Krishnaram Kenthapadi is part of the AI team at LinkedIn, where he leads the fairness, transparency, explainability, and privacy modeling efforts across different LinkedIn applications. He also serves as LinkedIn's representative in Microsoft's AI and Ethics in Engineering and Research (AETHER) Committee. He shaped the technical roadmap and led the privacy/modeling efforts for LinkedIn Salary product, and prior to that, served as the relevance lead for the LinkedIn Careers and Talent Solutions Relevance team, which powers search/recommendation products at the intersection of members, recruiters, and career opportunities. Previously, he was a Researcher at Microsoft Research Silicon Valley, where his work resulted in product impact (and Gold Star / Technology Transfer awards), and several publications/patents. He received his Ph.D. in Computer Science from Stanford University in 2006, under the supervision of Professor Rajeev Motwani. Before joining Stanford, he received his Bachelors degree in Computer Science and Engineering from Indian Institute of Technology-Madras.
Krishnaram's expertise is in the areas of fairness/transparency/explainability/privacy in AI/ML systems, algorithms for large datasets and graphs, data mining, web search, information retrieval, search and recommendation systems, social network analysis, and computational education. He has 17+ years of experience (including 12+ years in industry after his PhD), working on challenging problems in these areas, and has shaped the technical roadmap/design/development/launch of new products, provided technical vision, and steered company-wide initiatives in new domains such as fairness/privacy, and improved business metrics for existing products via technology transfers. He has collaborated with over 60 people with diverse backgrounds and interests and mentored over 25 summer interns, resulting in 35+ publications, with 2500+ citations, and 130+ filed patents in his fields of interest. He serves regularly on the program committees of KDD, WWW, WSDM, and related conferences, and co-chaired the 2014 ACM Symposium on Computing for Development. He received Microsoft's AI/ML conference (MLADS) distinguished contribution award, CIKM best case studies paper award, SODA best student paper award, and WWW best paper award nomination. He has taught a tutorial on privacy-preserving data mining at KDD 2018, instructed a course on artificial intelligence at Stanford, and given several talks on his research work.