[Lisa_seminaires] [Tea Talk] Wengong Jin (MIT) August 10 2018 10:30 AA3195

Michael Noukhovitch mnoukhov at gmail.com
Mar 7 Aou 15:48:31 EDT 2018


This week we have *Wengong Jin * from * MIT * giving a talk on *August
10 2018* at *10:30* in room *AA3195*

Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>?
yes
Wengong is free to meet until 1:30pm so email me if you'd be interested in
grabbing lunch with him or talking with him before the talk.

If you're interested in the field, I'm certain the talk will get a great
reaction!
Michael

*TITLE* Graph Representation Learning for Chemistry

*KEYWORDS *computational chemistry, graph learning, generative models

*ABSTRACT*
Learning representation for molecules and reactions is crucial for
computational chemistry. Traditional methods, based on expert designed
molecular fingerprints or reaction templates, have lead to suboptimal
predictive performance and poor scalability. In this talk, I will present
our recent effort in dealing with these challenges. Particularly, I will
demonstrate two novel approaches, showing how graph neural network could
overcome these challenges, and how domain knowledge could help us design
better graph neural network architectures. In the first part of the talk, I
will describe a graph convolutional network (with self-attention) for
predicting organic reaction outcomes. Our model significantly outperforms
traditional template-based methods, approaching expert chemist performance
while running 100x faster. In the second part of the talk, I will present a
generative model called Junction Tree Variational Autoencoder for molecular
graph generation, and show how it could be used for drug discovery.

*BIO*
Wengong Jin is currently a PhD student in Computer Science and Artificial
Intelligence Lab (CSAIL) at MIT, working with Prof. Regina Barzilay and
Tommi Jaakkola. Before he finished his Master of Science degree from MIT in
2018, and Bachelor of Engineering from Shanghai Jiao Tong University in
2016. His research interest lies in machine learning, with particular focus
on deep learning methods for graphs. His recent research on molecular graph
generation has been covered as spotlight news in MIT homepage. He is also
honored with 2018 Ho-Ching and Han-Ching scholarship.
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