Krishnaram Kenthapadi is a Principal Scientist at Amazon AWS AI, where he leads the fairness, explainability, privacy, and model understanding initiatives in Amazon AI platform. Prior to joining Amazon, he led similar efforts across different LinkedIn applications as part of the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. 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 2007, 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 19+ years of experience (including 14+ years in industry after his PhD), working on challenging problems in these areas, and has shaped the technical roadmap/design/development/launch of new AI products, provided technical vision and steered company-wide initiatives in new domains such as fairness/explainability/privacy, and improved business metrics for existing products via technology transfers. He has collaborated with over 70 people with diverse backgrounds and interests and mentored over 30 summer interns, resulting in 50+ publications, with 4500+ citations, and 145+ filed patents (65 granted) 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. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has presented lectures/tutorials on privacy, fairness, explainable AI, and responsible AI in industry at forums such as KDD ’18 ’19, WSDM ’19, WWW ’19 ’20 ’21, FAccT ’20 ’21, and AAAI ’20 ’21, and instructed a course on AI at Stanford.