Eric Ma

Through the construction of a Deep Research Agent, tutorial participants will learn the fundamental building blocks of LLM-driven applications. Starting with in-context learning and prompt design, we will progress through memory management, tool integration via the Model Context Protocol (MCP), and planning workflows.

Canvas Chat is a browser-based tool that combines Python's data science stack with large language model connectivity, enabling natural language interaction with data. Built on Pyodide, it runs entirely in the browser with no server-side computation required.

Through the use of NetworkX's API, tutorial participants will learn about the basics of graph theory and its use in applied network science. Starting with a computationally-oriented definition of a graph and its associated methods, we will progress through the following concepts: path and structure finding, visualization, and graph storage on disk.

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.

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.

In this tutorial, you will learn how to integrate Large Language Models (LLMs) directly into Python programs as thoughtfully-designed core components of the program rather than bolt-on additions. This hands-on session teaches design principles and practical techniques for incorporating LLM outputs into program control flow.

Through the use of NetworkX's API, tutorial participants will learn about the basics of graph theory and its use in applied network science. Starting with a computationally-oriented definition of a graph and its associated methods, we will progress through the following concepts: path and structure finding, visualization, and graph storage on disk.

In this talk, I will discuss how one can foster a culture of open source contributions at one's company. Based on my successes and failures as a data scientist working in the biotech space, I will describe two key ideas (fostering internal open source and articulating value to key senior leadership) as being on the critical path to generating buy-in within the organization.

In this talk, I will present LlamaBot, a Pythonic and modular set of components to build command line and backend tools that leverage large language models (LLMs). During this talk, I will showcase the core design philosophy, internal architecture and dependencies, and live demo command-line applications built using LlamaBot that use both open source and API-access-only LLMs.