I'm a PhD student in Computer Science at UC Berkeley, advised by Professor Aditya Parameswaran. My research focuses on developing software infrastructure solutions for facilitating machine learning and data analytics. Helix is the system solution I have been developing for the last few years to accelerate machine learning model development.
My research interests are inspired by my experience in developing large-scale ML models at LinkedIn prior to grad school. I received a BS in Computer Science from Caltech in 2012 and an MS in Computer Science from University of Illinois at Urbana-Champaign (UIUC) in 2019. I am continuning my PhD studies at UC Berkeley starting August 2019.
Our work titled “Demystifying a Dark Art: Understanding Real-World Machine Learning Model Development” has been accepted as a full paper and will be presented virtually at HILDA 2020.
I was invited to present our work on Helix (VLDB ‘19) at Google. Lots of interest in making maching learning development more agile and efficient!
Our work on enabling accelerated machine learning inference in RDBMS was presented at CIDR 2020. Paper
VLDB 19: We presented the full research paper on the Helix system that we demo’ed at VLDB 18.
Our vision paper on the data management and HCI challenges underlying AutoML was published in the IEEE Data Engg Bulletin.