Bio
Dr. Sahil Tyagi is a Postdoctoral Research Associate in the Analytics and AI Methods at Scale group (AAIMS) at ORNL, where he works at the intersection of ML and systems research. Specifically, he is exploring different computation and communication models to scale deep learning workloads across edge, cloud and high-performance computing (HPC) clusters.
Dr. Tyagi holds a Ph.D. in Intelligent Systems Engineering from Indiana University Bloomington, where he worked on distributed model training, federated learning, gradient compression, stream processing, and more.
Awards
- Best Early-Career and Student Research Poster Award, IEEE International Symposium on Cluster, Cloud, and Internet Computing (CCGrid), 2023.
- NSF Student Grant, IEEE CLUSTER, 2023.
- Luddy Dean's Graduate Student Award, Fall 2023.
- NSF Student Travel Award, IEEE CCGrid 2023.
Education
- Ph.D., Intelligent Systems Engineering, Indiana University Bloomington, USA.
Professional Service
- 2024: reviewer for USENIX Operating Systems Design and Implementation (OSDI), USENIX Annual Technical Conference (ATC), IEEE CLUSTER, Journal of Parallel and Distributed Computing (JPDC).
- 2025: reviewer for International Joint Conference on Neural Networks (IJCNN).
Other Publications
- Tyagi, S., & Swany, M. Flexible Communication for Optimal Distributed Learning over Unpredictable Networks. 2023 IEEE International Conference on Big Data (BigData ‘23), 925-935.
- Tyagi, S., & Swany, M. Accelerating Distributed ML Training via Selective Synchronization. 2023 IEEE International Conference on Cluster Computing (CLUSTER ‘23), 1-12.
- Tyagi, S., & Swany, M. GraVAC: Adaptive Compression for Communication-Efficient Distributed DL Training. 2023 IEEE 16th International Conference on Cloud Computing (CLOUD ‘23), 319-329.
- Tyagi, S., & Sharma, P. Scavenger: A Cloud Service For Optimizing Cost and Performance of ML Training. 2023 IEEE/ACM 23rd International Symposium on Cluster, Cloud and Internet Computing (CCGrid ‘23), 403-413.
- Tyagi, S., & Swany, M. ScaDLES: Scalable Deep Learning over Streaming data at the Edge. 2022 IEEE International Conference on Big Data (BigData ‘22), 2113-2122.
- Tyagi, S., & Sharma, P. Taming Resource Heterogeneity In Distributed ML Training With Dynamic Batching. 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS ‘20), 188-194.
- Widanage, C., Li, J., Tyagi, S., Teja, R., Peng, B., Kamburugamuve, S., Baum, D., Smith, D., Qiu, J., & Koskey, J. Anomaly Detection over Streaming Data: Indy500 Case Study. 2019 IEEE 12th International Conference on Cloud Computing (CLOUD ‘19), 9-16.
- Chaturvedi, S., Tyagi, S., & Simmhan, Y. Collaborative Reuse of Streaming Dataflows in IoT Applications. 2017 IEEE 13th International Conference on e-Science (e-Science ‘17), 403-412.
- Chaturvedi, S., Tyagi, S., & Simmhan, Y.L. (2019). Cost-Effective Sharing of Streaming Dataflows for IoT Applications. IEEE Transactions on Cloud Computing (TCC ‘19), 9, 1391-1407.