Model Blind Spot Discovery for Better Models

Pavol earned his PhD at ETH Zurich, specializing in machine learning, symbolic AI, synthesis, and programming languages. His groundbreaking research earned him the prestigious Facebook Fellowship in 2017, representing the sole European recipient, along with the Romberg Grant in 2016.

Following his doctorate, Pavol's passion for ensuring the safety and reliability of deep learning models led to the founding of LatticeFlow. Building on a more than a decade of research, Pavol and a dynamic team of researchers at LatticeFlow developed a platform that equips companies with the tools to deliver robust and high-performance AI models, utilizing automatic diagnosis and improvement of data and models.

David is originally from Los Angeles and has been passionate about building things from an early age. He went to college at the University of Southern California (USC) where I studied Math & Economics, and did his Masters in Computer Science at the University of St. Andrews, where he was a computer vision researcher. After David's Masters, He did both Fullstack and Infrastructure Software Development at companies like SpaceX and Affirm before transitioning back into AI this year.

While consulting for a legal tech startup, Fileread AI, David discovered the problem of building a high-volume, reliable vector embedding pipeline to connect raw data to LLMs. David launched VectorFlow two months ago with his friend Dan to solve this problem, so developers can focus on building real AI applications and not data engineering or infrastructure.

Ben was the machine learning lead for Splice Machine, leading the development of their MLOps platform and Feature Store. He is now a founding software engineer at Galileo (rungalileo.io) focused on building data discovery and data quality tooling for machine learning teams. Ben also works as an adjunct professor at Washington University in St. Louis teaching concepts in cloud computing and big data analytics.