General Information

The Doctor of Philosophy program prepares students to pursue careers in research and/or teaching in multidisciplinary that utilize the tools of statistics and operations research augmented by other quantitative tools. The program currently offers three specialized tracks:

1. Machine Learning,
2. Business Analytics,
3. Computational Finance.

Students must choose a track to enroll in at the beginning of their first semester. Students enrolled in a track follow a customized set of courses designed to provide a deeper theoretical understanding of the subject matter of their chosen track. If needed, a student can petition to get a specific set of courses approved as a personalized track. However, this will require approval by the advisor and the faculty of the department. Under special circumstances, a student may be allowed to change tracks. However such a change must be approved by the graduate advisor, and can result in a delay in the completion of degree requirements. The course requirements for each track are described later.


Requirements

The University of North Carolina at Chapel Hill imposes general requirements on all candidates for the Doctor of Philosophy Degree. These requirements are set forth in the Record of The University of North Carolina at Chapel Hill – the Graduate School. Each track in INSTORE has a specified six courses that each student in the track must take in the first year. In addition there are two required courses and seven elective courses that the student must complete to satisfy the course requirements of the degree program. Note that the students must secure permission form the instructor when enrolling in a non-STOR course.


Track I: Machine Learning

Brief Description:

(The earlier track called Applied Statistics and Optimization has evolved into the new Machine Learning track to reflect the recent trends in the field.) Statistics can be viewed as the science of managing uncertainty.  Classical statistical approaches are based on probability theory, and quantification of uncertainty via probability distributions.  Machine learning has arisen as an alternative approach to the same set of data analytic challenges, which was originally much more based purely on the application of optimization methods.  As both fields have developed, there have been fruitful exchanges in both directions, and several of our faculty members are regarded as major players in this process.  The INSTOR Machine Learning Track is aimed at training students in the skills needed to work at this exciting, and rapidly developing, interface between these two fields.  This is done through an integration of both Statistics and Optimization courses starting in the first year, so that students have the opportunity to become immersed in both fields in a deep way, early in their PhD coursework.  The UNC STOR Department is uniquely capable of doing this among American Universities, because both fields are in the same department, and because we have world leading innovators in both areas on our faculty.

The students enrolled in this track will take the following six courses in the first two semesters:

First Fall Semester

1. STOR 612: Deterministic Models in Operations Research I

2. STOR 654: Statistical Theory I

3. STOR 664: Applied Statistics I

First Spring Semester

4. STOR 614: Deterministic Models in Operations Research II

5. STOR 655: Statistical Theory II

6. STOR 665: Applied Statistics II

In addition the students in this track must pass the following courses

7. One of STOR 712/722/724 (Nonlinear Programming, Integer Programming, Networks)

8. ECON 871: Time Series and Forecasting (or an equivalent STOR course when offered)

9. STOR 89x: Machine learning (offered every other year)

10. STOR 790: 1-credit presentation course

 

Students must take a total of seven additional electives, out of which at least four must be STOR courses at level 600 or above.

Recommended Electives:

  • STOR 641: Stochastic Models in Operations research I
  • STOR 642: Stochastic Models in Operations research II
  • STOR 634: Measure and Integration
  • STOR 635: Probability Theory
  • STOR 743: Stochastic Models in Operations Research III
  • STOR 762: Discrete Event Simulation
  • STOR 712: Non-linear Programming (when offered)
  • STOR 722: Integer Programming (when offered)
  • STOR 724: Networks (when offered)
  • STOR 756: Design of Experiments (when offered)
  • STOR 757: Bayesian Statistics (when offered)
  • STOR 705: Practicum
  • STOR 994: Doctoral Dissertation

Total hours required for degree: 50


Track II: Business Analytics

Brief Description:

Decision making by businesses and organizations is increasingly driven by data. Examples are found in revenue management, marketing, fraud detection, supply chain management, health care management, disease control, traffic congestion control etc. Organizations collect large amounts of data and use it to make intelligent decisions. This requires strong mathematical modeling skills as well as solid background in statistical data analysis and optimization. UNC STOR department offers a unique combination of courses that a student can take to build expertise in these areas. In addition, students can augment these courses by selecting appropriate courses from the Business School, School of Public Health, the Economics department, and the Computer Science department. This allows our students to specialize in a particular area of interest in addition to getting strong methodological training. It is possible for the students to conduct research under the joint guidance of advisors from our department and from these other departments.

