Sagar Vare

Sagar Vare has had a diverse and successful career. In 2022, they began working as a Software Engineer at Glean. Prior to this, Vare was a Machine Learning Engineer at Google from 2017-2022, where they worked on Google Suggest and Google Shopping Ads. From 2015-2017, Vare was a Research Assistant at Stanford University, where they won a Best Paper and Best Students Paper Runner up award at KDD '17. Sagar also worked on developing Unsupervised Machine Learning ideas for time series data, and formulated a Convex Optimization problem for solving a simultaneous segmentation and clustering of time series data. Additionally, they implemented Beam Search and Monte Carlo Tree Search algorithms for solving Connect-6, and beat a commercial AI on its hardest level. In 2016, Vare worked as a Deep Learning Intern at Matroid, and as a Teaching Assistant at the Graduate School of Business, Stanford. In 2014, they completed their Masters thesis at Indian Institute of Technology, Madras, where they analyzed Multiscale structures using a novel idea of Wave Finite Element Method.

Sagar Vare completed their Bachelors of Mechanical Engineering from the Indian Institute of Technology, Madras in 2015, after studying there from 2010-2015. Sagar then went on to pursue a Masters of Computational and Mathematical Engineering from Stanford University, which they completed in 2017.