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 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 a Principal Scientist at Amazon AWS AI, where he leads 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 (https://sites.google.com/view/privacy-tutorial
), fairness (https://sites.google.com/view/fairness-tutorial
), and explainable AI (https://sites.google.com/view/explainable-ai-tutorial
) 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.