transportation history. A real bonus about being in this field with an ORFE degree is having a solid background in economics. Many of the exciting transportation innovations are market-based solutions and my ORFE studies have uniquely positioned me to work on these innovations with a different approach from my civil engineer and urban planner colleagues.

Daniel Nash ‘03

I am a Director of new Growth Platforms at Avery Dennison, a $6.5 billion dollar manufacturing and consumer products company with expertise in pressure-sensitive technology, and retail branding and information solutions. As such, I am responsible for identifying and building new growth opportunities for the company. For the past year, I have focused on the healthcare industry, working to leverage Avery Dennison’s RFID technology to improve key processes in hospitals. Prior to Avery Dennison, I worked as a consultant for the Boston Consulting Group serving in both the Singapore and Boston office, and in the Corporate Finance department of The Walt Disney Company. I received my MBA from Harvard Business School in 2008.

My ORFE education continues to provide a significant advantage relative to my peers. The ORFE department taught me how to quickly identify the key drivers of a new market or successful product launch, so that I can efficiently validate my assumptions and limit my downside risk. I believe there is no better education for a successful career in business.

Katherine Milkman ‘04

After completing my ORFE degree, I received a Ph.D. from Harvard University's joint doctoral program in Computer Science and Business. I am now an assistant professor at the Wharton School at the University of Pennsylvania where I teach undergraduates and MBAs about decision making and behavioral economics. When I’m not in the classroom, I spend my time on research that documents

various ways in which individuals systematically deviate from making optimal choices. I am particularly interested in understanding what factors lead people to undersave for retirement, exercise too little, eat too much junk food, and watch too many lowbrow films. Recently, I have also begun to study race and gender discrimination, focusing on how a decision's context can alter the manifestation of bias. I have published articles in leading academic journals such as Management Science and the Proceedings of the National Academy of Sciences, and my work has been featured by numerous media outlets including The New York Times, BusinessWeek, The Economist, and NPR. In 2011, I was recognized as one of the top 40 business school professors under 40 by Poets and Quants.

Kimberly Mattson ‘05

I am an Investment Associate at Bridgewater Associates, an institutional hedge fund manager in Westport, CT. We deal with large institutions - pension funds, college endowments, central banks. Most of my work revolves around solving problems that deal with portfolio structuring. I use a lot of the concepts I learned in ORFE in my day-to-day work. I use a lot of the portfolio math I learned in ORF 435 to figure out a portfolio's risk and return characteristics. We also have a tool that calculates a client's optimal portfolio using a Monte Carlo simulation process. I've even used some of the ideas I developed in my senior thesis, which used implied volatility in currency options to estimate the chance of a currency crisis, in my job, since Bridgewater manages currencies and options as part of its investment strategy. In my opinion an ORFE degree is the most flexible major to have at Princeton because it teaches a wide array of skills to apply basically anywhere after college.

Jacqueline Ng ‘06

Since graduating as an ORFE in 2006, I have worked in a variety of roles that have drawn upon the knowledge and skills I gained as an ORF major. After graduation, I spent nearly 6 years at Morgan Stanley, where I worked in US Government Bond Trading, Sales & Trading Strategy and US Equity Research. During my time at Morgan, I also pursued an executive- MBA at Columbia University. I recently left Morgan Stanley to work for Microsoft in a strategy and finance type position for their consumer advertising support business.

My undergraduate degree in ORFE has aided me tremendously in each one of my job functions. At a high level, my ORF classes taught me how to think quantitatively and analytically – which has been a versatile skill that has helped me grasp new concepts quickly in the workplace. The vigorous ORFE curriculum prepared me well for the challenges of the workplace, and has given me confidence when faced with complex problems.

On a more specific level, I find myself constantly drawing upon the knowledge I gained in my ORFE classes. For example, Professor Sircar’s ORFE 335 class (Introduction to Financial Engineering) taught me about various fixed income products and pricing models. As a bond trader, I frequently used similar pricing models on a day-to-day basis, in addition to various probability models, statistics and historical regressions. Additionally, my undergraduate classes prepared me well for business school, where my solid foundation in operations facilitated my understanding of our operations management MBA coursework. Finally, in my current role at Microsoft, I find that the knowledge I’ve gained as an ORF major accompanies me both directly and indirectly in every challenge and problem I face in the workplace.

Raj Hathiramani ‘07

Since graduating in 2007, I have worked in a few different roles that have drawn upon valuable concepts I learned from the ORFE program. I spent about 4 years working at Citadel Investment Group, first in quantitative research and derivatives trading and later in fundamental long/short equities, covering financial services. ORFE courses in financial engineering and asset pricing were instrumental in understanding how to value both stocks and derivatives.

