Bart Cox
Earth and Planetary Sciences · Indiana University
Publications
47
Citations
552
Est. group size
~2
Recurring co-author estimate
Active years
51
Publishing since 1976
Bart Cox's recent work centers on distributed machine learning systems, particularly federated learning, where models are trained across many geographically separated devices or servers without pooling raw data. Topics include making such training tolerant to unreliable participants, handling heterogeneous hardware, and running multiple deep neural networks efficiently on resource-constrained edge devices. Earlier publications addressed seismic monitoring and imaging techniques for reservoirs, both onshore and offshore.
After a gap around 2018-2019 and 2023, publication activity has grown notably in recent years, reaching its highest output in 2024-2026.
Generated by claude-opus-4-8 from public bibliographic data · Jul 9, 2026
Typically publishes in teams of ~4 · 33% small-team papers (≤3 authors) · across 6 venues
- Nomad: Accelerating Geo-distributed Learning with Client Transfers
2025
- Go With The Flow: Churn-Tolerant Decentralized Training of Large Language Models
arXiv (Cornell University) · 2025
- Parameterizing Federated Continual Learning for Reproducible Research
Communications in computer and information science · 2024
- Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
arXiv (Cornell University) · 2024
- Spyker: Asynchronous Multi-Server Federated Learning for Geo-Distributed Clients
2024
- Asynchronous Byzantine Federated Learning
arXiv (Cornell University) · 2024
- Training Diffusion Models with Federated Learning
arXiv (Cornell University) · 2024
- Aergia: Leveraging Heterogeneity in Federated Learning Systems
arXiv (Cornell University) · 2022
- Aergia
2022
- Memory-aware and context-aware multi-DNN inference on the edge
Pervasive and Mobile Computing · 2022
- Masa: Responsive Multi-DNN Inference on the Edge
2021
- MemA: Fast Inference of Multiple Deep Models
2021
- Artifact: Masa: Responsive Multi-DNN Inference on the Edge
2021
- Multi-model inference on the edge: Scheduling for multi-model execution on resource constrained devices
Research Repository (Delft University of Technology) · 2020
- DAS Enables Cost-effective Reservoir Monitoring Onshore and Offshore
Proceedings · 2017
- arXiv (Cornell University)×5
- Proceedings×2
- Pervasive and Mobile Computing×1
- Communications in computer and information science×1
- Research Repository (Delft University of Technology)×1
- L. J. Pyrak‐Nolte
Earth and Planetary Sciences · Purdue University West Lafayette
- Douglas R. Schmitt
Earth and Planetary Sciences · Purdue University West Lafayette
- Juan E. Santos
Earth and Planetary Sciences · Purdue University West Lafayette
- David W. Alexander
Earth and Planetary Sciences · Purdue University West Lafayette
- Bin He
Earth and Planetary Sciences · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Sep 1, 2026.
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