Data Science and Insights

Souvik Ghosh is a Principal Staff Engineer and Scientist at LinkedIn. Before joining LinkedIn, Souvik worked at Yahoo! Research and as an Assistant Professor of Statistics at Columbia University. Souvik completed his PhD applied probability and statistics from Cornell University.

As an expert in probability, statistics and machine learning, Souvik has extensive experience in the research and development of large scale recommender systems. Souvik has numerous publications and patents and he regularly serves in the program committees of conferences like KDD, ICML, NeurIPS and CIKM.

Krishnaram Kenthapadi is the Chief Scientist of Clinical AI at Oracle Health, where he leads AI initiatives for Clinical AI Agent and other Oracle Health products, focusing on modernizing clinical applications, reducing administrative burden for clinicians, and driving healthcare transformation through trustworthy AI. Previously, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, and privacy initiatives in Amazon AI platform. Until recently, 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. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006, and his Bachelors in Computer Science from IIT Madras.

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, NAACL best thematic paper award, CIKM best case studies paper award, SODA best student paper award, and WWW best paper award nomination. He has published 40+ papers, with 2500+ citations and filed 140+ patents (30+ granted). He has presented lectures/tutorials on privacy, fairness, and explainable AI in industry at forums such as KDD '18 '19, WSDM '19, WWW '19, FAccT '20, and AAAI'20 , and instructed a course on AI at Stanford.