speaker-info

Vikas Agrawal

Senior Principal Data Scientist @ Oracle Analytics Cloud

Vikas Agrawal works as a Senior Principal Data Scientist in Cognitive Computing for Oracle Analytics Cloud. His current interests are in automated discovery, adaptive anomaly detection in streaming data, intelligent context-aware systems, and explaining black-box model predictions. Vikas credits his continued learning to smart creative colleagues and mentors at Intel Corporation, Infosys Limited, and Oracle Corporation, where they researched and developed novel IP, products, and solutions to amplify humans such as:

  • Activity context-aware virtual personal assistants with N=1 personalization for insurance, pharma, retail and investment banks with risk and fraud detection
  • Real-time asset management, predictive maintenance, and reliability risk prediction systems using Internet of Things (IoT) driven data-streams in mining and production systems for eliminating down-time, waste, and delay in manufacturing with explanability.
  • Automated discovery, anomaly detection and guided modeling systems for scientific analysis of HCM, CRM, ERP and SCM datasets.

Vikas received a B.Tech. in Electrical Engineering from the Indian Institute of Technology, New Delhi (1997), an MS in Computer Science and a PhD in Computational Modeling from University of Delaware, with post-doctoral research at California Institute of Technology (CalTech, Pasadena, CA) with colleagues from NASA’s Jet Propulsion Labs (JPL) for NSF’s FIBR. http://bigdata-madesimple.com/10-indian-data-scientists-you-should-know/

My Sessions

Why Should You Trust My Model Predictions?

Auditorium

Machine learning and deep learning algorithms are getting increasingly useful and complex to understand. In some industries like banking, credit cards and pharmaceuticals, the explanations for predictions or decision rules derived from them are required by regulatory agencies.   In this session you shall learn how “LIME: Local Interpretable Model- Agnostic Explanations” can be used to […]

Machine Learning
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