Area of research
Nuclear and High Energy Physics
Research interest
Research interests include Particle physics theoretical and experimental studies, High-Energy Particle Collisions Research, Quantum Chromodynamics and Particle Interactions, and Particle Detector Development and Performance.
Greater than the Sum of its LUTs: Scaling Up LUT-based Neural Networks with AmigoLUT
Muon Collider Forum report
The Dark Energy Survey Supernova Program: Light Curves and 5 Yr Data Release
Opportunities and challenges of graph neural networks in electrical engineering
FAIR for AI: An interdisciplinary and international community building perspective
Evaluating generative models in high energy physics
Machine Learning for Particle Flow Reconstruction at CMS
The Dark Machines Anomaly Score Challenge: Benchmark Data and Model Independent Event Classification for the Large Hadron Collider
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing
Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications
Machine Learning in High Energy Physics Community White Paper