Collaborative Reuse of Streaming Dataflows in IoT Applications. - Shilpa Chaturvedi; Sahil Tyagi; Yogesh Simmhan (eScience, 2017)

Anomaly Detection over Streaming Data: Indy500 Case Study. - Chathura Widanage; Jiayu Li; Sahil Tyagi; Ravi Teja; Bo Peng 011; Supun Kamburugamuve; Dan Baum; Dayle Smith; Judy Qiu; Jon Koskey (CLOUD, 2019)

Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching. - Sahil Tyagi; Prateek Sharma (ACSOS, 2020)

Cost-Effective Sharing of Streaming Dataflows for IoT Applications. - Shilpa Chaturvedi; Sahil Tyagi; Yogesh Simmhan (IEEE Trans. Cloud Comput., 2021)

ScaDLES: Scalable Deep Learning over Streaming data at the Edge. - Sahil Tyagi; Martin Swany (IEEE Big Data, 2022)

GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training. - Sahil Tyagi; Martin Swany (CLOUD, 2023)

Flexible Communication for Optimal Distributed Learning over Unpredictable Networks. - Sahil Tyagi; Martin Swany (IEEE Big Data, 2023)

Scavenger: A Cloud Service for Optimizing Cost and Performance of DL Training. - Sahil Tyagi (CCGridW, 2023)

Scavenger: A Cloud Service For Optimizing Cost and Performance of ML Training. - Sahil Tyagi; Prateek Sharma (CCGrid, 2023)

Accelerating Distributed ML Training via Selective Synchronization. - Sahil Tyagi; Martin Swany (CLUSTER, 2023)

Accelerating Distributed ML Training via Selective Synchronization (Poster Abstract). - Sahil Tyagi; Martin Swany (CLUSTER Workshops, 2023)

OmniLearn: A Framework for Distributed Deep Learning Over Heterogeneous Clusters. - Sahil Tyagi; Prateek Sharma (IEEE Trans. Parallel Distributed Syst., 2025)