Tabular Foundations
Who are we?
Have an idea? Join the Discord and tell us all about it!
Unsolved questions in the field of time series data that are of interest.
[The question, why it matters, and what is known so far. Link a paper, notebook, or write-up once there is one.]
The newest posts from the community's Substack. Want your article featured? Join the Discord and let us know!
Who are we?
Totally Tabular is an open-source community for anyone interested in machine learning on tabular data. We offer a space for experts, students, and hobbyists alike to read, discuss, and build.
We believe that great research is the result of many diverse perspectives aligned toward a common goal. Our discussion-based reading group works through papers together, ensuring that everyone is comfortable with the literature. Community interest will determine the projects, articles, and research that we take on.
Join us on Discord, sign up for the Total Tabloid, or read us on Substack. We hope to see you soon!
Staff
Xan is a graduate of NYU's Courant Institute of Mathematics, Computing, and Data Science. His areas of interests are machine learning interpretability and time series forecasting. At Simulacrum, he was a co-contributor to the development of Chronax, a project that eventually led him to Totally Tabular.
Staff
Anvesha is a product marketer and GTM strategist who’s spent her career building marketing functions from the ground up at early-stage AI startups. It was her time at Simulacrum that got her into time-series forecasting, which is what led her to help found Totally Tabular. Here, she focuses on growing, outreach, marketing, and maintaining the community so people have a real space to dig into the research together.
Advisor
Deni Goktas is the founder of Simulacrum, a company that builds foundation models for time-series forecasting. Deni holds a PhD in computer science from Brown University and was a postdoctoral researcher at Cornell Tech, with research internships at Google DeepMind and J.P. Morgan AI Research. Deni's research is at the intersection of economics and computer science, and current work focuses on AI superforecasters.