Tristan Glatard

Orcid: 0000-0003-2620-5883

Affiliations:
  • Concordia University, Montreal, Canada


According to our database1, Tristan Glatard authored at least 159 papers between 2004 and 2024.

Collaborative distances:

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Mondrian forest for data stream classification under memory constraints.
Data Min. Knowl. Discov., 2024

Training Compute-Optimal Vision Transformers for Brain Encoding.
CoRR, 2024

An Analysis of Performance Bottlenecks in MRI Pre-Processing.
CoRR, 2024

Hierarchical storage management in user space for neuroimaging applications.
CoRR, 2024

Scaling up ridge regression for brain encoding in a massive individual fMRI dataset.
CoRR, 2024

Predicting Parkinson's disease trajectory using clinical and functional MRI features: a reproduction and replication study.
CoRR, 2024

The Impact of Hardware Variability on Applications Packaged with Docker and Guix: a Case Study in Neuroimaging.
Proceedings of the 2nd ACM Conference on Reproducibility and Replicability, 2024

2023
bids-specification.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2023

PyTracer: Automatically Profiling Numerical Instabilities in Python.
IEEE Trans. Computers, June, 2023

Classification of Anomalies in Telecommunication Network KPI Time Series.
CoRR, 2023

A numerical variability approach to results stability tests and its application to neuroimaging.
CoRR, 2023

Performance comparison of Dask and Apache Spark on HPC systems for neuroimaging.
Concurr. Comput. Pract. Exp., 2023

Numerical Uncertainty of Convolutional Neural Networks Inference for Structural Brain MRI Analysis.
Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, 2023

Reproducibility of Tumor Segmentation Outcomes with a Deep Learning Model.
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023

2022
nipy/nipype: 1.8.3.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, July, 2022

nipy/nipype: 1.8.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2022

nipy/nipype: 1.8.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2022

nipy/nipype: 1.7.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, April, 2022

Numerical Stability of DeepGOPlus Inference.
CoRR, 2022

Sea: A lightweight data-placement library for Big Data scientific computing.
CoRR, 2022

NeuroCI: Continuous Integration of Neuroimaging Results Across Software Pipelines and Datasets.
Proceedings of the 18th IEEE International Conference on e-Science, 2022

Dynamic Ensemble Size Adjustment for Memory Constrained Mondrian Forest.
Proceedings of the IEEE International Conference on Big Data, 2022

2021
nipy/nipype: 1.7.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, October, 2021

Multiple sclerosis lesions segmentation from multiple experts: The MICCAI 2016 challenge dataset.
NeuroImage, 2021

PyTracer: Automatically profiling numerical instabilities in Python.
CoRR, 2021

Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in Connectomics.
CoRR, 2021

A Recommender System for Scientific Datasets and Analysis Pipelines.
Proceedings of the 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), 2021

The benefits of prefetching for large-scale cloud-based neuroimaging analysis workflows.
Proceedings of the 2021 IEEE Workshop on Workflows in Support of Large-Scale Science (WORKS), 2021

Accurate Simulation of Operating System Updates in Neuroimaging Using Monte-Carlo Arithmetic.
Proceedings of the Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis, 2021

Modeling the Linux page cache for accurate simulation of data-intensive applications.
Proceedings of the IEEE International Conference on Cluster Computing, 2021

Reducing numerical precision preserves classification accuracy in Mondrian Forests.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

A Scalable Multi-factor Fault Analysis Framework for Information Systems.
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

Can we Estimate Truck Accident Risk from Telemetric Data using Machine Learning?
Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020
nipy/nipype: 1.5.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, September, 2020

nipy/nipype: 1.5.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, June, 2020

nipy/nipype: 1.4.2.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, February, 2020

nipy/nipype: 1.4.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, January, 2020

A Benchmark of Data Stream Classification for Human Activity Recognition on Connected Objects.
Sensors, 2020

Comparing perturbation models for evaluating stability of neuroimaging pipelines.
Int. J. High Perform. Comput. Appl., 2020

A Quantitative EEG Toolbox for the MNI Neuroinformatics Ecosystem: Normative SPM of EEG Source Spectra.
Frontiers Neuroinformatics, 2020

An Analysis of Security Vulnerabilities in Container Images for Scientific Data Analysis.
CoRR, 2020

