Presented By
O’Reilly + Cloudera
Make Data Work
March 25-28, 2019
San Francisco, CA
Sricharan Kumar

Sricharan Kumar
Principal Data Scientist , Intuit


Kumar Sricharan is a principal data scientist responsible for leading Intuit’s Machine Learning (ML) Research Group. His team focuses on cutting-edge ML problems and applications for financial artificial intelligence, combining financial domain knowledge with information in data to produce more accurate and explainable systems. Projects include extraction of information from financial documents, chatbots for financial conversations, mining tax forms to enable compliance, understanding transactions to power financial advice, and financial forecasting. Previously, he spent five years at Xerox PARC as a senior research scientist and program manager for self-learning systems. During his time at PARC, Kumar led a team focused on building learning algorithms that can exploit rich feedback in conjunction with unlabeled data to overcome the need for prohibitively large numbers of labeled examples and make these systems explainable. His other research interests include anomaly detection for large, unstructured data and nonparametric large sample estimation, and his research has resulted in nearly 30 papers in refereed conferences and journals and several accompanying patents. He’s an active member of the academic community and has served as a reviewer for multiple conferences and journals. He has also participated in several DARPA research projects, including the ADAMS program for detecting insider threat and XAI for building explainable AI agents. Kumar holds a PhD in electrical engineering from the University of Michigan.


11:50am12:30pm Thursday, March 28, 2019
Sricharan Kumar (Intuit )
Average rating: ****.
(4.29, 7 ratings)
Machine learning is delivering immense value across industries. However, in some instances, machine learning models can produce overconfident results—with the potential for catastrophic outcomes. Kumar Sricharan explains how to address this challenge through Bayesian machine learning and highlights real-world examples to illustrate its benefits. Read more.