Google

Google Cloud

Data Science Day 2018

Wednesday, March 28, 2018 (All day)
Columbia University
New York, NY 10027
United States

Data Science Day 2018

Wednesday, March 28, 2018
9AM–5PM

Celebrating 5 years of Data Science at Columbia University

Join us for demos and lightning talks by Columbia researchers presenting their latest work in data science. The event provides a forum for innovators in academia, industry and government to connect.

Roone Arledge Auditorium
Lerner Hall | Columbia University
2920 Broadway, New York, NY 10027

Networking reception for industry, faculty and students following the event.

Featuring a Keynote Address by Diane Greene, Google Cloud CEO

Yoram Singer

Google
Senior Research Scientist

Dr. Yoram Singer is a senior research scientist at Google. From 1999 through 2007, he was an Associate Professor at the Hebrew University of Jerusalem. From 1995 through 1999 he was a member of the technical staff at AT&T Research. He was also the co-chair of the conference on Computational Learning Theory (COLT) in 2004 and of Neural Information Processing Systems (NIPS) in 2007. He served as an editor of Machine Learning Journal (MLJ), Journal of Machine Learning (JMLR), IEEE Signal Processing Magazine (SPM), and IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI). His collaborative work with colleagues won several awards including the best 10 year retrospect machine learning paper and three best student paper awards at NIPS. He has been an AAAI fellow since 2011.

Learning Compact Models from High Dimensional Large Datasets

Monday, April 13, 2015 - 7:00am to 8:00am
United States

Dr. Yoram Singer reviews the design, analysis, and implementation of stochastic optimization techniques, online algorithms, and modeling approaches for learning in high dimensional spaces using large amounts of data. His focus is on algorithms and models that are efficient, accurate, and yield compact models. Concretely, his group describes the forward-backward shrinkage algorithm (Fobos), mirror descent for learning composite objectives (COMID), and the stonking adaptive gradient (AdaGrad) algorithm.

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