My thesis focused on improving the scalability, accuracy and efficiency of large scale Graph Neural Networks. I designed new neural network models, learning algorithms as well as hardware systems for GNN training and inference. My work has led to publications in top venues in both AI (ICLR, NeurIPS, etc.) and systems (FPGA, VLDB, TRETS, etc.). I have received outstanding reviewer awards from ICLR and ICML.

I am broadly interested in problems in ML and system design. Please reach out to me via my personal email if you’d like to discuss.