L
Leon Wu
Researcher at Columbia University
Publications - 37
Citations - 995
Leon Wu is an academic researcher from Columbia University. The author has contributed to research in topics: Smart grid & Cyber-physical system. The author has an hindex of 15, co-authored 37 publications receiving 920 citations.
Papers
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Journal ArticleDOI
Machine Learning for the New York City Power Grid
Cynthia Rudin,David L. Waltz,Roger N. Anderson,Albert Boulanger,Ansaf Salleb-Aouissi,M. Chow,Haimonti Dutta,Philip Gross,Bert Huang,Steve Ierome,Delfina Isaac,Arthur Kressner,Rebecca J. Passonneau,Axinia Radeva,Leon Wu +14 more
TL;DR: A general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems is introduced, and these models are sufficiently accurate to assist in maintaining New York City's electrical grid.
Posted Content
Semantic Frames to Predict Stock Price Movement
TL;DR: This work introduces a novel tree representation, and uses it to train predictive models with tree kernels using support vector machines, and shows that features derived from semantic frame parsing have significantly better performance across years on the polarity task.
Patent
Forecasting system using machine learning and ensemble methods
TL;DR: In this paper, the techniques for determining forecast information for a resource using learning algorithms are disclosed, which can include an ensemble of machine learning algorithms and can also use latent states to generate training data.
Patent
Machine learning for power grids
Roger N. Anderson,Albert Boulanger,Cynthia Rudin,David L. Waltz,Ansaf Salleb-Aouissi,Maggie Chow,Haimonti Dutta,Phil Gross,Bert Huang,Steve Ierome,Delfina Isaac,Arthur Kressner,Rebecca J. Passonneau,Axinia Radeva,Leon Wu,Peter Hofmann,Frank Dougherty +16 more
TL;DR: In this article, a machine learning system for ranking a collection of filtered propensity to failure metrics of like components within an electrical grid that includes a raw data assembly to provide raw data representative of the like components in the electrical grid is presented.
Proceedings Article
Semantic Frames to Predict Stock Price Movement
TL;DR: This article used semantic frames to predict change in stock price from financial news using support vector machines (SVM) and showed that features derived from semantic frame parsing have significantly better performance across years on the polarity task.