Each challenge is a chance to explore new ML/AI methods with guidance from advisors and support from your peers. You don’t need to be an expert for your selected challenge—but you do want to pick something where you can grow while still contributing meaningfully to your team. Preview the challenges published so far below. We’re posting the rest as organizers finalize them. Come to the showcase to ask questions about each challenge before you submit your application!
Minimum requirements: All MLM participants are expected to know Python, machine learning fundamentals, and GitHub (enough to collaborate). If you need to get up to speed, please complete the relevant self-paced workshop(s) prior to kickoff: Intro Python, Collaborating with GitHub Desktop, and/or Intro to Machine Learning.
Challenge Showcase: Wednesday, August 19, 4:00-5:30PM (virtual)
Each organizer presents their challenge and takes questions. This is the best way to figure out which challenge fits you, and it’s the best chance to talk to organizers directly before you apply. Recording posted afterward. Applications open at the end of the Showcase — apply by August 23 to be included in our first review; after that we admit on a rolling basis until seats fill. See the full schedule.
Subscribe to the ML+X Google Group to get the calendar invite and the recording link. Email facilitator@datascience.wisc.edu if you have any trouble joining the group.
Badger Code
Summary: This challenge supports early-stage development and validation of the SurveyResponder Python package—a tool for generating and analyzing synthetic survey data using large language models (LLMs). Participants will assess whether LLMs can produce realistic and demographically fair survey responses by comparing variation across models and personas. The focus is on designing and evaluating testing strategies, identifying bias, and comparing LLM outputs to known human response patterns. Work from this challenge may contribute to more trustworthy use of LLM-generated survey data in research.
Method areas: Inferential statistics (e.g., t-tests, ANOVA), response variability analysis, psychometric validation, LLM-based text generation.
Prerequisites: Familiarity with LLMs (e.g., via Ollama or AnywhereLLM) and basic survey research or psychometric principles is recommended. Experience with pandas and seaborn/matplotlib will be helpful for analysis and visualization.
WattBot 2026
Coming soon — see WattBot 2025 . WattBot 2026 will be very similar.
Summary: AI systems can consume vast amounts of energy and water, but reliable emissions data remains hard to find and harder to trust. In this challenge, you’ll build a retrieval-augmented generation (RAG) system that extracts credible environmental impact estimates from peer-reviewed sources. Your model must output concise, citation-backed answers—or explicitly indicate when the evidence is missing. The goal: turn scattered academic knowledge into transparent, actionable insights for researchers, engineers, and policy makers.
Method areas: Retrieval‑augmented generation (RAG) workflows using LLMs, optical character recognition (optional) to better parse figures/tables from PDFs.
Prerequisites: No prior experience with large language models (LLMs), RAG, or Hugging Face is required, but you must be willing to learn!