IAB Reveals Winners of Data Rockstar Awards

IAB (Interactive Advertising Bureau) and its Data Center of Excellence today announced the winners of the inaugural IAB Data Rockstar Awards, celebrating top industry leaders and practitioners who have demonstrated achievement in data science or technology. The top finalists were selected by the IAB Data Center of Excellence Board of Directors and were evaluated based on demonstrated excellence, creativity or forward-thinking approaches to solving problems in data science, as well as the impact their contributions have made to their company or industry. Chalasani developed a highly efficient, distributed, extreme-scale, single-pass online logistic regression learning system in Scala/Spark, using variants of Stochastic Gradient Descent, capable of handling hundreds of millions of sparse features and billions of training observations. His system incorporates a number of state-of-the-art techniques that do not exist together in any other machine learning system, including adaptive feature-scaling, adaptive gradients, feature-interactions and feature-hashing. Chalasani work is central to MediaMath's vision for every addressable interaction between a marketer and a consumer to be driven by Machine Learning optimization against all available, relevant data at that moment, to maximize long-term marketer business outcomes.