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The educational and research profile of the STOR PhD program is focused on the core disciplines of statistics, optimization, probability, and stochastic modeling. These disciplines have driven, and continue to drive, progress in data science and machine learning, as well as business and medical analytics. The STOR Department is one of the few in the US that brings together experts in each of these disciplines under one academic roof.

STOR offers a rigorous but flexible interdisciplinary PhD program within which students can benefit from the strength and diverse expertise of the Department’s core faculty, while also having the opportunity to interact with domain scientists and researchers working in other fields. STOR PhD students complete foundational coursework in the four core disciplines before undertaking more specialized coursework and directed dissertation research. Dissertation research is completed under the supervision of one or more faculty advisers. Research topics may lie within a single core discipline, or may span several core disciplines. Many research topics involve involve interdisciplinary research, including active collaboration with faculty and students at UNC and elsewhere.

The breadth and depth of the STOR PhD program prepares graduates for a wide variety of careers, ranging from academia to industry, and from the public to the private sector. Recent graduates have taken jobs in mathematics, statistics, IE, OR departments, and high-tech, biotech companies, and government agencies, etc.

Requirements

Courses

Completion of the PhD degree requires at least forty-five (45) semester hours of graduate coursework. To meet this requirement, students typically take fifteen three-credit courses. These can be divided into core courses and electives:

  • By the end of the Fall of the 2nd year students complete the five Core-I courses (see below) in order to get a solid foundation in all areas of the department. This is accomplished by students choosing (at least) 3 out of the 5 Core-I courses in the Fall of the first year; the remaining (at most) two Core-I courses are completed in the Fall of the second year.
  • In the spring of the first year, students take at least 3 courses including at least 2 of the Core-II courses that are in the same subject as the Core-I course taken in the Fall of year 1. The remaining course could either be another Core-II course, another course in the department or (with DGS approval) courses in other departments.
  • See the coursework timeline below for more details on course electives. Click here for full details on all course requirements.

Core I courses

Offered in Fall:

  • Probability (P)
  • Optimization (Opt)
  • Theoretical Statistics (TS)
  • Applied Statistics and Statistical Computing (AS)
  • Stochastic Modeling (SM)

Core II courses

Offered in Spring:

  • Probability II (PII)
  • Optimization II (OptII)
  • Theoretical Statistics II (TSII)
  • Applied Statistics and Statistical Computing II (ASII)
  • Stochastic Modeling II (SMII)

Other Advanced and Topics Courses

Offered in Spring and Fall across the 5 disciplines, depending on faculty availibility, demand and other factors.

In particular, the following courses of potential interest to first year students would also be offered in Spring. Examples in past years include:

  • Machine Learning
  • Simulation
  • Design and Control of Queueing Systems with Applications to Manufacturing and Health Care
  • Time Series Multivariate Analysis
  • Convex Optimization
  • Concentration inequalities and Combinatorial optimization
  • Stochastic Analysis
  • Introduction to Computational Finance
  • Stochastic Models for Financial Market Dynamics
  • Stochastic Models In Health care
  • Object Oriented Data Analysis

See advanced courses webpage for more examples.

Coursework Timeline

Year 1: Fall

Students choose at least 3 of the 5 possible Core-I courses: P,Opt,TS,AS,SM.

Year 1: Spring

In the spring of the first year, students take at least 3 courses including at least 2 of the Core-II courses that are in the same subject as the Core-I courses taken in the Fall of year 1. The third course could either be another Core II course, another course in the department or (with DGS approval) courses in other departments.

Year 2: Fall

Students are required to take the remaining (typically 2) Core-I courses that were not taken in the Fall of the first year. In addition students can take other advanced and topics courses offered in the department.

Year 2: Spring and beyond

  • By the completion of the program, students need to take in total 45 credit hours. By the end of the first year all students have completed at least 15 credit hours within the department. The remaining credit hours can be attained either via taking courses at the 600 or more advanced level in the department or by taking courses outside the department (up to nine credit hours).
  • PhD students may count up to nine credit hours of coursework outside the program towards the forty-five credit hours required for the doctoral degree. Outside coursework can come from other units at the university including Biostatistics, Business School, Mathematics, Computer Science and Economics, and can also be at Duke University or at NCSU. Coursework outside the department needs to be approved by the Graduate Curriculum Committee in order to count towards the PhD degree.
  • For a full detailed breakdown of course-requirements including details on seminar courses click here.

Dissertation

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.

Time limit: 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.

NOTE: For more information on the University’s requirements and procedures for the PhD and PhD dissertations, see the Graduate School Handbook.

Examinations

Written: Students choose 2 of the full sequences (a full sequence is a Core-I course and Core-II course in the same subject) they have completed in the first year and take a comprehensive written exam (CWE) on these two chosen sequences. A student can write a CWE in a sequence only if they have enrolled and passed both courses in that sequence in the first year. The CWE is usually taken just prior to a student’s second year of study.

Preliminary and Final Oral Examinations: Under normal circumstances, students take the Preliminary Oral Exam by the end of their third year of study. During the Preliminary Oral Examination students present a general, prospective outline of their thesis research, which may include preliminary results. The Final Oral Examination is the student’s formal thesis defense, and usually takes place during the fifth year of study.

Examples of course plans

None of the following are “set in stone” and are only meant to provide examples of what students can pursue. Paths are tailored for individual students based on their interests and ability when joining the program in discussion with DGS/graduate committee.

Focus on Probability

Example 1 (Previously part of the STAT track)

  • Fall: P, SM, TS
  • Spring: PII, SMII, TSII

Take CWEs in (P-PII) and (TS-TSII).

Example 2

  • Fall: P, SM, TS
  • Spring: PII, SMII, Functional Analysis (Math)

Take CWEs in (P-PII) and (SM-SMII).

Focus on Theoretical Statistics

Example 1 (Previously part of the STAT track)

  • Fall: P, TS, AS
  • Spring: PII, TSII, ASII

Take CWEs in (TS-TSII) and (AS-ASII).

Example 2

  • Fall: P, TS, AS
  • Spring: PII, TSII, Machine Learning (if offered).

Take CWEs in (P-PII) and (TS-TSII).

Focus on Applied Statistics and/or Machine learning

Example 1

  • Fall: P, TS, AS
  • Spring: TSII, ASII, Machine Learning (if offered).

Take CWEs in (TS-TSII) and (AS-ASII)

Example 2 (previously the machine learning track in INSTORE)

  • Fall: Opt, TS, AS
  • Spring: OptII, TSII, ASII

Take CWEs in (TS-TSII) and (AS-ASII).

Focus on Stochastic Modeling

Example 1

  • Fall: Opt, SM, AS
  • Spring: SMII, ASII, Real Analysis(Math)

Take CWEs in (SM-SMII) and (AS-ASII).

Example 2

  • Fall: Opt, SM, AS
  • Spring: OptII, SMII, Simulation

Take CWEs in (SM-SMII) and (OptI-OptII)

Example 3 (previously part of the OR track)

  • Fall: Opt, SM, TS
  • Spring: OptII, SMII, Simulation

Take CWEs in (SM-SMII) and (OptI-OptII)

Focus on Business Analytics (previously part of the INSTORE program)

Example 1

  • Fall: Opt, SM, AS
  • Spring: OptII, SMII, ASII

Take CWEs in (SM-SMII) and (AS-ASII).

Focus on Optimization

Example

  • Fall: Opt, SM, AS
  • Spring: OptII, SMII, ASII

Take CWEs in: (Opt-OptII) and (SM-SMII).