Performance benefits of Intel<sup>®</sup> Optane™ DC persistent memory for the parallel processing of large neuroimaging data.
Proceedings of the 20th IEEE/ACM International Symposium on Cluster, 2020

2019
nipy/nipype: 1.4.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, December, 2019

nipy/nipype: 1.3.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2019

nipy/nipype: 1.3.0-rc1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, October, 2019

nipy/nipype: 1.2.2.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, September, 2019

nipy/nipype: 1.2.2.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, September, 2019

nipy/nipype: 1.2.3.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, September, 2019

nipy/nipype: 1.2.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, August, 2019

nipy/nipype: 1.2.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2019

nipy/nipype: 1.1.9.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, February, 2019

nipy/nipype: 1.1.8.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, January, 2019

A Quantitative Comparison of Overlapping and Non-Overlapping Sliding Windows for Human Activity Recognition Using Inertial Sensors.
Sensors, 2019

OrpailleCC: a Library for Data Stream Analysis on Embedded Systems.
J. Open Source Softw., 2019

A Serverless Tool for Platform Agnostic Computational Experiment Management.
Frontiers Neuroinformatics, 2019

The global impact of science gateways, virtual research environments and virtual laboratories.
Future Gener. Comput. Syst., 2019

Performance benefits of Intel(R) OptaneTM DC persistent memory for the parallel processing of large neuroimaging data.
CoRR, 2019

Comparing Perturbation Models for Evaluating Stability of Post-Processing Pipelines in Neuroimaging.
CoRR, 2019

A Conceptual Marketplace Model for IoT Generated Personal Data.
CoRR, 2019

Subject Cross Validation in Human Activity Recognition.
CoRR, 2019

A Performance Comparison of Dask and Apache Spark for Data-Intensive Neuroimaging Pipelines.
Proceedings of the 2019 IEEE/ACM Workflows in Support of Large-Scale Science, 2019

Evaluation of Pilot Jobs for Apache Spark Applications on HPC Clusters.
Proceedings of the 15th International Conference on eScience, 2019

Performance Evaluation of Big Data Processing Strategies for Neuroimaging.
Proceedings of the 19th IEEE/ACM International Symposium on Cluster, 2019

High-Resolution Road Vehicle Collision Prediction for the City of Montreal.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

2018
nipy/nipype: 1.1.7.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, December, 2018

nipy/nipype: 1.1.6.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2018

nipy/nipype: 1.1.5.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2018

nipy/nipype: 1.1.4.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, October, 2018

nipy/nipype: 1.1.3.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, September, 2018

nipy/nipype: 1.1.2.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, August, 2018

nipy/nipype: 1.1.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, July, 2018

nipy/nipype: Nipype 1.1.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, July, 2018

nipy/nipype: 1.0.4.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2018

nipy/nipype: 1.0.4.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2018

nipy/nipype: Nipype 1.0.3.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, April, 2018

nipy/nipype: 1.0.2.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, March, 2018

nipy/nipype: 1.0.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, February, 2018

nipy/nipype: Nipype - v1.0.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, January, 2018

The first MICCAI challenge on PET tumor segmentation.
Medical Image Anal., 2018

Data models for service failure prediction in supply-chain networks.
CoRR, 2018

Special isssue of the CCGrid-Life workshop 2017.
Concurr. Comput. Pract. Exp., 2018

Evaluation Through Realistic Simulations of File Replication Strategies for Large Heterogeneous Distributed Systems.
Proceedings of the Euro-Par 2018: Parallel Processing Workshops, 2018

Service failure prediction in supply-chain networks.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

A multi-dimensional extension of the Lightweight Temporal Compression method.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering.
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017
nipy/nipype: 0.14.0.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2017

nipy/nipype: 0.14.0-rc1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2017

nipy/nipype: 0.14.0-rc1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, November, 2017

Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. 0.13.1.
, , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , ,
Dataset, May, 2017


Software architectures to integrate workflow engines in science gateways.
Future Gener. Comput. Syst., 2017

Boutiques: a flexible framework for automated application integration in computing platforms.
CoRR, 2017

Modeling Distributed Platforms from Application Traces for Realistic File Transfer Simulation.
Proceedings of the 17th IEEE/ACM International Symposium on Cluster, 2017

Sequential algorithms to split and merge ultra-high resolution 3D images.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016

The MNI data-sharing and processing ecosystem.
NeuroImage, 2016

Cyberinfrastructure for Open Science at the Montreal Neurological Institute.
Frontiers Neuroinformatics, 2016

