I'm a senior research scientist in Yahoo Research's advertising science group, and focus on deep learning for ad tech and online user understanding in general. Before joining the industry, I was a PhD student at UCLA (Electrical Engineering, advisor Prof. Suhas Diggavi). My research interests include statistics and information theory with applications in machine learning, security and privacy. I'm also interested in applications of machine learning in smart cities and intelligent transportation systems. I have served as a reviewer for applied machine learning papers in major conferences (KDD, WWW, WSDM), and was a technical program committee member for ACM Safe Things Workshop (2017-2019).


  • Paper on deep learning based recommender system for ad creative strategists accepted at RecSys 2019!
  • RNN based purchase funnel understanding paper accepted for KDD 2019!
  • Paper on B2B ad targeting accepted for AdKDD 2019!
  • TPC member for AdKDD 2019
  • Advertiser specific sentiment analysis paper accepted for WikiWorkshop 2019 (in conjunction with WWW'19)!
  • TPC member for ACM Safe Things 2019
  • TPC member for ACM Safe Things 2018
  • Paper on deep learning for engineering cross features for mobile app install ads accepted for CIKM 2017.
  • TPC member for 1st ACM Workshop on the Internet of Safe Things (SafeThings 2017).
  • Joined Yahoo's advertising sciences group as a research scientist, Sunnyvale, August 2016.
  • Selected for Qualcomm Innovation Fellowship 2014 finals (joint proposal on video summarization with Vignesh Ramanathan, Stanford).