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Fast Machine Learning for Science Conference 2025
Fast Machine Learning for Science Conference 2025

Sep 1 – 5, 2025
ETH Zurich
Europe/Zurich timezone

Event menu

  • Overview
  • Invited Speakers
  • Timetable
  • Contribution List
  • Book of Abstracts
  • Registration
  • How to become a Sponsor
  • Poster awards
  • Practical information
  • Conference photo

Local organisers

  • fml-2025-organisers@cern.ch

Details for Philip Coleman Harris

Massachusetts Inst. of Technology (US)

Author in the following contributions

  • State space models for Project 8 event reconstruction
  • COLLIDE-2V - 750 Million Dual-View LHC Event Dataset for Low-Latency ML
  • SparsePixels: Efficient Convolution for Sparse Data on FPGAs
  • SuperSONIC: Cloud-Native Infrastructure for ML Inferencing
  • KAN-LUT: Efficient LUT-Based Acceleration of Kolomogorov-Arnold Networks (KANs) on FPGAs
  • State Space Models for Scientific Time Series Applications
  • Improving On-Chip Compression of High-Granularity Calorimeter Data with Conditional Autoencoders
CERN
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