Purdue University · Computer Science

Ethan Dickey

PhD candidate in Computer Science

Ethan Dickey

I am a sixth-year PhD candidate in Computer Science at Purdue University, advised by Sabre Kais and Alex Psomas. I expect to graduate in May 2027.

My research connects quantum information with the collective behavior of classical networks. Using graph theory and spectral methods, I construct networks with prescribed quantum-like states encoded in their collective modes (eigenvectors of the adjacency matrix) and study how their connections determine what we can control and measure. My research in computing education asks how students develop independent understanding and judgment while working with generative AI. I also study the computational complexity of voting rules and games. Teaching and mentoring are a substantial part of my work as well: I lead Purdue’s competitive programming course sequence and have advised or co-advised twelve undergraduate researchers.

dickeye@purdue.edu Request my CV Google Scholar ORCID LinkedIn

Questions & directions

Research

Quantum-like information in classical networks

A large network can have collective modes (eigenvectors of the adjacency matrix) whose behavior admits a much smaller description. I study how to design networks in which these modes represent quantum-like states, then use their connections to manipulate and measure the information they encode. My work gives explicit graph constructions for prescribed states and develops readout methods that recover information from the network’s response to small changes in connectivity. These classical systems let us investigate which features of quantum states can be reproduced through network structure and what is required to use them.

I am now studying composition, dynamics, and robustness: when does a useful logical description survive imperfections, and how do leakage and spectral isolation affect what the network can do? I am also developing a review and tutorial on graph-based quantum-like information and collaborating with Gregory Scholes’s group on network dynamics.

Try the QL-bit LabSigned-graph constructionsComplex statesMeasurement & readout

Algorithms & computational social choice

I study the computational difficulty of collective decision making. With Alex Psomas and Athina Terzoglou, I proved that finding a winner under Stable Voting or Simple Stable Voting is PSPACE-complete. My work with Aidan Casey considers how the assumptions and possibilities of voting change in quantum models.

My ongoing projects examine tournament solutions, restricted voting instances, and the complexity of computational games.

Stable VotingQuantum voting

Generative AI & computing education

I study how to teach students to use generative AI while developing the judgment and problem-solving skills they need to work independently. My research combines classroom studies with the design of instructional tools, from structured AI-literacy activities and reflection on code to simulated students for teaching-assistant training.

In a randomized algorithms-course study, students earned higher homework scores when evaluating GenAI solutions, without a statistically significant difference in exam performance. This distinction motivates my interest in whether success during an AI-supported activity carries over to work beyond it. It also raises questions about instructional design: what should students do themselves (learner agency), what assistance should AI provide (instructional authority), and how should instructors oversee that interaction (oversight)?

My current work examines these design choices in educational tools, including AI-simulated students for graduate teaching-assistant preparation. Alongside this work, I study how constructing test cases helps students evaluate and understand programs.

Learning & evaluationAI-Lab studyInstructional governanceTools & materials

Recent news

Publications & preprints

Journal articles and accepted conference papers, followed by preprints, manuscripts, and poster presentations. Ongoing research is described above.

Journal articles & accepted conference papers

Preprints & manuscripts

Posters & extended abstracts

Teaching

I developed Purdue’s CS 211/311/411 competitive programming sequence beginning in Fall 2021 and have been Instructor of Record for CS 211 since Fall 2022. I coordinate the sequence, develop its curriculum and assessments, and mentor its teaching staff. CS 211 typically enrolls about 100 students each semester.

My approach starts with students learning to recognize a problem’s structure, explain a solution, and test whether it works. The sequence builds toward timed individual and team contests after students have developed a broader foundation in algorithmic problem solving. This work is described in my curriculum design paper.

Selected teaching experience
CS 211 · Competitive Programming IInstructor of Record, Fall 2022–present; curriculum development and teaching assistance, Fall 2021–Spring 2022.
CS 211 / 311 / 411Course-sequence leadership, assessment design, and TA supervision.
CS 240 · Programming in CGraduate Teaching Assistant, Summer 2026.
CS 251 · Data Structures and AlgorithmsGraduate Teaching Assistant and course development, Summer 2022. Graduate Teaching Assistant, Summer 2023.

I earned Purdue’s Graduate Certificate in Teaching and Learning in Engineering in 2023. I also coach Purdue’s ICPC teams and have supported teams at regional, North American, and World Finals competitions.

Teaching & service recognition

Mentoring

I mentor Syed Ahmed Taimoor on mathematical bounds for the effects of Bernoulli edge disorder in quantum-like networks.

