I am a PhD student in Computer Science at Stanford University, where I work with Jure Leskovec on scaling software systems for Graph Neural Networks (GNNs).
My broad research interests include distributed systems and cloud computing – in particular, I am interested in the system-side problems associated with learning, deploying, and operationalizing machine learning models at scale.
Previously, I was a Research Fellow at Microsoft Research India and prior to that obtained my Masters (by Research) in Computer and Data Systems from the Indian Institute of Science (IISc).
· [September 2022] Serving on EuroSys 2023 Artifact Evaluation Committee
· [April 2022] Serving on OSDI 2022 Artifact Evaluation Committee
· [October 2021] Serving on EuroSys 2022 Shadow PC committee
· [July 2021] Joined Microsoft Research India as a Research Fellow. Looking forward to my time here 🎇
· [March 2021] Our work on training GNNs at scale is accepted to OSDI 2021! 🎉
· [May 2020] Successfully defended M.Tech.(Research) Thesis. 🎓 🗞 🙌 🥳
· [April 2020] Our work on distributed temporal graph processing is accepted to ICDE 2020 🎊
2. An Interval-centric Model for Distributed Computing over Temporal Graphs April 2020
Swapnil Gandhi and Yogesh Simmhan
IEEE 36th International Conference on Data Engineering (ICDE) in Dalas, Texas 2020
PDF Video Slides IEEE Xplore Code
Distributed Programming Abstraction for Scalable Processing of Temporal Graphs January 2020
M.Tech (Research) Thesis · Indian Institute of Science (IISc)
1. Wave : A Substrate for Distributed Incremental Graph Processing on Commodity Clusters October 2019
ACM Student Research Competition (SRC) at 27th Symposium on Operating Systems Principles (SOSP), Ontario, Canada
2. Distributed Querying over Compressed Property Graphs December 2018
Swapnil Gandhi, Sayandip Sarkar, Abhilash Sharma, Yogesh Simmhan
Student Research Symposium (SRS) at 24th IEEE International Conference on High Performance Computing, Data and Analytics (HiPC), Jaipur, India
Best Poster Award