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Data Science and Insights

Souvik Ghosh Souvik Ghosh | Principal Staff Engineer, AI | LinkedIn
Krishnaram Kenthapadi Krishnaram Kenthapadi | Chief Scientist, Clinical AI | Oracle Health

The field of data science often delivers value in multiple ways to a business. One way is in the development of predictive data features such as personalization and recommendations, fraud detection and mitigation, home automation, self-driving cars, etc… Another common way is to drive decisions and insights within a company. For example, did the web or mobile feature you just released increase engagement?

Are we on track to make earnings this quarter? How about more operation-centric insights? Can we detect a complex failure pattern due to a new code release before it takes down our site? Data science can be used to build both internal and external solutions that drive a business. Come to this track to learn about recent innovations in this space that highlight full-stack data science solutions.

Souvik Ghosh
Souvik Ghosh
Principal Staff Engineer, AI | LinkedIn

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
Krishnaram Kenthapadi
Chief Scientist, Clinical AI | Oracle Health

Krishnaram Kenthapadi is the Chief Scientist, Clinical AI at Oracle Health, where he leads the AI initiatives for Clinical Digital Assistant and other Oracle Health products. Previously, as the Chief AI Officer & Chief Scientist of Fiddler AI, he led initiatives on generative AI (e.g., Fiddler Auditor, an open-source library for evaluating & red-teaming LLMs before deployment; AI safety, observability & feedback mechanisms for LLMs in production), and on AI safety, alignment, observability, and trustworthiness, as well as the technical strategy, innovation, and thought leadership for Fiddler. Prior to that, he was a Principal Scientist at Amazon AWS AI, where he led the fairness, explainability, privacy, and model understanding initiatives in the Amazon AI platform, and shaped new initiatives such as Amazon SageMaker Clarify from inception to launch. Prior to joining Amazon, he led similar efforts at the LinkedIn AI team, and served as LinkedIn’s representative in Microsoft’s AI and Ethics in Engineering and Research (AETHER) Advisory Board. Previously, he was a Researcher at Microsoft Research Silicon Valley Lab. Krishnaram received his Ph.D. in Computer Science from Stanford University in 2006. He serves regularly on the senior program committees of FAccT, 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 published 60+ papers, with 7000+ citations and filed 150+ patents (72 granted). He has presented tutorials on trustworthy generative AI, privacy, fairness, explainable AI, model monitoring, and responsible AI at forums such as ICML, KDD, WSDM, WWW, FAccT, and AAAI, given several invited industry talks, and instructed a course on responsible AI at Stanford.

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