Combining analytical modeling, realistic simulation and real experimentation for the optimization of Monte-Carlo applications on the European Grid Infrastructure.
Future Gener. Comput. Syst., 2016

2015
Reproducibility of neuroimaging analyses across operating systems.
Frontiers Neuroinformatics, 2015

A stateful storage availability and entropy model to control storage distribution on grids.
Concurr. Comput. Pract. Exp., 2015

Classifications of Computing Sites to Handle Numerical Variability.
Proceedings of the 15th IEEE/ACM International Symposium on Cluster, 2015

2014
Domain-specific summarization of Life-Science e-experiments from provenance traces.
J. Web Semant., 2014

OntoVIP: An ontology for the annotation of object models used for medical image simulation.
J. Biomed. Informatics, 2014

CBRAIN: a web-based, distributed computing platform for collaborative neuroimaging research.
Frontiers Neuroinformatics, 2014

Controlling fairness and task granularity in distributed, online, non-clairvoyant workflow executions.
Concurr. Comput. Pract. Exp., 2014

Controlling the Deployment of Virtual Machines on Clusters and Clouds for Scientific Computing in CBRAIN.
Proceedings of the 14th IEEE/ACM International Symposium on Cluster, 2014

Global Initiative for Sentinel e-Health Network on Grid (GINSENG): Medical Data Integration and Semantic Developments for Epidemiology.
Proceedings of the 14th IEEE/ACM International Symposium on Cluster, 2014

2013
A Virtual Imaging Platform for Multi-Modality Medical Image Simulation.
IEEE Trans. Medical Imaging, 2013

Bundle and Pool Architecture for Multi-Language, Robust, Scalable Workflow Executions.
J. Grid Comput., 2013

Self-healing of workflow activity incidents on distributed computing infrastructures.
Future Gener. Comput. Syst., 2013

A classification of file placement and replication methods on grids.
Future Gener. Comput. Syst., 2013

Efficient distributed monitoring with active Collaborative Prediction.
Future Gener. Comput. Syst., 2013

Monte Carlo simulation on heterogeneous distributed systems: A computing framework with parallel merging and checkpointing strategies.
Future Gener. Comput. Syst., 2013

Toward fine-grained online task characteristics estimation in scientific workflows.
Proceedings of WORKS 2013: 8th Workshop On Workflows in Support of Large-Scale Science, 2013

PRACE DECI (Distributed European Computing Initiative) Minisymposium.
Proceedings of the Parallel Computing: Accelerating Computational Science and Engineering (CSE), 2013

On-Line, Non-clairvoyant Optimization of Workflow Activity Granularity on Grids.
Proceedings of the Euro-Par 2013 Parallel Processing, 2013

Workflow Fairness Control on Online and Non-clairvoyant Distributed Computing Platforms.
Proceedings of the Euro-Par 2013 Parallel Processing, 2013

Simulating Application Workflows and Services Deployed on the European Grid Infrastructure.
Proceedings of the 13th IEEE/ACM International Symposium on Cluster, 2013

2012
Multi-modality image simulation with the Virtual Imaging Platform: Illustration on cardiac echography and MRI.
Proceedings of the 9th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2012

Technical support for Life Sciences communities on a production grid infrastructure.
Proceedings of the HealthGrid Applications and Technologies Meet Science Gateways for Life Sciences, 2012

HealthGrid 2012.
Proceedings of the HealthGrid Applications and Technologies Meet Science Gateways for Life Sciences, 2012

A Science-Gateway Workload Archive to Study Pilot Jobs, User Activity, Bag of Tasks, Task Sub-steps, and Workflow Executions.
Proceedings of the Euro-Par 2012: Parallel Processing Workshops, 2012

Self-Healing of Operational Workflow Incidents on Distributed Computing Infrastructures.
Proceedings of the 12th IEEE/ACM International Symposium on Cluster, 2012

Distributed Monitoring with Collaborative Prediction.
Proceedings of the 12th IEEE/ACM International Symposium on Cluster, 2012

2011
A model of pilot-job resource provisioning on production grids.
Parallel Comput., 2011

An exploration framework for segmentation parameter spaces.
Proceedings of the 18th IEEE International Conference on Image Processing, 2011

Multi-modality medical image simulation of biological models with the Virtual Imaging Platform (VIP).
Proceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems, 2011

