Head of Machine Learning, Abnormal Security
Friday, November 11, 2022 at 2:00 PM

CIT - Room 368 and virtual via Zoom: https://brown.zoom.us/j/92823661643
Add to calendar

BigAI Talk: "Resilient Machine Learning"

The real world is messy. Systems fail, pipelines break, services go down, engineers push bugs, and users behave erratically. Software is hard exactly because these problems always happen. Effective systems must gracefully handle these events and smoothly degrade without catastrophic failure.

Unfortunately, ML systems are more likely to break than bend. Just like a boxer who only punches a bag will fail in the ring, an ML model that only learns with clean data may fail in production. Most ML models are trained with clean data, and when failures occur feature distributions can shift in ways that the model has never seen during training. This can cause strange and unexpected behavior.

In this talk we will explore how to build resilience into ML systems. We will discuss several types of production-specific risks and how these risks tend to manifest. These risks are common across many domains, but we will primarily use examples from our experience at Abnormal Security to demonstrate how we can detect, mitigate, and overcome these risks.

Dan Shiebler works as the Head of Machine Learning at Abnormal Security, where he builds cybercrime detection systems to keep people and businesses safe. Cybercrime is always changing as attackers innovate, and the detection organization at Abnormal Security must stay ahead of these innovations. As the Head of Machine Learning, Dan works to optimize the detection organization's ability to anticipate these innovations, iterate and improve. Before joining Abnormal Dan worked at Twitter: first as an ML researcher working on recommendation systems, and then as the engineering manager for the web ads machine learning team. Before Twitter Dan built smartphone sensor algorithms at TrueMotion and Computer Vision systems at the Serre Lab. Dan's PhD at the University of Oxford focused on the applications of Category Theory to Machine Learning.Dan is a 2015 Computer Science graduate of Brown University.

Host: Professor Suresh Venkatasubramanian