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

Michael Noukhovitch mnoukhov at gmail.com
Ven 10 Aou 10:16:24 EDT 2018


This is in 15!

On Tue, Aug 7, 2018, 15:48 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> 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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