PhD in Complex Systems and Data Science

Build a deep portfolio of research on important, complicated, data-rich problems that matter.

Overview

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.

Students will receive a wide and rich training in empirical, computational, and theoretical methods for describing and understanding complex systems.

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.

The program's scope is science-wide, encompassing natural, artificial, and sociotechnical systems.

Program Goals

Our Essential Goal

To train the deeply skilled and ethical research data scientists the world needs.

Our More Detailed Goal

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.

Foundation

The PhD rests upon our Master's and our five-course Graduate Certificate.

Major Skill Sets

Team Collaboration

Being a good team member in a highly-collaborative pan-disciplinary, creative research environment that helps solve some of the biggest questions in the world.

Data Wrangling

Methods of data acquisition, storage, manipulation, and curation.

Visualization

Visualization techniques, with a potential for building high-quality web-based applications.

Mechanistic Stories

Powerful ways of identifying and extracting explanatory, mechanistic stories underlying complex systems—not just how to use black box techniques.

Step 1 Becoming the Crow

Crow

Who we're looking for to join our corvine cohort:

Prerequisites

Students must have a Bachelor's degree and preferably a Master's degree in a relevant field and prior coursework in:

  • Calculus
  • Coding (Python/R ideal but not necessary)
  • Data structures
  • Linear algebra
  • Probability and Statistics

Additional Background: 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.

Evaluation Criteria: 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.

Catch-up Courses Available

Not all three courses can be taken together. At most one of MATH 2522 or CS 2240 may be taken for graduate credit.

MATH 2522
Applied Linear Algebra
ⓘ
Solving linear systems, vectors, matrices, linear independence, vector spaces, determinants, linear transformations, eigenvalues and eigenvectors, singular value decomposition, and matrix factorizations.
CS 2240
Data Structures
ⓘ
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.
STAT 1410
Basic Statistical Methods 1
ⓘ
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.

Step 2 Your Destination

Create and defend a PhD Dissertation

Consisting of three or more peer-reviewed journal papers. There are many ways to reach this level.

Step 3 Common Core Requirements

Take the first course in each sequence (9 credits) and at least two accompanying second courses:

Option 1: Data Science

Required

Data Science I: CSYS/CS/STAT 5870

Optional

Data Science 2: CSYS/CS/STAT 6870

Option 2: Modeling

Required

Modeling Complex Systems: CSYS/CS 6020

Optional

Modeling Complex Systems 2

Option 3: Principles

Required

Principles of Complex Systems 1: CSYS/MATH 6701

Optional

Principles of Complex Systems 2: CSYS/MATH 6713

Each course scores 3 credits.

Step 4 Notch up 75 or more credits total

Minimum Requirements

  • 75 credits total of graduate study approved by your graduate studies committee
  • 30 credits minimum of research
  • 30 credits minimum of graduate coursework
  • 15 credits minimum must be graded (B- or above)

Transfer Credits and GPA

Students may transfer credits from other universities or within UVM following standard UVM policies.

Minimum GPA to graduate: 3.0

Students with two grades below B are eligible for dismissal.

Step 5 Complex Systems and Data Science Electives

View All CSDS Electives
  • Chaos, Fractals and Dynamical Systems (CSYS 5766)
  • Complex Networks (CSYS/MATH 6713)
  • Evolutionary Computation (CSYS/CS 6520)
  • Applied Artificial Neural Networks (CSYS/CEE 7920)
  • Applied Geostatistics (CSYS/STAT/CEE 7980)
  • Database Systems (CS 3040)
  • Human Computer Interaction (CS 3280)
  • Machine Learning (CS 3540)
  • Statistical Methods (STAT 3210)
  • Multivariate Analysis (STAT 5230)
  • Logistic Regression and Survival Analysis (STAT 5290)
  • Experimental Design (STAT 5310)
  • Categorical Data Analysis (STAT 5350)
  • Probability Theory (STAT 5510)
  • Statistical Theory (STAT 5610)
  • Bayesian Statistics (STAT 6300)
  • Statistical Learning (STAT/CS 3990)

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.

Step 6 Optional Elective Paths to Tailor Your PhD

Energy Systems

Domain Consultant: Mads Almassalkhi

Evolutionary Robotics

Domain Consultant: Josh Bongard

Biomedical Systems

Domain Consultant: Jason Bates

Environmental Systems

Domain Consultants: Donna Rizzo and Taylor Ricketts

Policy Systems

Domain Consultant: Asim Zia

Distributed Systems

Domain Consultant: Chris Skalka

Step 7 Five Stages to Acquire Corvid Cleverness

Stages 1-2: Coursework and Exams

  • Completion of 75 credits of coursework with sufficiently high GPA
  • 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.

Stages 3-5: Research and Dissertation

  • Presentation and defense of the dissertation proposal
  • 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.
  • Delivery of a written dissertation and oral defense of the dissertation

How to Apply

Deadline
February 15 for Fall admission
Requirements
Bachelor's degree (Master's preferred)
Relevant coursework or competency
GRE
Not Required
TOEFL
90 minimum (100 for TA funding)
Program Director
Professor Laurent Hébert-Dufresne
Apply
UVM Graduate College

Important Notes

Start Term: Students may not start in the Spring.

Faculty Advisor: We recommend prospective students identify a faculty advisor in advance.

International Students: Will need to apply well in advance taking into consideration visa processes.

Funding: Limited funding opportunities are available.