[Lisa_teatalk] Petar Veličković presentation from August 3

Sanjib Manna sanjib_manna at hotmail.com
Mon Aug 6 09:25:33 EDT 2018


Hello

I do not find Petar's presentation "Keeping our graphs attentive" on the MILA Tea Talks page.

Would it be possible to make that available.




Regards,

Sanjib

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Sent: Friday, August 3, 2018 11:00 AM
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Subject: Lisa_teatalk Digest, Vol 87, Issue 2

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Today's Topics:

   1. [Extra Tea Talk] Berton Earnshaw (Recursion Pharmaceuticals)
      Mon Aug 6 12PM AA3195 (Michael Noukhovitch)
   2. Re: [Tea Talk] Petar Veličković (Cambridge / Mila) August
      32018 10:30 AA3195 (Michael Noukhovitch)


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Message: 1
Date: Thu, 2 Aug 2018 21:53:11 -0400
From: Michael Noukhovitch <mnoukhov at gmail.com>
To: mila-tous at mila.quebec, lisa_teatalk at iro.umontreal.ca,
        lisa_seminaires at iro.umontreal.ca, teatalk-orgs at lisa.iro.umontreal.ca
Subject: [Lisa_teatalk] [Extra Tea Talk] Berton Earnshaw (Recursion
        Pharmaceuticals) Mon Aug 6 12PM AA3195
Message-ID:
        <CAMVr0VwfVewOL3E_CS8+zAN+BAd0V1zDA2+B+iGOvuk7hoFrBg at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Early next week we have an extra talk by *Berton Earnshaw*, Senior Director
of Data Science Research at *Recursion Pharmaceuticals* giving a talk
on *Monday
August 6 2018* at *12pm - 1pm* in room *AA3195.* This will be a lunch talk
and mingle with *food served!*

Will this talk be streamed <https://mila.bluejeans.com/809027115/webrtc>?
Yes

Want to meet with Berton in the afternoon?
https://calendar.google.com/calendar/selfsched?sstoken=UVBVTVF0X25Nd09LfGRlZmF1bHR8ZWQ5MmNlYWMxODI4OWVkNmUzNGU3OTE4ZDExMGI0YTk

Come for the food and machine learning, stay because the talk will be
awesome.
Michael

*TITLE *Representation learning in human cellular biology

*KEYWORDS* bioinformatics, representation learning

*ABSTRACT*
The automated labs at Recursion Pharmaceuticals currently run more than
100,000 experiments every week, and each experiment involves imaging the
morphology of human cells under various biological and chemical
perturbations. The resulting data set is the largest and most diverse of
its kind, and is used to train networks that learn to predict which
compounds treat which diseases, and why. In this talk, I will describe the
challenges of learning on biological image sets, discuss some approaches we
have taken to learning representations of these images, and describe how we
use these representations to discover candidate treatments for disease.

*BIO *
Berton earned a PhD in math from the University of Utah in 2007, where he
designed biophysical models of protein trafficking at synapses during
episodes of learning and memory formation. In 2010, after two post-docs at
Utah and Michigan State University spent developing the theory of
non-autonomous master equations, Berton took some time off of academia and
co-founded the call center software company Perfect Pitch where he led as
CTO. In 2012 he joined the founding team of Red Brain Labs as Director of
Operations and Data Science, where he worked to optimize call centers using
machine learning and simulation. In 2014, Red Brain Labs was acquired by
Savvysherpa where, as a Senior Scientist and Principal, Berton led various
research projects applying machine learning to insurance claims and medical
records with the goal of improving healthcare outcomes. In 2017, Berton
joined Recursion Pharmaceuticals as Senior Director of Data Science
Research, where he leads the research team in applying deep learning to
millions of images of human cells in search of treatments for hundreds of
diseases.

*COMPANY BIO*
At Recursion Pharmaceuticals, we have raised over $80M to apply machine
learning to one of the most unique datasets in existence - tens of millions
of images of cells under a huge number of biological and chemical
perturbations, generated in our own labs - in order to find treatments for
hundreds of diseases. