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It's all happening:

Our PhD program will start in the Fall of 2018.

The online application system is up and running. See below for instructions and links.

PhD in Complex Systems and Data Science


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.

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Application basics:

  • For Fall 2018, applications will be accepted rollingly.

  • Starting in 2019: Application Deadlines will be February 1 for Fall admission, October 15 for Spring admission.

  • Students should have a relevant Masters or be able to show exceptional promise.

  • Prospective students should arrange for an advisor in advance.

  • International students will need to apply well in advance taking into consideration visa processes.

  • Please apply online through UVM's Graduate College.

Program Director: Prof. Peter Dodds, Director of the Vermont Complex Systems Center.

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Contact information: Please direct inquiries to Samantha Williams, Graduate Programs Coordinator in the College of Engineering and Mathematical Sciences.

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Educational Mission:

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.

The Curious Platypus


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


Major skill sets we want students at all levels to develop:

  1. Being a good team member in a highly-collaborative pan-disciplinary, creative research environment that helps save the world.
  2. Data wrangling: Methods of data acquisition, storage, manipulation, and curation.
  3. Visualization techniques, with a potential for building high-quality web-based applications.
  4. Uncovering complex patterns and correlations in systems through data-fueled machine learning, and genetic programming.
  5. Powerful ways of identifying and extracting explanatory, mechanistic stories underlying complex systems—not just how to use black box techniques.

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Becoming the Crow:

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

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, 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.

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.

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Required background for applicants:

Students must have prior coursework or be able to establish competency in:

  1. Calculus
  2. Coding (Python/R ideal but not necessary)
  3. Data structures
  4. Linear algebra
  5. Probability and Statistics

As for our Masters, we offer three catch-up courses for students who are missing these prerequisites. However, at the PhD level, students will have to show exceptional promise if they are lacking these basics.

  1. Applied Linear Algebra (MATH 122)
  2. Data Structures (CS 124)
  3. Statistical Methods I (STAT 211)

Not all three courses can be taken together:

At most one of MATH 122 or CS 124 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.

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Catch-up course descriptions

Applied Linear Algebra (MATH 122):

Solving linear systems, vectors, matrices, linear independence, vector spaces, determinants, linear transformations, eigenvalues and eigenvectors, singular value decomposition, and matrix factorizations.

Data Structures (CS 124):

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.

Statistical Methods I (STAT 211):

Fundamental concepts for data analysis and experimental design. Descriptive and inferential statistics, including classical and nonparametric methods, regression, correlation, and analysis of variance. Statistical software.

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Extra pieces for Admission:


No. This is not a thing you need take.

International Students:

TOEFL score thresholds:

  • Minimum for admission: 90.
  • Minimum to qualify for funding in a teaching assistant position at UVM: 100.

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You're on board? Here are the paths you can take:

You're in the PhD program so you have only one destination for your research:

Create and defend a PhD Dissertation consisting of three or more peer-reviewed journal papers. There are many ways to reach this level.

Travelers of All Paths must take the Four Course Common Core (12 credits):

  • Principles of Complex Systems (CSYS/MATH 300)
  • Modeling Complex Systems (CSYS/CS 302)
  • Data Science I (STAT 287)
  • Data Science II (STAT 387)

(Each course scores 3 credits.)

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Notch up 75 or more credits total

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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

Complex Systems and Data Science Electives:

  • Chaos, Fractals and Dynamical Systems (CSYS/MATH 266)
  • Complex Networks (CSYS/MATH 303)
  • Evolutionary Computation (CSYS/CS/BIOL 352)
  • Applied Artificial Neural Networks (CSYS/CE 359)
  • Applied Geostatistics (CSYS/STAT/CE 369)
  • Database Systems (CS 204)
  • Human Computer Interaction (CS 228)
  • Machine Learning (CS 254)
  • Data Mining (CS 332)
  • Statistical Methods II (STAT 221)
  • Multivariate Analysis (STAT 223)
  • Stats for Quality & Productivity (STAT 224)
  • Applied Regression Analysis (STAT 225)
  • Logistic Regression and Survival Analysis (STAT 229)
  • Experimental Design (STAT 231)
  • Probability Theory (STAT 251)
  • Categorical Data Analysis (STAT 235)
  • Statistical Inference (STAT 241)
  • Probability Theory (STAT 251)
  • Statistical Theory (STAT 261)
  • Bayesian Statistics (STAT 330)
  • Statistical Learning (STAT/CS 295)

Two things:

  1. This course list evolves and not all courses will be offered in any given semester.
  2. Other courses (including special topics) may be approved by the CSDS Curriculum Committee.
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Optional Elective Paths to Tailor your PhD

Some framing to light the possible ways:


Design your own path with your advisor.

Biomedical Systems

Domain consultant: Jason Bates
  • CSYS/ME 312 Advanced Bioengineering Systems
  • CSYS/ME 350 Multi-Scale Modeling
  • CS/MMG 232 Methods in Bioinformatics
  • CTS 302 Quality in Health Care
  • CSYS/MATH 268 Mathematical Biology & Ecology
  • STAT/BIOS/MPBP 308 Biometrics & Applied Statistics
  • STAT/BIOS 350 Advanced methods in biostatistics
  • Other approved advanced electives in biomedical systems related areas

Distributed Systems

Domain consultant: Chris Skalka
  • CS 265 Computer Networks
  • CS 266 Network Security & Cryptography
  • CS 275 Mobile Apps and Wireless Devices
  • CS 361 Wireless Sensor Network Applications
  • Other approved advanced electives in distributed systems

Energy Systems

Domain consultants: Paul Hines and Mads Almassalkh
  • EE 215 Electric Energy Systems Analysis
  • EE 217 Smart Grid
  • CE 295 Reliability of Engineering Systems
  • EE 395 Optimization in Engineering
  • Other approved advanced electives related to energy

Environmental Systems

Domain consultants: Donna Rizzo and Taylor Ricketts
  • CSYS/STAT/CE 369 Applied Geostatistics
  • ENVS 295 Environmental Modeling and Systems Thinking
  • Geog 281 Advanced Topic: GIS & Remote Sensing
  • Geog 287 Spatial Analysis
  • NR 245 Integrating GIS & Statistics
  • NR 343 Fundamentals of Geographic Information Systems
  • Other approved advanced electives related to the environment

Evolutionary Robotics

Domain consultant: Josh Bongard
  • CS 206 Evolutionary Robotics
  • CSYS/CS 352 Evolutionary Computation
  • CSYS/CE 359 Applied Artificial Neural Networks
  • Biol 271 Evolution
  • ME 338: Advanced Dynamics
  • Other approved advanced electives related to evolutionary robotics

Policy Systems

Domain consultant: Chris Koliba and Asim Zia
  • PA 306 Policy Systems
  • PA 308 Decision Making Models
  • PA 311 Policy Analysis
  • PA 317 Systems Analysis and Strategic Management
  • PA 395 Resilient Communities: Designing at the Nexus of Food, Energy and Water Systems
  • PSYC 296 Behavioral Economics
  • Other approved advanced electives related to policy

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Summary of the Five Stages needed to acquire Corvid Cleverness

1. Completion of 75 credits of coursework with sufficiently high enough GPA.

2. Passing of the oral comprehensive exams.

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

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