ML+Coffee — Coding Agents and More

ML+Coffee offers a supportive and casual environment to discuss ongoing ML/AI projects and share knowledge & tools across campus. Whether you're looking for advice on applying ML/AI to your data, hoping to demo a favorite tool, or interested in discussing a paper, ML+Coffee offers the perfect space. The majority of our attendees are applied practitioners from diverse fields, not AI/ML purists looking to critique. Coffee provided ☕ to keep the ideas flowing, courtesy of our sponsors.

Where: Room 1145, Discovery Building (330 N. Orchard St.).

When: Monthly on Wednesdays, 9-11am CT. Spring dates include: 2/18, 3/11, 4/8, and 5/6.

Register: https://forms.gle/DhoqBN8LHKaoPNLAA

March 11 Schedule

• 9-9:30 (Intros & Resource Sharing): The first 30-minutes are typically focused on introductions and causal share-outs about new ML/AI tools or resources folks are using.
• 9:30-10:30am (Coding Agents): Coding Agents (e.g., Claude, GitHub Copilot) represent one of the most exciting areas of AI development right now, but also come with some serious concerns (e.g., pricing, security, development off the rails). Let's talk progress, tooling options, and concerns. Come with any personal (success/horror) stories to share! We hope to help develop best practices as a community.
• 10:30-11am (Seeking Help - Khine Thant Su): I'd like help further developing a personal project where I build an earthquake data dashboard to track seismic activity in Myanmar since Jan 2025. My current pipeline includes scraping earthquake data from United States Geological Survey (USGS) API, processing it in pandas and storing it in a cloud PostgreSQL database called Neon, which feeds the data into the Streamlit app. My Streamlit app currently has two parts that take data from this PostgreSQL database: a bar graph of the number of earthquakes per month, and an interactive Folium map that shows where each earthquake occurred, along with some details about it. For next step, I'd like to explore time-series analysis or some kind of forecasting model that I could apply to this earthquake data to predict future earthquake risk. I'm looking for general advice about what kind of ML analysis might be helpful in this case. Link to my Streamlit app: myanmar-earthquakes.streamlit.app 

Have a demo, paper, or ongoing project to discuss? Join the discussion queue! forms.gle/F5LYqnY5TMiZrzXW6.  No formal presentation is required—this event prioritizes open dialogue and casual discussion over formal presentations. If helpful, you're welcome to bring a couple of slides (e.g., to share data, methods, or results). Many participants just bring a few key points or a rough overview of their work, and the conversation flows naturally from there.