Presented By O’Reilly and Cloudera
Make Data Work
March 5–6, 2018: Training
March 6–8, 2018: Tutorials & Conference
San Jose, CA
Nina Mishra

Nina Mishra
Principal Scientist, Amazon Web Services

Nina Mishra is principal scientist at Amazon Web Services, where she focuses on data science, data mining, web search, machine learning, and privacy. Nina has many years of experience leading projects at Amazon, Microsoft Research, and HP Labs. She was also an associate professor at the University of Virginia and an acting faculty member at Stanford University. Nina’s research encompasses the design and evaluation of new data mining algorithms on real, colossal-sized datasets. She has authored almost 50 publications in top venues, including WWW, WSDM, SIGIR, ICML, NIPS, AAAI, COLT, VLDB, PODS, CRYPTO, EUROCRYPT, FOCS, and SODA, which have been recognized with best paper award nominations. Nina’s research was central to the Bing search engine and has been widely featured in external press coverage. Nina holds 14 patents with a dozen more still in the application stage. She has had the distinct privilege of helping others advance in their careers, including 15 summer interns and many full-time researchers. Nina’s service to the community includes serving on journal editorial boards Machine Learning, the Journal of Privacy and Confidentiality, IEEE Transactions on Knowledge and Data Engineering, and IEEE Intelligent Systems and chairing the premier machine learning conference ICML in 2003, as well as serving on numerous program committees for web search, data mining, and machine learning conferences. She was awarded an NSF grant as a principal investigator and has served on eight PhD dissertation committees.


5:10pm5:50pm Wednesday, March 7, 2018
Secondary topics:  Graphs and Time-series
Roger Barga (Amazon Web Services), Nina Mishra (Amazon Web Services), Sudipto Guha (Amazon Web Services), Ryan Nienhuis (Amazon Web Services)
Average rating: *****
(5.00, 8 ratings)
Roger Barga, Nina Mishra, Sudipto Guha, and Ryan Nienhuis detail continuous machine learning algorithms that discover useful information in streaming data. They focus on explainable machine learning, including anomaly detection with attribution, the ability to reduce false positives through user feedback, and the detection of anomalies in directed graphs. Read more.