Machine Learning • Data Mining • Statistical Modeling • Data Analysis • Linux • Social Networks • Information Retrieval • Recommendation Systems • Statistical Data Analysis • Research
Intuit
Senior Data Scientist
Insight Data Science Jan 2018 - Jun 2018
Data Scientist Fellow
University of California, San Diego Jul 2017 - Jun 2018
Post Doctoral Reseacher
University of San Diego Aug 2016 - Dec 2017
Adjunct Professor
University of California, San Diego 2011 - 2017
Graduate Student Reseacher
Education:
Uc San Diego 2011 - 2017
Doctorates, Doctor of Philosophy, Computer Engineering, Philosophy
University of Wisconsin - Madison 2007 - 2009
Masters
Rutgers University 2003 - 2007
Bachelors, Electronics, Engineering, Communications
Ps Senior Secondary School (Chennai, India)
Skills:
Machine Learning Data Mining Statistical Modeling Data Analysis Linux Social Networks Information Retrieval Recommendation Systems Statistical Data Analysis Research
Us Patents
Computer Estimations Based On Statistical Tree Structures
- Mountain View CA, US Janani Kalyanam - San Diego CA, US Leah Zhao - San Diego CA, US Peter Ouyang - Del Mar CA, US
Assignee:
Intuit Inc. - Mountain View CA
International Classification:
G06F 16/23 G06F 16/22 G06F 16/2458
Abstract:
A method for computer estimations based on statistical tree structures involves obtaining a statistical tree structure for reference elements. The statistical tree structure includes leaf nodes segmenting a statistic for a data label according to data features in the reference elements, and intermediate nodes connecting a first node to the leaf nodes. Each of the first node and the intermediate nodes provide a branching based on one of the data features. The method further includes obtaining target data, including values for the data features, and a value for the data label. The method also includes selecting the first node, associated with a first data feature, traversing the statistical tree structure to a leaf node by matching the values of the data features to the branching of the intermediate nodes, and assessing the value for the data label in the target data based on the statistic associated with the leaf node.
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