Dimitris Stripelis

According to our database, Dimitris Stripelis authored at least 17 papers between 2017 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Federated Learning over Harmonized Data Silos.

Dimitris Stripelis, José Luis Ambite

CoRR, 2023

MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows.

Dimitris Stripelis, Chrysovalantis Anastasiou, Patrick Toral, Armaghan Asghar, José Luis Ambite

Proceedings of the 4th International Workshop on Distributed Machine Learning, 2023

2022

Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings.

Dimitris Stripelis, Paul M. Thompson, José Luis Ambite

ACM Trans. Intell. Syst. Technol., 2022

Secure Federated Learning for Neuroimaging.

Dimitris Stripelis, Umang Gupta, Hamza Saleem, Nikhil J. Dhinagar, Tanmay Ghai, Rafael Sanchez, Chrysovalantis Anastasiou, Armaghan Asghar, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, José Luis Ambite

CoRR, 2022

Performance Weighting for Robust Federated Learning Against Corrupted Sources.

Dimitris Stripelis, Marcin Abram, José Luis Ambite

CoRR, 2022

Federated Progressive Sparsification (Purge, Merge, Tune)+.

Dimitris Stripelis, Umang Gupta, Greg Ver Steeg, José Luis Ambite

CoRR, 2022

Federated Named Entity Recognition.

Joel Mathew, Dimitris Stripelis, José Luis Ambite

CoRR, 2022

Towards Sparsified Federated Neuroimaging Models via Weight Pruning.

Dimitris Stripelis, Umang Gupta, Nikhil J. Dhinagar, Greg Ver Steeg, Paul M. Thompson, José Luis Ambite

Proceedings of the Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health, 2022

2021

Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption.

Dimitris Stripelis, Hamza Saleem, Tanmay Ghai, Nikhil J. Dhinagar, Umang Gupta, Chrysovalantis Anastasiou, Greg Ver Steeg, Srivatsan Ravi, Muhammad Naveed, Paul M. Thompson, José Luis Ambite

CoRR, 2021

Semi-Synchronous Federated Learning.

Dimitris Stripelis, José Luis Ambite

CoRR, 2021

Membership Inference Attacks on Deep Regression Models for Neuroimaging.

Umang Gupta, Dimitris Stripelis, Pradeep K. Lam, Paul M. Thompson, José Luis Ambite, Greg Ver Steeg

Proceedings of the Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany., 2021

Scaling Neuroscience Research Using Federated Learning.

Dimitris Stripelis, José Luis Ambite, Pradeep Lam, Paul M. Thompson

Proceedings of the 18th IEEE International Symposium on Biomedical Imaging, 2021

2020

Accelerating Federated Learning in Heterogeneous Data and Computational Environments.

Dimitris Stripelis, José Luis Ambite

CoRR, 2020

2019

MR-Cubes: On-the-Fly Computation of Location Popularity from Check-in Data Streams.

George Constantinou, Chrysovalantis Anastasiou, Dimitris Stripelis, Cyrus Shahabi

Proceedings of the 20th IEEE International Conference on Mobile Data Management, 2019

2018

Extending Apache Spark with a Mediation Layer.

Dimitris Stripelis, Chrysovalantis Anastasiou, José Luis Ambite

Proceedings of the International Workshop on Semantic Big Data, 2018

2017

A Scalable Data Integration and Analysis Architecture for Sensor Data of Pediatric Asthma.

Dimitris Stripelis, José Luis Ambite, Yao-Yi Chiang, Sandrah P. Eckel, Rima Habre

Proceedings of the 33rd IEEE International Conference on Data Engineering, 2017

Mining Public Datasets for Modeling Intra-City PM2.5 Concentrations at a Fine Spatial Resolution.

Yijun Lin, Yao-Yi Chiang, Fan Pan, Dimitrios Stripelis, José Luis Ambite, Sandrah P. Eckel, Rima Habre

Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2017