Shaunak is a research scientist in the advertising science team. His work currently focuses on designing, training, and deploying very large scale deep learning models for online advertising (click and conversion prediction, real time bidding). In addition, he also works on machine learning methods (e.g., reinforcement learning) for boosting advertiser satisfaction, detecting fraud, and increasing revenue. He holds a B.Tech degree from the Indian Institute of Technology (IIT) Kharagpur (2010), an M.S. degree from UCLA (2011), and a PhD degree from UCLA (2016). He is a recipient of the Henry Samueli Fellowship (2010-2011), and was a finalist for the Qualcomm Innovation Fellowship 2014. His research interests are broadly in machine learning, information theory and control theory with applications in ad tech, security, IoT and smart cities.