Evan C. Ezell

Contact Information

[he/his/him]

Email: eezell3 at tennessee dot edu

Github Username: evanezell

About

I am a Data Science and Engineering PhD Student at the University of Tennessee. I am a graduate research assistant at Oak Ridge National Laboratory in the Geospatial Artificial Intelligence Group. During my time so far, I have worked on several projects of which some are a Radon Vapor Intrusion Screening Level Risk Calculator, Toxicology and Hazard Regional Screening Level iOS application, time series comparison suite for the World SpatioTemporal Analytics and Mapping Project (WSTAMP), and analysis of the demographics of traffic data for the Urban Information System (UrbIS). I am currently working on new methods and techniques for analyzing and visualizing dynamic network data. I enjoy using big data to make impactful conclusions for unique and interesting problems.

I am a Maryville College Fighting Scots alumnus. At Maryville College, I studied computer science, was the president of the ACM chapter for two years, played varsity baseball, and served as a STEM peer mentor. I try to keep up with the latest baseball statistical analysis and perform my own analysis from time to time. Sparingly I enjoy chess, leisure reading, hiking, and exploring new cuisines.

Research Interests

• Dynamic Network Analysis and Visualization
• Spatiotemporal Time Series Analysis
• Programming Languages and Software Engineering
• Machine Learning and Statistical Artificial Intelligence

Research

Publications

• Community Fabric (October 2022)
  [Ezell E, Lim SH, Anderson D et al. Community fabric: Visualizing communities and structure in dynamic networks. Information Visualization 2022; DOI:10.1177/14738716211056036.]
• Utility-scale Building Type Assignment Using Smart Meter Data (September 2021)
  [Bass B, New J, Ezell E et al. Utility-scale building type assignment using smart meter data. In Building Simulation 2021 Conference (BuildSim 2021). International Building Performance Simulation Association. URL: https://www.osti.gov/biblio/1820853.
• Visualizing Communities and Structure in Dynamic Networks (March 2021)
  [Ezell E, Lim SH, Anderson D et al. Visualizing communities and structure in dynamic graphs. In Auber D and Valtr P (eds.) Graph Drawing and Network Visualization: 28th International Symposium, GD 2020, Lecture Notes in Computer Science, volume 12590. Springer. ISBN 9783030687663, pp. 532-534. DOI:10.1007/978-3-030-68766-3.]

Selected Coursework

Graduate Courses (University of Tennessee)

• COSC 581 - Algorithms (taught by Dr. Michael Langston in Spring of 2020)
• COSC 525 - Deep Learning (taught by Dr. Amir Sadovnik in Spring of 2020)
• COSC 560 - Software Systems (taught by Dr. Michael Jantz in Spring of 2020)
• COSC 530 - Computer Systems Organization (taught by Dr. Gregory Peterson in Fall of 2019)
• COSC 594 - Advanced Programming and Algorithms (taught by Dr. James Plank in Fall of 2019)
• COSC 690 - Evidence Engineering (taught by Dr. Audris Mockus in Spring 2019)
• BZAN 645 - Advanced Topics in Data Mining (taught by Dr. Wenjun Zhou in Spring 2019)
• STA 564 - Probability and Mathematical Statistics II (taught by Dr. Wei Zheng in Spring 2019)
• STA 563 - Probability and Mathematical Statistics I (taught by Dr. Wei Zheng in Fall 2018)
• COSC 545 - Fundamentals of Digital Archeology (taught by Dr. Audris Mockus in Fall 2018)
• COSC 528 - Intro to Machine Learning (taught by Dr. Bruce MacLennan in Fall 2018)

Undergraduate Courses (Maryville College)

• CSC 314 - Data Mining (taught by Dr. Robert Lowe in Spring 2018)
• CSC 381 - Theory of Computation (taught by Dr. Robert Lowe in Fall 2017)
• CSC 349 - Compiler Construction (taught by Mr. Lee Wittenberg in Fall 2017)
• CSC 321 - Intro to Operating Systems (taught by Dr. Robert Lowe in Spring 2017)
• CSC 312 - Algorithm Design and Analysis (taught by Mr. Randy Meyers in Spring 2017)
• MTH 221 - Inferential Statistics (taught by Dr. Olga Ebert in Fall 2016)
• CSC 241 - Data Structures (taught by Mr. Lee Wittenberg in Fall 2016)
• CSC 221 - Computer Architecture (taught by Dr. Robert Lowe in Fall 2016)
• MTH 322 - Probability and Statistics II (taught by Dr. Jeff Bay in Spring 2016)
• CSC 313 - Database Management Systems (taught by Dr. Robert Lowe in Spring 2016)
• CSC 231 - Discrete Structures (taught by Dr. Jesse Smith in Spring 2016)
• MTH 321 - Probability and Statistics I (taught by Dr. Jeff Bay in Fall 2015)
• MTH 232 - Linear Algebra (taught by Dr. Jesse Smith in Fall 2015)
• MTH 222 - Regression Analysis (taught by Dr. Jeff Bay in Fall 2015)
• CSC 251 - Graphical User Interfaces (taught by Dr. Robert Lowe in Fall 2015)
• MTH 235 - Calculus III (taught by Dr. Maria Siopsis in Spring 2015)
• CSC 112 - Intro to Computer Science II (taught by Dr. Robert Lowe in Spring 2015)
• MTH 225 - Calculus II (taught by Dr. Maria Siopsis in Fall 2014)
• CSC 111 - Intro to Computer Science (taught by Dr. Robert Lowe in Fall 2014)

For Fun

I wrote a lecture that was taught in the Advanced Programming and Algorithms course. It describes how to generate chessboard diagrams using jgraph. The writeup is here.

While an undergrad, I designed my own computer at the gate level logic and implemented it in Logisim. It's a very basic computer. You can check it out here.

Epigrams

"You need the willingness to fail all the time. You have to generate many ideas and then you have to work very hard only to discover that they don't work. And you keep doing that over and over until you find one that does work." - John Backus

"Proof by contradiction is a far finer gambit than any chess gambit: a chess player may offer the sacrifice of a pawn or even a piece, but a mathematician offers the game." - G.H. Hardy

"Computer science deals with idealized components. We know as much as we want about these little programs and data pieces we are fitting together. We don't have to worry about tolerance and that means there's not all that much difference from what I can build and what I can imagine. Because the parts are these abstract entities that I know about as much as I want and as precisely as I would like, so opposed to other kinds of engineering where the constraints upon which I can build are the constraints of physical systems, the constraints of physics and noise and approximation, the constraints imposed in building large software systems are the limitations of our own minds. So in that sense computer science is like an abstract form of engineering, it's the kind of engineering where you ignore the constraints that are imposed by reality." - Hal Abelson, MIT 6.001 Lecture 1A 1986