Noa Henig
Noa Henig holds a B.Sc. in Computer Science from the Hebrew University and a Ph.D. in Medical Sciences from the Technion. Noa is a Data Scientist specializing in Genomics, with extensive experience in biological data analysis. Her professional experience spans startups, corporations, and medical institutions in both the US and Israel. Outside of work, Noa enjoys jogging, photography, and listening to podcasts.
Session
Tab-PFN is a recently introduced transformer-based model for classification and regression on tabular
data. Published after nearly a decade without significant advances in modeling of such data, it has
generated substantial interest due to its promise of exceptional performance across diverse knowledge
domains. Developed by researchers at the Machine Learning Lab in Freiburg, Germany, Tab-PFN aims
to set a new standard for predictive modeling in structured data.
Despite its potential, Tab-PFN remains relatively unknown among data scientists in Israel. In this talk,
we will explore the principles behind Tab-PFN, demonstrate its application across real-world use cases, benchmark its performance against established "gold standard" models for tabular data and discuss
its limitations.