After Citadel, I started working at Google, helping lead the sales strategy and financial analytics for the company's display advertising products. Prof. Carmona's class on regression and applied time series enabled me to use R and SQL to analyze and set targets for Google's revenue growth and present business insights to senior management.

Inspired by my managers at Google, I decide to pursue an MBA at Wharton, where I am currently a first year student. While at Wharton, I have also been working part-time as a portfolio consultant at First Round Capital, a seed-stage venture capital fund, conducting strategy projects for portfolio companies and assisting with diligence for the firm's potential investments.

Zachary Kurz, ‘08

I am working as an investment banking analyst in Morgan Stanley’s Mergers & Acquisitions Group. I am responsible for building financial models in Excel and creating presentations for our senior bankers to present to the Firm’s clients. I have been part of some really interesting transactions, including the recent $46 billion merger of Merck and Schering-Plough, two large pharmaceutical companies. I frequently use a lot of the math skills I learned as an ORFE major and the rigorous foundation has definitely given me a leg up on other young people at the Firm. In particular, a lot of the programming skills I acquired at Princeton have been incredibly useful. I am still unsure about where my career is headed but I am eager to further immerse myself in the financial world.

Jonathan Lange ‘09

I am an associate in the private equity group at Bain Capital. We invest in market-leading companies in a wide range of industries and try to optimize operations during our ownership. In my first year, I have evaluated several interesting potential transactions, primarily in media and technology. Prior to Bain Capital, I spent two years as an analyst in the Technology, Media, and Telecommunications investment banking group at Goldman Sachs, where we advised companies on mergers, acquisitions, IPOs, and other financing transactions. Through my roles at Goldman Sachs and Bain Capital, I have had the chance to travel to across North and South America and work directly with management teams, both as an adviser and investor. My ORFE education definitely gave me a great foundation in financial modeling and has been a huge advantage as I work through the theoretical aspect of valuation and the financial impacts of potential transactions.

Kate Hsih ‘10

At Princeton, I studied an interdisciplinary curriculum that treaded not only in ORFE courses, but also in the natural sciences, global health, and even the humanities. My goal was the develop a holistic understanding of quantitative and qualitative approaches to interpreting and solving real world problems. After graduating in 2010, I worked as a Princeton ReachOut56-81 International Fellow with Wellbody Alliance (formerly Global Action Foundation), a global health NGO focusing on sustainable community-based health interventions in the rural diamond-mining district of Sierra LEone. There, I was involved in a wide range of projects

including HIV home-based care, reproductive health peer education, social entrepreneurship in agriculture, amputee health, primary care in resource-limited environments, and more. I also learned about what it takes to operate a startup in this field and about the challenges and complexities of navigating in a developing environment with different cultural and professional norms. In addition to working in the NGO space, I became interested in ethnography in Sierra Leone, particularly on the topic of female genital cutting. This year, I am studying a Master of Science degree in Medical Anthropology at the University of Oxford prior to returning to the US for medical school.

While I have not directly applied many of the mathematical skills I learned in my ORFE core courses, my engineering training is omnipresent in managing my day-to-day operations. It shapes the way that I think, analyze, and organize my thoughts when presented with new problems or projects. Moreover, I have found that my ORFE degree and unusual academic and extracurricular background has been an asset in finding internship or employment opportunities because it allows me to bring unique insights to the table. ORFE teaches students to model real-world situations and make optimal and efficient decisions under uncertainty or with limited information - these tools are necessary in virtually any field, particularly in healthcare or global health. I encourage students to consider using their ORFE education to pursue unconventional careers, as it may open doors to greater responsibility and intellectual creativity. I am happy to connect any students interested in the healthcare, international development, or medical anthropology.

Chetan Narain ‘11

I'm working at Google on the Search Ads Quality team, which is responsible for deciding which ads to show on the search result page and thus brings in the majority of Google's revenue. The job is a blend of engineering, computer science, and mathematics, which is exactly what ORFE is all about. I've worked on projects where we try to predict how many people will click on an ad, something that requires substantial data crunching, strong mathematical analysis, and a good understanding of how people's minds works. My position is right at the confluence of finance, mathematics and computer science, and was a natural extension of the similarly multifaceted education I got in the ORFE program.

