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Sreekar Krishna, PhD

National Leader of Artificial Intelligence and Head of Data Engineering, KPMG US

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Sreekar Krishna, PhD

National Leader of Artificial Intelligence and Head of Data Engineering

Dr. Sreekar Krishna is currently working as the National Leader of Artificial Intelligence & Head of Data Engineering within the KPMG Lighthouse in KPMG's Innovation & Enterprise Solutions Division. He specializes in innovative ideas centered around Big Data Machine Learning and Knowledge from Unstructured Data.

Working with KPMG's customers, he has helped evangelize the transition of various industry partners from the traditional data warehousing to a more sophisticated Big Data Analytics driven culture. He specializes in Big Data AI solutions that improve business process and customer engagement. Specifically, he focuses on the core areas of Machine Learning and its associated technologies like Natural Language Processing, Billion-scale Data Processing, Ranking, Information Retrieval and Simulation. 

Previously he worked at Microsoft Bing as a Senior Researcher in the Core Ranking and Relevance team, before which, he held the position of Assistant Research Technologist with the School of Computing, Informatics & Decision Support Engineering, Arizona State University. 

Sreekar has published a book chapter, 5 journals and over 30 conference papers in various academic venues. In the past, he has worked with various research institutions including Samsung SDS Korea and UtopiaCompression Corporation on advancing computer vision algorithms for real-world applications including target tracking, marine obstacle avoidance, face detection in videos and visual knowledge discovery. He also worked at the Indian Institute of Science, Bangalore, India as a research assistant towards developing real-time face detection and person tracking algorithms.

Specialties: Relevance and Ranking, Information Retrieval, Visualization, Hadoop, Data Mining, Distributed Datasets and Distributed Computing, Collaborative Filtering, Machine Learning, Search Technologies, Crowd Sourcing, Pattern Recognition, Computer Vision, Map Reduce, Person Detection and Tracking, Face Recognition.

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