“Distributed Matrix-Based Sampling for Graph Neural Network Training” accepted to MLSys 2024

Passed Ph.D. Qualifying Exam

“Communication-Avoiding Algorithms for Full-Batch and Mini-Batch GNN Training” accepted to NVIDIA GTC 24

TA’d for JamCoders 2023

This is a phenomenal experience. For anybody interested in teaching CS or algorithms, I strongly recommend applying.

Recent & Upcoming Talks


(2023). Distributed Matrix-Based Sampling for Graph Neural Network Training. Proceedings of Machine Learning and Systems, arXiv:2311.02909.


(2020). Accurately and Efficiently Estimating Dynamic Point-to-Point Shortest Path. IEEE BigGraphs Workshop at International Conference on Big Data (BigData) 2020.

(2020). Reducing Communication in Graph Neural Network Training. ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, arXiv:2005.03300.


(2018). Scaling Betweenness Centrality in Dynamic Graphs. IEEE High Performance Extreme Computing (HPEC) 2018.