Nathan & Natalie Keys ‘12

Natalie: After graduation I worked for Liberty Mutual Insurance as an actuarial analyst. Some of my favorite projects were creating statistical models of cost- saving initiatives in the claims handling department, finding root causes of financial or operational trends and developing predictive models, and assessing profitability for several lines of business in different states.

ORF 309 and ORF 335 were directly applicable, as the material is in several of the first actuarial exams. ORF 407, 409, 467, and 411 acted as capstone courses - they gave me practice applying fundamental probability and statistics, and a level of comfort with complex systems and processes.

At the beginning of 2015 I joined a startup developing anti-collision technology for heavy vehicles. I work from a Joint Venture office in China, doing data analysis and programming.

Nathan: As an ORFE student, I drew many of my departmental electives from the Computer Science department. After graduating I started working for a small marketing analytics company in Seattle that builds and operates omni-channel marketing campaigns large retailers. My role was two-fold: first, I worked on the user interface team that developed the web applications that our clients used to set parameters on their marketing campaigns and evaluate their results through a rich reporting and data analytics portal. Second, I participated in “productionalizing” prototype code that the research team had written on our Hadoop big-data analytics platform to help generate the data that was ultimately presented in our user interface tool.

Most recently I have worked for a start-up company in China that is developing anti-collision technology for heavy vehicles. My role encompasses both directing and participating in technical product design and development. In this role I supervise a team of developers, from orienting them in our project, to defining, prioritizing, and assigning tasks to them, in addition to working alongside them as a fellow developer.

Bryton Shang ‘12

Bryton Shang '12 graduated from ORFE and after graduation started his career in algorithmic high frequency trading. Shortly after, he started his own algorithmic trading firm, and subsequently went into the tech world, co-founding multiple startups as CTO. Five years after graduation, he started Aquabyte, which uses computer vision and AI to give aquaculture farms insight into the health, growth, and sustainability of their fish. Bryton grew Aquabyte from an idea with a prototype in his bathtub to a smart camera and data platform that helps the world's fish farms understand what's happening in their fish pens. His ORFE knowledge allowed him to develop statistical models that help optimize fish growth and health over time.

In 2019, Shang was recognized as a Forbes 30 Under 30 leader in Manufacturing and Industry.

Anna Zhao ‘12

I work at the NBA League Office in the basketball operations department.

My department functions like the government of basketball, as we oversee the on-court activities, manage rule changes, and address any questions or concerns from teams. My most direct ORFE project has been when I used my foundation in probability (ORF 309!) to evaluate teams’ drafting abilities (not gonna lie, I was pretty excited about this). Most of my time has been spent on performing statistical analysis on a variety of areas, such as injuries, the NBA Draft, and the NBA Development League. Even when my job has no math involved, like stuffing All-Star Game gift bags, what I learned about operations research helps increase efficiency. I’m glad that I switched to ORFE because I learned how to approach solving complex problems in real-world situations, how to code to manipulate raw data into a useful form, and how to complete tasks efficiently.

Malavika Balachandran ‘13

I am working as an Analyst in the Securities Division at Goldman, Sachs & Co. My team is responsible for structuring derivative products for private individuals; I spend most of my day working with clients, providing them with pricing on derivative packages, pitching trade ideas that meet their investment objectives, and explaining how these complex financial products work. It was through my ORFE education,

particularly my coursework in Financial Math, that I gained a strong foundation in derivatives pricing theory, which governs the valuation of the products I structure and sell. While I am not solving stochastic calculus problems or developing pricing models every day, understanding how the underlying pricing models work and how a position's risk and price changes with respect to the input variables is extremely valuable in my job. My interest in math and computer science attracted me to the department, where I developed a strong intellectual interest in derivatives pricing and computational finance, and I am very lucky that I have the opportunity to apply my knowledge and passion in my career.

Adam Esquer ‘14

After completing my ORFE degree in 2014, I began interning with the Tampa Bay Rays almost immediately after school. I then signed on with the team in a fulltime capacity later that year. I am a member of the Rays’ Research and Development Department. Being in a division with some of the biggest market teams in the league (the Boston Red Sox and New York Yankees), it is crucial that we maintain our edge in analytics to continue to compete and succeed while only being able to sustain a payroll that is one quarter that of our competitors. The Rays have always been at the forefront when it comes to creating and applying new methods of player valuation and on-field strategy,

and it is an honor to now consider myself a part of this group. The work that we do affects everything from trades and signings to defensive alignment and other strategies on the field. There is nothing more rewarding than seeing the work that you do directly affect decisions that are being made.

