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
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. Participants will build a working agent that can query a Zotero citation library, synthesize literature summaries, and engage in multi-turn research conversations. We will also discuss failure modes, limitations, and the role of such agents in an age of coding assistants.
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. We will also offer tutorial participants the option of one advanced topic overview, including the use of graphs alongside LLMs for knowledge retrieval, scalable alternatives to NetworkX including cuGraph, and the use of linear algebraic translation of graph problems to speed up computations.
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. Users bring their own API keys for LLM access, while all session data persists locally in IndexedDB. The visual, non-linear interface represents conversations as nodes on an infinite canvas, supporting branching, merging, and stateful exploration of data analysis workflows. This talk demonstrates how browser-based Python plus LLMs can democratize data science by removing infrastructure barriers while preserving privacy and reproducibility.