[Lisa_seminaires] [Tea Talk] William Hamilton (Stanford) Fri Feb 2, 10:30AM, AA1360

Michael Noukhovitch mnoukhov at gmail.com
Mer 31 Jan 13:50:01 EST 2018


*Note:* This is the same as the DIRO colloqium email, I'm just making sure
that everyone on the mailing lists gets it!

Sorry for the late email! This week we have a PhD Candidate from
Stanford, *William
Hamilton,* giving a talk on *Friday Feb 2* at* 10:30AM* in room *AA1360*.

Make sure to chart your way to this tea talk and invite all your friends!
Michael

*KEYWORDS *ML on Graphs, Massive Structured Datasets, Social Science

*TITLE* Machine Learning for Computational Social Science

*ABSTRACT*
The combination of machine learning and massive social datasets has the
potential to revolutionize our ability to predict and understand human
behavior. However, machine learning on large social datasets is difficult
because these datasets tend to be noisy, dynamic, and involve
graph-structured relationships (e.g., social networks between users)—while
traditional machine learning tools are largely designed for static datasets
comprised of simple Euclidean vectors or grids.

In this talk, I will describe new methods that I have developed for machine
learning on massive, graph-structured social datasets. The technical focus
of the talk will be on techniques for graph embedding, i.e., representation
learning on graph-structured data. In the first part, I will describe how I
have used graph embedding techniques to enable diverse social
applications—from modeling cultural change to predicting conflict between
online communities. In the second part, I will describe a new graph
embedding framework, called GraphSAGE, that can scale to datasets that are
orders of magnitude larger than previous approaches and that has been
deployed at the website Pinterest to power a recommender system serving
over 200 million users. I will close the talk with a general outlook on
social AI technologies, including future technical directions and important
ethical considerations.

*BIO*
William (Will) Hamilton is a PhD Candidate in Computer Science at Stanford
University, working jointly in the NLP and SNAP groups. He is co-advised by
Dan Jurafsky and Jure Leskovec, and his interests lie at the intersection
of machine learning, network science, natural language processing, and
computational social science. Will's research is supported by the SAP
Stanford Graduate Fellowship and an NSERC PGS-D Grant. Prior to coming to
Stanford, Will completed a BSc and MSc at McGill University, where he
studied machine learning under the supervision of Joelle Pineau and was the
2014 recipient of the Canadian AI Master's Thesis Award.
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