Dylan S. Rankin

Orcid: 0000-0001-8411-9620

  • Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
  • Boston University, Department of Physics, MA, USA

According to our database1, Dylan S. Rankin authored at least 18 papers between 2019 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.



In proceedings 
PhD thesis 


Online presence:

On csauthors.net:


GWAK: gravitational-wave anomalous knowledge with recurrent autoencoders.
Mach. Learn. Sci. Technol., 2024

Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml.
Mach. Learn. Sci. Technol., June, 2023

<i>AIgean</i>: An Open Framework for Deploying Machine Learning on Heterogeneous Clusters.
ACM Trans. Reconfigurable Technol. Syst., 2022

Physics Community Needs, Tools, and Resources for Machine Learning.
CoRR, 2022

A Software Ecosystem for Deploying Deep Learning in Gravitational Wave Physics.
Proceedings of the FlexScience '22: Proceedings of the 12th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures, 2022

Compressing deep neural networks on FPGAs to binary and ternary precision with hls4ml.
Mach. Learn. Sci. Technol., 2021

GPU coprocessors as a service for deep learning inference in high energy physics.
Mach. Learn. Sci. Technol., 2021

Fast convolutional neural networks on FPGAs with hls4ml.
Mach. Learn. Sci. Technol., 2021

Applications and Techniques for Fast Machine Learning in Science.
CoRR, 2021

hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices.
CoRR, 2021

Fast convolutional neural networks on FPGAs with hls4ml.
CoRR, 2021

Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics.
Frontiers Big Data, 2020

Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs.
CoRR, 2020

Fast inference of Boosted Decision Trees in FPGAs for particle physics.
CoRR, 2020

FPGAs-as-a-Service Toolkit (FaaST).
Proceedings of the 2020 IEEE/ACM International Workshop on Heterogeneous High-performance Reconfigurable Computing, 2020

AIgean: An Open Framework for Machine Learning on Heterogeneous Clusters.
Proceedings of the 28th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, 2020

FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing.
Comput. Softw. Big Sci., December, 2019

Fast Inference of Deep Neural Networks for Real-time Particle Physics Applications.
Proceedings of the 2019 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays, 2019