Data Science and Machine Learning
Prasad 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.
Prasad’s 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. His innovative work on ad-measurement drove the release of MediaMath’s much-anticipated ad-measurement product.
Agency Ad Operations
Angelina consolidated platforms across media platform partners (ad servers, DSPs, paid social, media tools, etc.) to strengthen Merkle’s Agency Services team and expand capabilities to a more addressable/people-based approach. Improved and streamlined operations, reporting, insights and optimizations. Standardized data to enable simplified data categorization process.
Angelina reshaped the definition of the role of an ad agency ad ops team and developed team of media tech experts who can consult, implement, and manage all aspects of media operations.
Data-driven solutions for premium publishers and Fortune 100 brands.
Kelly took offline data to build a full match network to harvest and monetize the data to over 100 DSPs, trading desks, and agencies.
Kelly created entire digital data taxonomies, built match networks with independent publishing networks, managed costs, and developed relationships with all buyers across the data landscape.
Data Science and Data Engineering
Madhu has developed Near’s location intelligence platform, products and team by understanding future eco-system requirements as well as fulfilling current needs as data-driven marketing and advertising strategies continue to evolve at a rapid pace.
Madhu has led the development of Near’s location intelligence platform and the Allspark SAAS product. Key focus has been on developing state-of-art spatio-temporal models by fusing heterogeneous spatial data that can power future advertising and media management strategies for brands and publishers.
Online data product and real-time bidding partnerships owner for TotalSource PlusTM Online and MarketView Online
Evan has expanded Epsilon’s assets from a few hundred data segments to thousands of targetable audiences over several quarters. He’s helped develop internal segment creation and QA tools, codify online data governance policy, and has worked tirelessly to formalize internal and external delivery processes. Evan’s success is a testament to his ability to solve complex problems, with an eye on the customer, and juggle competing stakeholders in a large organization like Epsilon.
Evan has transformed Epsilon’s digital data strategy since taking over the product, yielding dramatic growth in partnerships, end-client usage, and overall revenues. Evan has also championed digital initiatives and cross-functional product development in partnership with the Epsilon offline data team. Evan’s ability to bridge the gap between online and offline has been critical to his, and Epsilon’s, success.
Congratulations to the 2016 IAB Data Rockstars! The IAB Data Center of Excellence celebrates top industry leaders and practitioners who have demonstrated achievement in data science or technology. Read more about the IAB Data Rockstar Awards.