I have advised twelve undergraduate researchers on computing-education projects, including the development and study of GLOW, Owlgorithm, CodeStylist, BoilerSketch, and BoilerTAI. My earlier mentoring included co-advising students with Andres Bejarano through Summer 2025 and independently advising Ashok Saravanan and Alex Siladie from Fall 2025 through Spring 2026. Since Fall 2026, I have been advising Thanh Dat Le on adversarial test design in competitive programming.

Undergraduate research mentees
ResearcherDatesProjects
Thanh Dat LeFall 2026–presentAdversarial test design in competitive programming
Ashok SaravananSummer 2025–Summer 2026GLOW
Alex SiladieSummer 2025–Summer 2026GLOW
Peter KurtoFall 2024–Spring 2025CodeStylist; Owlgorithm
Erin KramerFall 2024–Spring 2025Owlgorithm
Juliana Nieto-CardenasFall 2024–Spring 2025Owlgorithm; GoBoiler
Libra VentoFall 2024–Spring 2025CodeStylist; GoBoiler
Vivan TiwariSummer 2024–Spring 2025BoilerTAI; BoilerSketch
Anvit SinhaSpring 2024–Spring 2025BoilerTAI; BoilerSketch
Shruti GoyalFall 2023–Spring 2024BoilerTAI
Zachary SyFall 2023–Spring 2024BoilerTAI
Yashwi ThakkarSummer 2023Exploratory research

I have also advised instructors developing competitive-programming courses, including Metin Aktulga at Michigan State University and Sam Weese at the University of Cincinnati.

Service & research support

Building academic communities

I served as president of Purdue’s Computer Science Graduate Student Association from 2024 to 2026, after earlier roles on the graduate student board. I founded and chaired the inaugural Purdue CS Graduate Research Symposium in 2025 and chaired it again in 2026.

I serve on the ICPC Curriculum Committee and, since 2026, as a judge and problem writer for the ICPC Mid-Central North America Regional Contest. I also helped build Purdue’s community of instructors working with generative AI in education.

My reviewing service includes ACM Transactions on Computing Education, Computers and Education: Artificial Intelligence, SIGCSE, IEEE FIE, and IEEE EDUCON.

Support for computing-education research

With Andres Bejarano, I served as co-PI on Purdue Innovation Hub project IH-AI-23002 (2023–2025), supported by an initial $88,000 award and a $99,000 extension. This support enabled our work on generative AI in computing education and funded undergraduate research opportunities.

Research experience

My experience also includes a research internship at Blue Wave AI Labs in Summer 2026 and a quantum-computing research internship at IBM in Summer 2021.

Teaching & research resources

Materials for instructors, workshop participants, and researchers. Poster PDFs are linked with their presentations above.

GLOW: GTA training

Evaluating GenAI solutions

AI-Lab

Instructional Governance by Design

GAIDE

Interactive labs & atlases

Quantum-Like Lab

Can you break a bit built from a network? Cut and connect lines, flip phases, and compare the resulting patterns in an interactive 3D playground. A hands-on tutorial gets you started, with one- to five-bit experiments, matrix views, dynamics, and measurement tools ready when you want to go further.

Play with the lab → · Mathematical guide

Atlases

Interactive maps of voting results, quantum and classical computation, and the complexity of games. Explore connections and check the assumptions behind each result.

Voting Atlas · Quantum/Classical Atlas · Games & Complexity Atlas

Browse the atlases →

GenAI teaching tools

GLOW

Office-hour practice with AI-simulated students and rubric-based feedback for graduate teaching assistants.

Visit GLOW · Research poster

Owlgorithm

Reflection prompts that ask students to explain their code and reasoning during competitive programming. The live demo is currently offline; example inputs are available below.

Example problems & code

CodeStylist

Course-aware feedback on code style for early undergraduate programmers. The live demo is currently offline; the original Java examples remain available.

Example code

BoilerSketch

A TA-supervised workflow for generating and discussing structured diagrams in introductory computing courses. Contact me for a demonstration.

Example prompts & recorded demonstrations

BoilerTAI

Draft responses to course-forum questions, with instructional staff reviewing and editing each response before it is posted.

Recorded demonstration (MP4)

Poster templates

Horizontal Purdue poster template (PowerPoint) · Vertical Purdue poster template (PowerPoint)

Writing & outside of research

I enjoy explaining research and teaching ideas to audiences outside my own field. My blog includes classroom activities, occasional reflections, and a little satire.

Outside research, I love hiking Colorado’s fourteeners, skiing double-black-diamond terrain, and climbing outdoors. I also enjoy Sudoku and host weekly board game nights. My wife and I train our border collies, Kali and Rogue, in agility on a course we built ourselves. They have rather different opinions about toys: Kali’s defense of the tennis ball prompted a rebuttal from Rogue.