Looking back, I can think of no better course of study than ORFE to prepare oneself for the sports industry. I think ORFE provides an invaluable combination of having a strong theoretical background and the technical skills to apply this more abstract understanding to real world problems. From covering applied regression to simulation, ORFE got me well ahead of where I needed to be to break into the sports industry and succeed. The truth is that an ORFE degree can be applied in any industry. ORFE is all about optimizing decision-making, and that basic idea is pertinent to any front office of any company that you will ever come across.

Michelle Scharfstein '15

Michelle Scharfstein '15 graduated from ORFE and started her career at a data analytics consulting company, Applied Predictive Technologies (APT), which has since been acquired into Mastercard's Data & Services business. At APT, Michelle ran statistical analysis and developed models to help Fortune 500 clients make better business decisions. After two years in consulting, Michelle pivoted to software engineering and joined Google where she started on one of Google's Ads products before shifting her focus to algorithmic music recommendations on YouTube and YouTube Music. In this role, Michelle develops, experiments with, and launches features that improve both the quality of music content and the way the content is surfaced for billions of YouTube users. Between the two teams at Google, Michelle also participated in a 6 month "COVID Swat Team" for the NY governor's office through a Google.org fellowship. The skills Michelle cultivated during her time in ORFE, including statistical modeling, programmatic simulation, and general analytical thinking, have helped her succeed in each of her roles since graduation.

Lydia Liu '17

Liu joined Princeton as an assistant professor in 2023. Her current research examines the theoretical foundations of machine learning and algorithmic decision-making, with a focus on societal impact and welfare.

Prior to joining Princeton she was a postdoctoral associate at Cornell University Computer Science in the Artificial Intelligence, Policy, and Practice (AIPP) initiative. Her work has be recognized with a Microsoft Ada Lovelace Fellowship, an Open Philanthropy AI Fellowship, an NUS Development Grant, and an ICML Best Paper Award.

She obtained a Ph.D. in Electrical Engineering and Computer Sciences from University of California, Berkeley and a B.S.E. in Operations Research and Financial Engineering at Princeton University.

