Complex Systems and Data Science Ph.D.

All students must meet the Requirements for the Doctor of Philosophy Degree

Overview

The Ph.D. in Complex Systems and Data Science provides a pan-disciplinary academic training for graduate students working on complex systems problems across all quantitative sciences. While the PhD resides in the College of Engineering and Mathematical Sciences (CEMS), thereby providing a strong computational and theoretical training, the program’s scope is science-wide, encompassing natural, artificial, and sociotechnical systems. Depending on their chosen area of focus, students will work within and across research groups (potentially outside of CEMS) and be strongly connected with other students through co-location and regular student-led meetings and events. Students will be expected to generate and defend a scientifically important and socially meaningful body of work generally resulting in a minimum of three peer-reviewed journal papers and a dissertation. All students will receive a core training in empirical, computational, and theoretical methods for (1) describing and understanding complex systems thereby enabling them to, where possible, (2) predict, control, manage, and create such systems. Coursework will share a common core with the allied program Masters in Complex Systems and Data Science which include: (a) Data acquisition, storage, manipulation, and curation; visualization techniques including state- of-the-art approaches to building high quality web-based applications; (b) Finding complex patterns and correlations through, for example, machine learning; and (c) Powerful ways of hypothesizing, searching for, and extracting explanatory, mechanistic stories underlying complex systems—not just how to use black box techniques.

Specific Requirements

Requirements for Admission to Graduate Studies for the Degree of Doctor of Philosophy

A Bachelor's degree and preferably a Master's degree in a relevant field and prior coursework in computer programming, calculus, linear algebra, probability, and statistics. Training in relevant aspects of physics (e.g., statistical mechanics) will be beneficial but not required. Applicants lacking one or more of these prerequisite areas may be accepted provisionally and will be required to complete an approved program of supplementary work within their first year of study. GRE scores are not required. Applicants will be evaluated based on their potential for excellence in research, as judged from their academic background, test scores, relevant experience and letters of recommendation. We will admit students who we believe are most likely to succeed and thrive in the program.

Applicants whose native language is not English or whose formal education has been conducted in a language other than English must have a Test of English as a Second Language (TOEFL) score of 90 (Internet-based test) or above or an International English Language Testing System (IELTS) score of 6.5 or above. To be considered for financial assistantship from the university, applicants must have an iBT TOEFL score of 100 or an IELTS score of 7.0 or above.

The student’s Studies Committee (see below) may recommend to the Dean of the Graduate College that a student be dismissed from the program if they receive two or more grades below a B (3.00), a designation of U in Dissertation Research, or if the Studies Committee deems that they are not making satisfactory progress towards their degree requirements (for which they must be able to provide sufficient documentation).

Minimum Degree Requirements

Minimum Degree Requirements

The PhD will have five milestones:

  • Completion of coursework.
  • The comprehensive exams. 

  • The dissertation proposal. 

  • At least two published or accepted peer-reviewed publications prior to defending their dissertation, with a third at least in peer-review. These publications must be deemed of sufficient breadth, depth, and quality by their Graduate Studies Committee. 

  • The written dissertation and oral defense of the dissertation
Coursework:

A minimum of seventy-five credits of graduate study must be approved by the students graduate studies committee and successfully completed. All students must take a minimum of thirty (30) credits of research and thirty (30) credits of graduate coursework, of which at least fifteen must be graded and may not count towards a Master’s degree (only courses with grades of B- or above are counted towards this minimum requirement and students with two grades below B are eligible for dismissal). Students may transfer credits for other universities or within UVM following standard UVM policies. Students will need to earn a minimum 3.0 GPA to graduate.

Core courses (3 credits each):
CS 287QR: Data Science I3
or STAT 287 QR: Data Science I
CSYS 300Principles of Complex Systems3
or MATH 300 Principles of Complex Systems
CSYS 302Modeling Complex Systems3
or CS 302 Modeling Complex Systems
CS 387Data Science II3
or STAT 387 Data Science II

Students will meet their course requirements by selecting appropriate coursework under the guidance of their studies committees. It is anticipated that most students would choose a subset of courses from a variety of complex systems and data science electives, including but not limited to:

Complex Systems and Data Science Electives (3 credits each):
Dissertation Research Credits3 or 6 credits per semester
CSYS 303Complex Networks3
or MATH 303 Complex Networks
CSYS 266QR:Chaos,Fractals&Dynmcal Syst3
or MATH 266 QR:Chaos,Fractals&Dynmcal Syst
CSYS 352Evolutionary Computation3
or CS 352 Evolutionary Computation
or BIOL 352 Evolutionary Computation
CSYS 369Applied Geostatistics3
or STAT 369 Applied Geostatistics
or CE 369 Applied Geostatistics
CSYS 359Appld Artificial Neural Ntwrks1-3
or CE 359 Appld Artificial Neural Ntwrks
CS 204QR: Database Systems3
CS 254QR: Machine Learning3
CS 228QR: Human-Computer Interaction3
CS 332Data Mining3
STAT 330Bayesian Statistics3
STAT 235QR: Categorical Data Analysis3
STAT 223QR:Appld Multivariate Analysis3
STAT 225QR:Applied Regression Analysis3
STAT 229QR:Survivl/Logistic Regression3
Other advanced Complex Systems and Data Science electives approved by the PhD in Complex Systems and Data Science Curriculum Committee (including special topics)


Students who do not make satisfactory progress toward their PhD dissertation will be offered the opportunity to switch to the MS program, provided they meet the standards for the MS. 


Elective Tracks for the PhD in CSDS match those provided for the MS in CSDS:

  • CSDS: Energy Systems

  • CSDS: Policy Systems

  • CSDS: Biomedical Systems

  • CSDS: Evolutionary Robotics

  • CSDS: Environmental Systems

  • CSDS: Transportation Systems

  • CSDS: Distributed Systems Track

  • CSDS: Self-designed named disciplinary track (requires approval of the CSDS curriculum committee) 

Concentration Track Electives:

Track Electives are considered relatively flexible and may be updated on a semester by semester basis, based on current course offerings and content and availability and may include special topics. See the Center’s website for current offerings. Track electives applied toward the MS in CSDS must be approved by the CSDS graduate coordinator.

Comprehensive Examination

Students will be tested via an extensive oral examination involving three faculty, one of whom should be their advisor. Material will cover the four core courses and/or curriculum committee approved content.

Requirements for Advancement to Candidacy for the Degree of Doctor of Philosophy

Successful completion of the comprehensive exam and all required coursework.