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by Dr Christopher Edward Brown (CERN), Dr Richard Stotz

→ Europe/Zurich
593/R-010 - Salle 11 (CERN)

593/R-010 - Salle 11

CERN

50
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Description

Abstract

This class introduces the practical application of Machine Learning, covering problem framing, model analysis, and deployment considerations. The second half explores decision forests, specifically Gradient Boosted Trees and Random Forests, with a strong focus on modern methods for fast inference

Lecturer Bio 

Richard Stotz is the Tech Lead of the Google Decision Forests team in Zurich, Switzerland. Before joining Google, he earned his PhD from the Technical University of Munich, focusing on scheduling algorithms for buffering and partitioning problems under the supervision of Prof. Harald Räcke.

Tutor's Bio 

I am a research fellow working on the CMS Level-1 trigger and design machine learning algorithms to make sub microsecond decisions whether to keep or discard incoming data. I specifically work on jet tagging algorithms and explore a range of different architectures and techniques to boost tagging performance while also reducing their inference latency and overall cost in hardware.

I am also interested in the impact of changing environments on trigger algorithms and how we can design robust machine learning or use continual learning to constantly update algorithms as the detector changes. I explore how different architectural considerations and learning techniques can change how an ML algorithm understands its training data and how that can help reduce the impact of noise in incoming data.

A further research area is in tree methods for fast machine learning, designing and implementing decision trees in firmware as well as method for updating them on the fly or self-updating learning.