Fwd: Colloque du DIRO, vendredi le 2 février, 10:30, AA1360 Conférencier: William Hamilton
Job talk for ML position. Candidate has a Stanford PhD. Please attend and give Simon L-J and I feedback. More candidates are coming over the next few weeks. ---------- Forwarded message ---------- From: "Pierre Poulin" poulin@iro.umontreal.ca Date: Jan 31, 2018 11:39 Subject: Colloque du DIRO, vendredi le 2 février, 10:30, AA1360 Conférencier: William Hamilton To: seminaires@iro.umontreal.ca Cc:
Machine Learning for Computational Social Science
par
William Hamilton PhD candidate, Stanford University
Vendredi 2 février, 10:30 à 11:30, salle *** 1360 ***, pavillon André-Aisenstadt Université de Montréal, 2920 chemin de la Tour
Résumé:
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.
Note: La présentation sera donnée en anglais.
Pierre -- Pierre Poulin (poulin@iro.umontreal.ca) Directeur Dept. I.R.O., Universite de Montreal (normal mail) (courier) C.P. 6128, Succ. Centre-Ville 2920, chemin de la Tour, room 2163/2194 Montreal (Quebec) Canada, H3C 3J7 Montreal (Quebec) Canada, H3T 1J4 (514) 343-6780, (514) 343-5834 (fax) http://www.iro.umontreal.ca/~poulin
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Will is great! Attending his talk is highly recommended.
Ioannis
On Jan 31, 2018, at 1:24 PM, Yoshua Bengio yoshua.umontreal@gmail.com wrote:
Job talk for ML position. Candidate has a Stanford PhD. Please attend and give Simon L-J and I feedback. More candidates are coming over the next few weeks. ---------- Forwarded message ---------- From: "Pierre Poulin" <poulin@iro.umontreal.ca mailto:poulin@iro.umontreal.ca> Date: Jan 31, 2018 11:39 Subject: Colloque du DIRO, vendredi le 2 février, 10:30, AA1360 Conférencier: William Hamilton To: <seminaires@iro.umontreal.ca mailto:seminaires@iro.umontreal.ca> Cc:
Machine Learning for Computational Social Science
par
William Hamilton PhD candidate, Stanford University
Vendredi 2 février, 10:30 à 11:30, salle *** 1360 ***, pavillon André-Aisenstadt Université de Montréal, 2920 chemin de la Tour
Résumé:
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.
Note: La présentation sera donnée en anglais.
Pierre
Pierre Poulin (poulin@iro.umontreal.ca mailto:poulin@iro.umontreal.ca) Directeur Dept. I.R.O., Universite de Montreal (normal mail) (courier) C.P. 6128, Succ. Centre-Ville 2920, chemin de la Tour, room 2163/2194 Montreal (Quebec) Canada, H3C 3J7 Montreal (Quebec) Canada, H3T 1J4 (514) 343-6780, (514) 343-5834 (fax) http://www.iro.umontreal.ca/~poulin http://www.iro.umontreal.ca/~poulin
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