Ryan Missel

Orcid: 0000-0002-9509-6775

According to our database1, Ryan Missel authored at least 12 papers between 2020 and 2024.

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

Timeline

Legend:

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In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Hybrid Neural State-Space Modeling for Supervised and Unsupervised Electrocardiographic Imaging.
IEEE Trans. Medical Imaging, August, 2024

HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology.
CoRR, 2024

Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework.
CoRR, 2024

DATS: Difficulty-Aware Task Sampler for Meta-Learning Physics-Informed Neural Networks.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023
Continual Unsupervised Disentangling of Self-Organizing Representations.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

A Comparative Analysis of Data-Driven Modelling Techniques for 30-Day Heart Failure Readmission Prediction.
Proceedings of the Computing in Cardiology, 2023

Novel In-Home Cardiac Monitoring for Heart Failure Patients.
Proceedings of the Computing in Cardiology, 2023

2022
Neural State-Space Modeling with Latent Causal-Effect Disentanglement.
Proceedings of the Machine Learning in Medical Imaging - 13th International Workshop, 2022

Few-Shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-learning.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021
Label-Free Physics-Informed Image Sequence Reconstruction with Disentangled Spatial-Temporal Modeling.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2021 - 24th International Conference, Strasbourg, France, September 27, 2021

2020
A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms.
Comput. Biol. Medicine, 2020


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