Presented By O'Reilly and Cloudera
Make Data Work
September 25–26, 2017: Training
September 26–28, 2017: Tutorials & Conference
New York, NY
Gerard de Melo

Gerard de Melo
Assistant Professor, Rutgers University

Website

Gerard de Melo is an assistant professor of computer science at Rutgers University, where he heads a team of researchers working on big data analytics, natural language processing, and web mining. Gerard’s research projects include UWN/MENTA, one of the largest multilingual knowledge bases, and Lexvo.org, an important hub in the web of data. Previously, he was a faculty member at Tsinghua University, one of China’s most prestigious universities, where he headed the Web Mining and Language Technology Group, and a visiting scholar at UC Berkeley, where he worked in the ICSI AI Group. He serves as an editorial board member for computational intelligence, for the Journal of Web Semantics, the Springer Language Resources and Evaluation journal, and the Language Science Press TMNLP book series. Gerard has published over 80 papers, with best paper or demo awards at WWW 2011, CIKM 2010, ICGL 2008, and the NAACL 2015 Workshop on Vector Space Modeling, as well as an ACL 2014 best paper honorable mention, a best student paper award nomination at ESWC 2015, and a thesis award for his work on graph algorithms for knowledge modeling. He holds a PhD in computer science from the Max Planck Institute for Informatics.

Sessions

Data science & advanced analytics, Machine Learning & Data Science
Location: 1A 06/07 Level: Intermediate
Secondary topics:  Deep learning, Text
Gerard de Melo (Rutgers University)
Average rating: ****.
(4.00, 4 ratings)
How can we exploit the massive amounts of data now available on the web to enable more intelligent applications? Gerard de Melo shares results on applying deep learning techniques to web-scale amounts of data to learn neural representations of language and world knowledge. The resulting resources can be used in Spark to work with text in over 300 languages. Read more.