Sharing object models for multi-modality medical image simulation: A semantic approach.
Proceedings of the 24th IEEE International Symposium on Computer-Based Medical Systems, 2011

2010
A virtual laboratory for medical image analysis.
IEEE Trans. Inf. Technol. Biomed., 2010

Dynamic Partitioning of GATE Monte-Carlo Simulations on EGEE.
J. Grid Comput., 2010

Large-scale functional MRI study on a production grid.
Future Gener. Comput. Syst., 2010

Optimization of Mean-Shift scale parameters on the EGEE grid.
Proceedings of the Healthgrid Applications and Core Technologies, 2010

2009
Modeling the latency on production grids with respect to the execution context.
Parallel Comput., 2009

A data-driven workflow language for grids based on array programming principles.
Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science, 2009

Modeling user submission strategies on production grids.
Proceedings of the 18th ACM International Symposium on High Performance Distributed Computing, 2009

Crossing HealthGrid Borders: Early Results in Medical Imaging.
Proceedings of the Healthgrid Research, Innovation and Business Case - Proceedings of HealthGrid 2009, Berlin, Germany, 29 June, 2009

Towards Production-level Cardiac Image Analysis with Grids.
Proceedings of the Healthgrid Research, Innovation and Business Case - Proceedings of HealthGrid 2009, Berlin, Germany, 29 June, 2009

Modelling Pilot-Job Applications on Production Grids.
Proceedings of the Euro-Par 2009, 2009

2008
Flexible and Efficient Workflow Deployment of Data-Intensive Applications On Grids With MOTEUR.
Int. J. High Perform. Comput. Appl., 2008

Workflow-Based Data Parallel Applications on the EGEE Production Grid Infrastructure.
J. Grid Comput., 2008

A Service-Oriented Architecture enabling dynamic service grouping for optimizing distributed workflow execution.
Future Gener. Comput. Syst., 2008

A framework for evaluating the impact of compression on registration algorithms without gold standard.
Proceedings of the International Conference on Image Processing, 2008

From "Low Hanging" to "User Ready": Initial Steps into a HealthGrid.
Proceedings of the Global Healthgrid: e-Science Meets Biomedical Informatics, 2008

User Friendly Management of Workflow Results: From Provenance Information to Grid Logical File Names.
Proceedings of the Fourth International Conference on e-Science, 2008

A Probabilistic Model to Analyse Workflow Performance on Production Grids.
Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2008), 2008

Implementation of Turing Machines with the Scufl Data-Flow Language.
Proceedings of the 8th IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2008), 2008

Workflow Integration in VL-e Medical.
Proceedings of the Twenty-First IEEE International Symposium on Computer-Based Medical Systems, 2008

2007
Description, deployment and optimization of medical image analysis workflows on production grids. (Description, déploiement et optimisation de chaînes de traitements d'analyse d'images médicales sur grilles de production).
PhD thesis, 2007

Optimizing jobs timeouts on clusters and production grids.
Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2007), 2007

Impact of the execution context on Grid job performances.
Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid 2007), 2007

Merging overlapping orchestrations: an application to the Bronze Standard medical application.
Proceedings of the 2007 IEEE International Conference on Services Computing (SCC 2007), 2007

Workflow-Level Parametric Study Support by MOTEUR and the P-GRADE Portal.
Proceedings of the Workflows for e-Science, Scientific Workflows for Grids., 2007

2006
Probabilistic and Dynamic Optimization of Job Partitioning on a Grid Infrastructure.
Proceedings of the 14th Euromicro International Conference on Parallel, 2006

Performance Evaluation of Grid-Enabled Registration Algorithms Using Bronze-Standards.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention, 2006

Efficient services composition for grid-enabled data-intensive applications.
Proceedings of the 15th IEEE International Symposium on High Performance Distributed Computing, 2006

Medical image registration algorithms assesment: Bronze Standard application enactment on grids using the MOTEUR workflow engine.
Proceedings of the Challenges and Opportunities of HealthGrids, 2006

2005
Grid-enabling medical image analysis.
Proceedings of the 5th International Symposium on Cluster Computing and the Grid (CCGrid 2005), 2005

Grid-Enabled Workflows for Data Intensive Medical Applications.
Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems (CBMS 2005), 2005

2004
Texture based medical image indexing and retrieval: application to cardiac imaging.
Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, 2004


  Loading...