Eric Ma
As Senior Principal Data Scientist at Moderna Eric leads the Data Science and Artificial Intelligence (Research) team to accelerate science to the speed of thought. Prior to Moderna, he was at the Novartis Institutes for Biomedical Research conducting biomedical data science research with a focus on using Bayesian statistical methods in the service of discovering medicines for patients. Prior to Novartis, he was an Insight Health Data Fellow in the summer of 2017 and defended his doctoral thesis in the Department of Biological Engineering at MIT in the spring of 2017.
Eric is also an open-source software developer and has led the development of pyjanitor, a clean API for cleaning data in Python, and nxviz, a visualization package for NetworkX. He is also on the core developer team of NetworkX and PyMC. In addition, he gives back to the community through code contributions, blogging, teaching, and writing.
His personal life motto is found in the Gospel of Luke 12:48.
Sessions
Learn to build practical LLM agents using LlamaBot and Marimo notebooks. This hands-on tutorial teaches the most important lesson in agent development: start with workflows, not technology.
We'll build a complete back-office automation system through three agents: a receipt processor that extracts data from PDFs, an invoice writer that generates documents, and a coordinator that orchestrates both. This demonstrates the fundamental pattern for agent systems—map your boring workflows first, build focused agents for specific tasks, then compose them so agents can use other agents as tools.
By the end, you'll understand how to identify workflows worth automating, build agents with decision-making loops, compose agents into larger systems, and integrate them into your own work. You'll leave with working code and confidence to automate repetitive tasks.
Prerequisites: Intermediate Python, familiarity with APIs, basic LLM understanding. Participants should have Ollama and models installed beforehand (setup instructions provided).
Materials: GitHub repository with Marimo notebooks. Setup uses Pixi for dependency management.
You're already using AI coding assistants for more than autocomplete, but are you using them effectively? This talk presents battle-tested patterns for productive collaboration with AI coding agents on real Python projects.
You'll learn a structured four-step approach: plan your changes, write tests first, let the agent build, then document what you created—iterating through this cycle multiple times. We'll explore why fast test harnesses are critical for agent productivity, how to pipe shell tools and logging output back to your agent for better context, and how custom slash-commands can automate repetitive tasks like code cleanup and style enforcement.
This session is for intermediate Python programmers who are already working with AI coding agents and want proven patterns for getting more value from the collaboration.
Takeaway: A practical framework and concrete techniques for collaborating effectively with AI coding assistants on real projects.