The students enrolled in this track are expected to take the following six courses in the first two semesters:

First Fall Semester

1.  STOR 612: Deterministic Models in Operations Research I

2.  STOR 641: Stochastic Models in Operations Research I

3.  STOR 664: Applied Statistics I

First Spring Semester

4.  STOR 614: Deterministic Models in Operations Research II

5.  STOR 642: Stochastic Models in Operations Research II

6.  STOR 665: Applied Statistics II

In addition the students in this track must pass the following courses

7.  COMP 790-90: Data Mining – Concepts, Algorithms, Applications (or an equivalent STOR course when offered

8.  ECON 871: Time Series and Forecasting (or an equivalent STOR course when offered)

9.  STOR 790: 1-credit presentation course

Students must take a total of seven additional electives, out of which at least four must be STOR courses at level 600 or above.

Recommended Electives:

  • STOR 743: Stochastic Models in Operations Research III
  • STOR 762: Discrete Event Simulation
  • STOR 712: Non-linear Programming (when offered)
  • STOR 722: Integer Programming (when offered)
  • STOR 724: Networks (when offered)
  • STOR 705: Practicum
  • STOR 89x: Machine learning (when offered)
  • MBA 748A: Marketing Analytics
  • BA 830: Operations Management I
  • BA 832: Operations management II
  • BA 837: Decision Making with Spreadsheets (when offered)
  • ECON 710: Advanced Microeconomic Theory I
  • Quantitative Methods in Marketing (when offered)
  • STOR 994: Doctoral Dissertation

Total hours required for degree: 50


Track III: Computational Finance

Brief Description:

Computational finance aims at solving problems in financial economics, such as asset pricing, risk analysis, interest rate modeling, market microstructure, etc., via stochastic modeling, statistical inference and scientific computation. These tasks require a rigorous training in probability and statistics. UNC STOR Department has superb expertise in those areas. Students can further augment their training by taking courses in Economics and Finance Department of Business School. It is routine for students to have joint advisors from our department along with one or more from these other departments.

The students enrolled in this track will take the following six courses in the first two semesters:

First Fall Semester

1.  STOR 634: Measure and Integration

2.  STOR 641: Stochastic Models in Operations research I

3.  STOR 664: Applied Statistics I

First Spring Semester

4.  STOR 635: Probability Theory

5.  STOR 642: Stochastic Models in Operations research II

6.  STOR 665: Applied Statistics II

In addition the students in this track must pass the following courses

7.  Busi 880: Financial Economics

8.  Busi 886: Quantitative Methods in Finance (or an equivalent STOR course when offered)

9.  STOR 790: 1-credit presentation course

Students must take a total of seven additional electives, out of which at least four must be STOR courses at level 600 or above.

Recommended Electives:

  • STOR 612: Deterministic Models in Operations Research I
  • STOR 614: Deterministic Models in Operations Research II
  • STOR 654: Statistical Theory I
  • STOR 655: Statistical Theory II
  • STOR 743: Stochastic Models in Operations Research III
  • STOR 762: Discrete Event Simulation
  • STOR 712: Non-linear Programming (when offered)
  • STOR 722: Integer Programming (when offered)
  • STOR 724: Networks (when offered)
  • STOR 705: Practicum
  • STOR 89x: Machine learning (offered every other year)
  • STOR 831: Advanced Probability
  • ECON 710: Advanced Microeconomic Theory I
  • ECON 711: Advanced Microeconomic Theory II
  • ECON 871: Time Series and Forecasting (or an equivalent STOR course when offered)
  • Busi 899: Market Microstructure (or an equivalent STOR course when offered)
  • Busi 881: Corporate Finance

STOR 994: Doctoral Dissertation

Total hours required for degree: 50


Comprehensive Written Examination:

The students take the comprehensive written exam based on the six courses taken in the first year. The examination is given in generally in the second or third week of August of each year. The purpose of the examination is to measure the student’s mastery of fundamental topics in the quantitative areas relevant to this track. After passing the examination, the student chooses the remaining coursework and a research topic suitable for a Ph.D. dissertation in consultation with a faculty advisor. There is no language requirement for the Ph.D. degree.


The Oral Examination:

Near the end of all coursework the student takes an oral examination (proposal defense) designed to reveal an in-depth understanding of the area selected for research. The examination committee is composed of the dissertation director and four other faculty members, at least three of whom are on the Operations Research Faculty. Successful completion of the examination signifies faculty approval for admission to candidacy for the doctoral degree and initiation of dissertation research.


PhD Thesis:

All PhD students must take six or more credits of STOR 994, PhD Thesis Research, and must be registered for STOR 994 during the semester that they take their Final Oral Exam (PhD Defense).  Credits for STOR 994 do not count towards the forty-five credit total needed for the PhD degree. Students develop and pursue their dissertation research under the guidance of a core member of the STOR Faculty.  In some cases, a student may be co-advised by two core faculty members, or by a core faculty member and a co-advisor from another department.  Students are expected to complete their coursework and thesis research within five years of entering the program.   The University does not provide tuition remission beyond five years.  In addition, the Department cannot provide assistantships for students after the end of their fifth year.