Class of 2023 Senior Thesis Titles

• Batch Exchanges Moo-ving Forward: A Formalization and Empirical Analysis of CowSwap and Other CFMM-Based Clearing Price Auctions
• Modeling A Mixed-Integer Optimization Approach to Allocating Housing Resources for the Homeless
• Constructing Optimal Flow Networks: An Exploration Centered on the U.S. Flight Network
• Optimizing Fun: A TSP-Based Approach to Route Optimization at Disneyland
• An Analysis of Rerouting Produce Supply Chains in South African Game Reserves
• Optimizing Emergency Medical Services Systems: Integrating Hospital Congestion to Minimize Time to Treatment
• Time and Frequency Volatility Connectedness Among European Equity and Commodity Markets Since the Russian Invasion of Ukraine
• Can Bitcoin Go Green? An In-Depth Look into Crytocurrency Mining via Renewable Energy Sources
• Optimization of Solar Energy and Battery Storage Integration: Economic Benefits, Government Policies, and Emissions Reductions in New Jersey
• Are Stock Prices Really Random? Exploration into Support Vector Regression and Stock MarketRetrieval-Based Systems for Open-domain Question Answering
• Gamma Squeezing: An analysis of the market impact of institutional equity options market makers on the underlying market
• Maximizing REITurns: A Multi-Armed Bandit Approach to Optimizing Trading Strategies on Real Estate Investment Trusts
• Has Social Media given stock markets a soft side? An analysis of the influence of Twitter Sentiment on stock markets prices and volatility
• A Long/Short Framework for Modeling Earnings Beats Given Non-Normality of Analyst Estimates      
• Liquid Gold: A Multi-Faceted Quantitative Analysis of Private Equity Secondaries
• Predicting GDP Growth using the U.S. Transportation Sector: A Machine Learning Approach
• WAR in Pieces: A Bottom-Up Approach to Player Evaluation in the NBA
• Catching the Bubble: Analyzing Asset Bubbles Using Mean Field Game Simulations
• Under the Olive Trees: A Multimarket Liquidity Demand Model for Derivatives-Implied Arbitrage Spreads  
• Pricing Weather Derivatives with Deep Neural Networks in an Equilibrium Trading Model
• Predicting Speed: A Statistical Analysis of Biomechanical Features in Rowing
• Developing Tools for Identifying and Analyzing Amtrak’s Most Delayed Trains
• Emotion-Style Transfer for Music Using Deep Learning
• Computational Experiments on Some Novel Tree-based Classification Algorithms
• In Search of the Recipe for Hit Songs: Predicting Hit Songs Using Machine Learning
• Using Expected Goals to Forecast the English Premier League
• A Random Walk Approach to Quantifying Racial Segregation and Food Access in Atlanta, Georgia
• Exploring the Applications of Machine Learning Methods in Understanding the Nature of Active Listening Techniques in Predicting Therapeutic Outcomes
• Pricing American Options Through Model-Guided Machine Learning Incorporating Asset Characteristics and Market Environments
• Modeling the Added Volatility Components of ETF Structure onto their Constituent Assets via the Creation/Redemption Process
• An Analysis of Algorithm-driven Financial Planning Services
• Predicting SP500 Price Through Machine Learning and Natural Language Processing
• A Data Mining and Machine Learning Approach to Private Equity Replication in Public Markets
• A Pricing Analysis of European Cap & Trade Carbon Futures: Trading Strategies and Implications
• CS:Go On: Modeling Chained Loot Box Opening Behavior
• The Response of European Electricity Markets to Supply and Demand Disruptions in 2020-2023
• Humanitarian Consequences of Nuclear Fallout from Attacks on Missile Silos
• Acquisition Risk Management: An Analysis of Changes in Credit Risk Following Company Acquisitions
• Forecasting Optimal Oil Trade Flows with Quantified Geopolitical Risk in the Belt and Road Initiative
• Using Regression Analysis on US Tobacco and Australian Sports Gambling Markets to Provide Insight on Strategies to Recuce the Harm of US Sports Gambling
• Data Loss in Geotechnical Engineering: How to Stem the Bleeding
• A Regime-Switching Approach to Portfolio Construction
• Effects of Credit Downgrade Path on Fallen Angel Returns
• Identifying Drivers of Cyclical Returns in Semiconductor Equities: A Jump Model Approach
• Index-Based Longevity Risk Hedging Using the M7-M5 Model: Analysis on the Japanese Public Pension System
• Regime-Based Dynamic Asset Allocation
• The Enhanced Markowitz Model: A Case for Microfinance Institutions in Socially and Financially Optimized Portfolios
• A Game of Musical Darts: How Music Charts inform Revenue Predictability and Investment Choices in the Music Industry    
• The Efficiency Crisis in the Use of Nitrogen Fertilizer in Agriculture
• Spillover Effects of Localized School Choice Programs
• Hot Property: Evaluating Dubai’s Real Estate Market since 2002
• Manufacturing Misogyny: How The YouTube Recommendation Algorithm Radicalizes Young Men
• Modelling the Dynamics and Optimal Control of Schistosomiasis Transmission Under Climate Change
• A Machine Learning Approach to Diabetes Diagnosis: Evaluating Logistic Regression and SVM Models on NHANES Data
• Calling For Help: Using Queueing Theory to Model the Optimal Number of Counselors a University Should Employ
• Reefers Unite! Analyzing Coral Prices to Incentivize Land-Based Coral Aquaculture
• Option Trading Strategies to Harvest the Volatility Risk Premium
• More is Less is More: Using Autoencoders for Feature Extraction in M&A Deal Outcome Prediction
• A Decision Support System for Maximizing TSP Returns with Limited Fund Transfer Opportunities
• Leaving a Mark: Incorporating Trader Impact into Asset Allocation Models using Q-Learning
• A General Form of Opinion Dynamics for Modeling Diverse Political Networks
• An Analysis of The Correlations Between The Cryptocurrency Market And Other Major Asset Classes In The Post-Covid Era
• Quarterbacking - The Impact of Pay and Play on NFL Team Success
• Time Series Forecasting and Stochastic Optimization for the Operation of Decarbonized Power Grids
• Trading With Trees: Exploiting Market Inefficiencies With Random Forests
• Environmental Racism in the News: Analyzing Racial Biases in Minority Coverage
• What Seems to be the Problem? Stigmatizing Language in Patient Medical Notes
• Applications of Reinforcement Learning in Trade Execution
• A New Perspective on Tax-Aware Investing: Optimal Trading in a Multi-Period Portfolio Optimization Model
• Creating an Optimal Subset of Financial Statement Variables to Value Private Companies
• Identifying, Understanding, and Leveraging W-Shaped Implied Volatility in Options Markets
• Equity, Mobility, and Sustainability: Analyzing Geographic and Demographic Disparities in Urban Bikeability
• Dynamic Estimation: A Linear State Space Model for Pairs Trading Spreads in the S&P500
   

Class of 2023 Post Graduate Plans