Eric Ma
As 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
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. Finally, I will conclude with a roadmap for LlamaBot development, and an invitation to contribute and shape its development during the Sprints.
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 build out into progressively more advanced concepts (path and structure finding, and graph theory's relation to linear algebra), as well as an overview of scalable alternatives to NetworkX.