Work and Research Experience

  •  Allocation and pricing of promoted items in sponsored search
  • Understanding and modeling users’ interests from their online activity trails collected from wide specter of data sources for prospective and retargeting conversion prediction tasks.
  • Modeling mobile users demographics and interests from their installed mobile apps.
  • Bid shading (diminishing bid price) for first-price auctions by modeling bidding landscape.
  • Improving click prediction model for premium ads marketplace (native advertising).
  • Contextual predictive audiences product (conversion prediction targeting and optimization) for the cookieless online world.
  • Selected for an amazing year-long Senior Leadership Fast Track Development program titled SPARK among 27 within a company of 10,000+ employees.
  • Selected for a leadership talent program titled ELEVATE that paired senior leadership member for guidance, support and mutual learning.


  • Developing major extensions aimed to: 1) facilitating ensemble-based users embedding where constituents can be replaced by embedding models of any type; 2) learning distributed representations for the target events-time purchase pairs.
  • Funded by Yahoo Faculty Research and Engagement Program (FREP) 2019
  • Developing novel methods for learning to match and rank investigators (physitians) for new clinical trials.
  • Funded by IQVIA
  • Fusing large scale hospital discharge records databases with various domain knowledge sources to obtain novel and meaningful insights and improved predictions in healthcare.
  • Funded by Office of Naval Research (ONR) (grant N00014-15-1-2729)
  • Correlating comorbidity and genetic disease networks across age, sex, and geography from large scale hospital discharge records databases.
  • Funded by National Science Foundation (NSF) (grant 14476570)