Machine learning has played an important role in particle physics for simulation, reconstruction, and analysis for decades. The emergence of deep learning and new heterogeneous computing paradigms are transforming almost every aspect of the software in particle physics into Machine Learning approaches. I will provide an overview of how machine learning is used in the LHC experiments, followed by an introduction to the core concepts of deep learning, connections between deep learning and LHC data analysis, examples of key results, deployment of deep learning in particle physics computing platforms, and discussion of future prospects and concerns. I will demonstrate that the acceleration of machine learning inference as a web service represents a heterogeneous computing solution for particle physics experiments that potentially requires minimal modification to the current computing model. Our code and data are publicly available at https://fastmachinelearning.org .

Bio：Shih-Chieh Hsu is an Associate Professor in the Physics department at the University of Washington Seattle. He received his PhD (2003-2008) from the University of California San Diego after he earned a bachelor degree (1995-1999) and a master degree (2000) in Taiwan University, all in Physics. He worked as a Chamberlain Fellow (2008–2012) at the Lawrence Berkeley National Laboratory, joined the faculty at the University of Washington in Fall 2012. His primary research subjects focus on using the Large Hadron Collider data with the ATLAS and FASER detector for searches of Beyond the Standard Model physics, e.g. dark matter associated production with Standard Model particles and weakly-interacting long-lived particles. His interests include tracking algorithm, fast read-out software for the ATLAS (Phase II) pixel detector, and machine learning inference acceleration in accelerated computing. Professor Hsu was named a recipient of a 2016 U.S. Department of Energy Early Career Award.