Rate My Professor Alex Tapper

Professor Alexander Tapper is a Professor of Physics in the Department of Physics at Imperial College London, Faculty of Natural Sciences. He leads the Imperial College CMS research group and serves as the Principal Investigator for the CMS UK Upgrade project. Additionally, he chairs the CMS Trigger and Data Acquisition Upgrade Institute. Tapper's research specializes in experimental high energy physics, focusing on the CMS experiment at the Large Hadron Collider (LHC) to search for physics beyond the Standard Model at the highest energies. His work encompasses the development of advanced trigger systems, including custom electronics boards that enhance capabilities for dark matter searches and other new physics signatures. Tapper is also actively involved in the Deep Underground Neutrino Experiment (DUNE) and contributes to fast machine learning techniques, such as graph neural networks and FPGA-accelerated deep learning for real-time particle identification and event reconstruction in high-luminosity LHC conditions. He has pioneered embedded transformer neural networks and low-latency algorithms tailored for scientific applications in particle physics.

Tapper completed his undergraduate MPhys degree at the University of Oxford from 1993 to 1997 and earned his PhD from Imperial College London in 2001, with thesis work on high energy physics. He has remained at Imperial College throughout his career, progressing from postdoctoral researcher to Lecturer, Reader, and Professor since 2011. Tapper supervises PhD students, delivers undergraduate and postgraduate teaching in particle physics analysis procedures, collider experiments, and triggering, and has mentored numerous theses, such as those on CMS triggering and W-polarization studies. He was nominated for the Institute of Physics High Energy Particle Physics Group Prize in 2005 for contributions to the ZEUS experiment at HERA. Tapper holds professional roles including Visiting Fellow at the Durham Institute of Research, Development, and Invention, and participates in international collaborations and workshops on machine learning for science. His publications include contributions to CMS results on Higgs boson discovery, ZZ and ZH production searches, and advancements in neural network deployments for HL-LHC, such as 'Roadmap on fast machine learning for science' (2022) and 'LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics' (2024).