Christopher BROWN

Research Fellow

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.

Fields of interest:

• Machine Learning
• Continual Learning
• Robust Algorithms
• Quantum Machine Learning
• Decision Trees