Yongyao Jiang

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Ph.D. candidate in Geoinformation Science with over five years of experience in geospatial technologies, search engine, machine learning, high performance computing and object oriented software development. At GMU, I have worked as the technical lead for a couple of search engine, machine learning, and Web crawling projects funded by NASA and NSF. The MUDROD search engine for satellite imagery I developed on the top of Elasticsearch is the first attempt of using machine learning to improve data discovery in the Earth science domain.

nlp search engine earth science knowledge management mobility health polar cyberinfrastructure

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MUDROD - Mining and Utilizing Dataset Relevancy from Oceanographic Dataset Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access

Há 6 anos • 156 Visualizações

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Yongyao Jiang

Apresentações

MUDROD - Ranking

A Knowledge Discovery Framework for Planetary Defense

Polar CI: A Clound based Polar Resource Discovery Engine

Automated mobility mode detection based on GPS tracking data

MUDROD - Mining and Utilizing Dataset Relevancy from Oceanographic Dataset Metadata, Usage Metrics, and User Feedback to Improve Data Discovery and Access

Earth science knowledge graph

Gostaram

Building a real time big data analytics platform with solr

Lucene/Solr Revolution 2015: Where Search Meets Machine Learning

Leveraging Lucene/Solr as a Knowledge Graph and Intent Engine: Presented by Trey Grainger, CareerBuilder

Better Search Through Query Understanding

Techniques For Deep Query Understanding

Intro to Graph Databases Using Tinkerpop, TitanDB, and Gremlin