Sahil Tyagi

Stochastic Gradient Optimization Techniques Advanced Neural Network Applications Privacy-Preserving Technologies in Data Cloud Computing and Resource Management IoT and Edge/Fog Computing

Publications

Citations

Est. group size

—

Recurring co-author estimate

Active years

Publishing since 2018

Research summary

AI-generated

Sahil Tyagi works on making machine learning training faster and cheaper when it runs across many computers at once. Their research develops methods to coordinate distributed and federated learning across varied hardware — from cloud servers and high-performance computing clusters to edge devices — while managing communication costs, resource differences, and time/cost trade-offs.

Distributed deep learning trainingFederated learning frameworksCommunication-efficient and adaptive compression methodsCloud and edge/HPC resource managementCost and performance optimization for ML

Publication activity was sparse until a large burst in 2023, followed by a lower but continuing output in 2025 and 2026.

Generated by claude-opus-4-8 from public bibliographic data · Jul 11, 2026

Publication cadence

Publications per year over the last 10 years — averaging 3.6/year recently

Recent publications

Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training
arXiv (Cornell University) · 2026
Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training
arXiv (Cornell University) · 2026
OmniLearn: A Framework for Distributed Deep Learning over Heterogeneous Clusters
arXiv (Cornell University) · 2025
OmniLearn: A Framework for Distributed Deep Learning Over Heterogeneous Clusters
IEEE Transactions on Parallel and Distributed Systems · 2025
OmniFed: A Modular Framework for Configurable Federated Learning from Edge to HPC
2025
Scavenger: A Cloud Service for Optimizing Cost and Performance of ML Training
arXiv (Cornell University) · 2023
Scavenger: A Cloud Service For Optimizing Cost and Performance of ML Training
2023
Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching
arXiv (Cornell University) · 2023
Scavenger: A Cloud Service for Optimizing Cost and Performance of DL Training
2023
GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training
2023
Accelerating Distributed ML Training via Selective Synchronization
2023
Flexible Communication for Optimal Distributed Learning over Unpredictable Networks
2023
ScaDLES: Scalable Deep Learning over Streaming data at the Edge
arXiv (Cornell University) · 2023
GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training
arXiv (Cornell University) · 2023
Accelerating Distributed ML Training via Selective Synchronization
arXiv (Cornell University) · 2023

Publishes in

arXiv (Cornell University)×9
IEEE Transactions on Cloud Computing×1
2022 IEEE International Conference on Big Data (Big Data)×1
IEEE Transactions on Parallel and Distributed Systems×1
International Journal of Tourism and Hospitality in Asia Pasific×1

This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.

Last updated Jul 11, 2026.

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