What to expect
Data Science DC hosts two 30-minute technical talks with food and socializing beforehand. The first covers decision models, which take a situation and a list of options and return a probability for each option. The second covers differential privacy and what algorithmic stability says about whether learned models generalize.
Who should attend
The listings name no specific audience, though the first talk is billed as a practical introduction for data scientists. The second includes a formal definition, so it leans toward theory. A founder working with machine learning, or looking to meet the local data community, is the closest fit.
Agenda and speakers
- 5:30 PM: Doors open
- 5:30 to 6:15 PM: Food and socializing
- 6:30 to 6:45 PM: Introductions
- 6:45 to 7:15 PM: Building with Decision Models
- 7:15 to 7:45 PM: You May Not Be Stable, But Your Algorithms Should Be
- 7:45 to 8:00 PM: Wrap up
Banjo Obayomi builds with AI and runs Generative AI DC. His talk compares decision models with LLMs and classifiers, includes a live race between an LLM and a decision model, and covers when to use one, when not to, and how to test one on your own data.
Jess Sorrell is an Assistant Professor of Computer Science at Johns Hopkins University, studying machine learning theory and cryptography. Her talk introduces the formal definition of differential privacy, simple mechanisms that achieve it, and why stability notions provide generalization guarantees.
Venue and access
Tuesday, November 10, 2026, at Prefect, 2112 Pennsylvania Ave NW, Washington, DC 20037. Check in with security, then take the elevator to the 2nd floor.
How to register
The Meetup listing directs attendees to register on the Luma event page. Luma shows a free ticket with no approval required. Register with your first and last name.