About the challenge

A team sprint for Cornell Systems Engineering students. Your team picks a prompt, invents the data, and ships a working quality-control tool.

Who: Cornell students in the Systems Engineering MEng/MS program or enrolled in SYSEN 5300 / MAE 5390 or SYSEN 5900 — up to 5 per team
Where: Upson Hall 116
Submissions: on Devpost — the event page and submission instructions are shared at the event
On-Campus Version: 24 hours, October 16, 3PM - October 17, 3PM
- required for on-campus students, unless excused
Virtual DL Challenge: 7 days, October 16, 3 PM - October 23, 3 PM
- required for distance learning students and any others who cannot attend in person.

You will tackle one real-world quality control and reliability problem, drawn from industry, healthcare, energy, or infrastructure.

🧩 Prompts are released at kickoff — each tied to a dataset you design and build yourself
📊 The statistics are the graded core. Statistical process control, process capability, reliability modeling, failure analysis. The app is the delivery vehicle for the analysis, not the point.
🧱 Ship a dashboard / web app (React, Shiny, etc.) that answers the prompt, and anything you need to make it work (eg. an R or Python library, a public REST API, a design scheme, etc.)
🚀 Deploy it live to the course Posit Connect server — publisher credentials are handed out at the event
🔢 Every project is scored 0-100 by the event staff. Read the criteria
🏆 Top team wins a prize and bragging rights

Get Started

🗓️ Schedule of Events (24 hour event)
💬 Prompts — revealed at the start of the event
🔢 Evaluation Criteria
📚 Resources for Building Your Tool
🧰 Starter Templates
🤖 Agent context bundle — read this before you start building
⁉️ FAQ

👥 How to Join

Form a team (max 5) and register through DevPost. No team? Sign up anyway and we'll match you.

Requirements

What to Build

🤖 Build with AI — bring your own agent

AI-assisted development is expected and encouraged. Use whatever you already have: Claude Code, Cursor, Copilot, Codex, Gemini CLI. The skill being tested is steering a capable assistant toward statistically correct work — which is exactly the skill this course is about.

This repo ships an agent context bundle so your assistant starts oriented!

Hackathon Sponsors

Prizes

2 non-cash prizes

On-Campus Hackathon Winner Dinner

1 winner

Dinner at the Statler Dining Room for your Team.
Eligible only for on-campus students!

Distance Learning Hackathon Winner Bragging Rights

1 winner

Infamy for all time

Judges

Timothy Fraser
Asst Teaching Professor, Systems Engineering, Cornell University

Judging Criteria

Effective, working tool
(25 pts) does it run?
Performs valid analysis
(25 pts) Performs valid analyses relevant to quality control in R or Python
Scope Match
(5 pts) Scope of tool/product closely matches one of the prompts.
Clear Use Case
(5 pts) The product should have a clear user and use case in mind.
Clear, Minimal Requirements
(5 pts) Requires a minimal, reasonable number of inputs or requirements from the user/customer. For example, requiring the customer to know traits and values about their product that they are unlikely to be able to measure should be avoided.
Demonstration
(10 pts) A clear demonstration of the tool, using test datasets.
Documentation
(10 pts) Excellent, easy-to-follow documentation for the tool - how does each function work, what are the inputs, parameters, etc.
Test Datasets
(5 pts) 2-3 accurate, working test datasets
Dataset Explainers
(5 pts) A codebook and README for your datasets, describing what each file and variable means, and any other background information necessary for collecting this data.
Reproducibility
(5 pts) Fully reproducible code, posted publicly to a Github Repository,