Final Presentations at DataFirst Fall 2023

• Date: Friday, December 1, 2023, 5:00-7:10pm
• Location: MHP 101

Agenda

• 5:00-5:10pm Introduction
• 5:10-5:55pm Presentation Session I (serial presentations, 5 minutes each)
  • Analyzing Open Source Software Ecosystems (pptx) (website)
  • Urban Futures Data Core
  • Aviation safety (website)
  • Bad Writing is “Fine”: Tuning an LLM to Suggest Improvements
  • Building a Platform for NFL Data Insights
  • US Public Sector Labor Market (website)
  • Fault detection in foundry processed devices
  • Learning and forgetting in neural networks
  • Forensic interviews (pptx) (movie) (website)
• 5:55-6:05pm Questions and Networking (or break)
• 6:05-6:50pm Presentation Session II (serial presentations, 5 minutes each)
  • Multilingual decipherment
  • Municipal Broadband
  • Nuclear safety
  • Paleoclimatology
  • Smart Watches
  • Understanding the Relation Between Noise and Bias in Annotated Datasets (website)
  • DataBidet (Regular Data: Quality health monitoring while you sit)
  • Federated Learning for Neuroscience (postscriptum)
  • Utilizing AI Generated Images for Object Detection and Classification
• 6:50-7:00m Questions and Networking
• 7:00-7:10pm Award presentation

Presentation Formats

There should be two versions of presenting the projects.

1. A short 5-minute slide version for the session on December 1, serving as a “teaser”
   • Format options: Powerpoint, Google slides, Keynote, …
   • Please email link to slides to ulf@isi.edu by Friday, Dec. 1, 2023, at 12:00noon
2. A fuller website version that can do much better justice to include project information along the lines listed below.
   • Please place your project website in your project directory under https://github.com/ckids-datafirst
   • Websites due on Friday, Dec. 1, 2023, at 11:59pm.

Presentation Content

A good general presentation should contain an overview of the problem and the results:

1. Title, participating faculty (name and department), participating students (name and degree program/year)
2. Motivation: why is this project important, interesting, or necessary?
3. Data and resources available: describe the work that you have done so far to understand the project, e.g., what data is available, its characteristics, what you have learned so far about the challenges of the project
4. Problem: the concrete questions that you are planning to tackle this semester
5. Approach: what is the general idea for tackling the questions/problem
6. Initial results: how you tested/will test the ideas, how would you know if you have succeeded at solving the problem
7. Discussion: summarize what you did, discuss what the results imply, how they can advance the state-of-the-art, and how they can be improved further
8. Demonstration of their website

Award Nominations

Each semester at DataFest we award students and teams for their outstanding work! Please fill out the following form to nominate students and teams for the awards:

• Mentors fill this out: https://forms.gle/duqPEW5ej9p8ecZMA
• Students, vote for the best mentor: https://forms.gle/p83nq7Wbg1c3wq4s6

Share your nominations by midnight, November 28!