Krishnaram Kenthapadi
Associate Research Fellow
Dr. Krishnaram Kenthapadi has over a 20-year record of advancing new research areas, identifying early trends in AI/ML & influencing thought leadership in industry, shaping the technical vision, development & launch of new AI products, and steering company-wide initiatives in new domains. As the Chief Scientist, Clinical AI at Oracle Health, he leads the AI initiatives for Clinical Digital Assistant & other Oracle Health products. Previously, as the Chief AI Officer & Chief Scientist of Fiddler AI, he led initiatives on generative AI, AI safety, observability & trustworthiness. Previously, he led the responsible AI initiatives in Amazon AWS AI platform, and shaped new initiatives such as Amazon SageMaker Clarify from inception to launch. Before that, he was part of the LinkedIn AI team, where he led a large-scale responsible AI deployment in industry (fairness-aware LinkedIn Talent Search), incubated responsible AI initiatives, and shaped the scientific roadmap for new products such as LinkedIn Salary, and prior to that, he was a researcher at Microsoft Research Silicon Valley Lab.
Krishnaram has advanced the state-of-the-art in areas such as fairness, explainability, privacy, and robustness in ML, and helped start new research areas (e.g., co-authoring the second paper on Differential Privacy). Leveraging his expertise in research and industry, he has steered company-wide initiatives on responsible AI and AI observability, led the technical roadmap/design/launch of new AI products, and improved existing products via technology transfers. His work has been recognized through awards at NAACL, WWW, SODA, CIKM, ICML AutoML workshop, and Microsoft’s AI/ML conference (MLADS). He has published 60+ papers, with 7500+ citations and filed 150+ patents (71 granted). He has given several invited industry talks, presented tutorials on privacy, fairness, explainable AI, model monitoring, responsible AI, and generative AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, and instructed a course on responsible AI at Stanford, thereby influencing industry thinking, thought leadership, and practice in his areas of work.
Areas: Health AI, Trustworthy Generative AI, AI Observability, AI Safety, 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, Computational Education.