1. Our PhD in Complex Systems and Data Science (CSDS) enables students to build a deep portfolio of research on important, complicated, data-rich problems that matter.
2. Students will receive a wide and rich training in empirical, computational, and theoretical methods for describing and understanding complex systems.
3. Depending on their chosen area of focus, students will work within and across research groups, and be strongly connected with other students through co-location and regular student-led meetings and events.
4. The program’s scope is science-wide, encompassing natural, artificial, and sociotechnical systems.
To train the deeply skilled and ethical research data scientists the world needs.
Our PhD is for students who want to develop a potent portfolio of scientific research into data-rich problems. We want to make this happen. And as for our Masters, we provide students with a broad training in computational and theoretical techniques for (1) describing and understanding complex natural and sociotechnical systems, enabling them to then, as possible, (2) predict, control, manage, and create such systems.
The PhD rests upon our Master's and our five-course Graduate Certificate.
#scaffolding
Students must have a Bachelor's degree and preferably a Master's degree in a relevant field and prior coursework in computer programming, calculus, linear algebra, statistics and/or probability.
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.
Applicants will be evaluated based on their potential for excellence in research, as judged from their academic background, test scores, relevant experience and three (3) letters of recommendation. We will admit students who we believe are most likely to succeed and thrive in the program.
At most one of MATH 2522 or CS 2240 may be taken for graduate credit. Students must also submit a form for pre-approval from the Graduate College at least 1 month before the semester in which they take the course.
Solving linear systems, vectors, matrices, linear independence, vector spaces, determinants, linear transformations, eigenvalues and eigenvectors, singular value decomposition, and matrix factorizations.
Design and implementation of linear structures, trees and graphs. Examples of common algorithmic paradigms. Theoretical and empirical complexity analysis. Sorting, searching, and basic graph algorithms.
Foundational course for students taking further quantitative courses. Exploratory data analysis, probability distributions, estimation, hypothesis testing. Introductory regression, experimentation, contingency tables, and nonparametrics. Computer software used.
No. This is not a thing you need take.
TOEFL score thresholds:
Create and defend a PhD Dissertation consisting of three or more peer-reviewed journal papers. There are many ways to reach this level.
(Each course scores 3 credits.)
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 from other universities or within UVM following standard UVM policies. Students will need to earn a minimum 3.0 GPA to graduate
This course list evolves and not all courses will be offered in any given semester. Other courses (including special topics) may be approved by the CSDS Curriculum Committee.
1. Completion of 75 credits of coursework with sufficiently high enough GPA.
2. Passing of the written comprehensive exams: PhD students sit for a triplet of 1 hour written exams (in 3 subjects) on topics decided upon in consultation with their research advisor(s). For example, the exams might cover the content found in CSYS 6701, CSYS 6020, and STAT 6870. Students need not have taken all core classes prior to sitting for a particular exam, and the exams need not be taken on the same day. A follow-up oral exam could be scheduled to address material the student failed to demonstrate mastery of during the written exam.
3. Presentation and defense* of the dissertation proposal.
4. Generation of 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.
5. Delivery of a written dissertation and oral defense* of the dissertation
*Defense Timetable for Thesis/Dissertation Students
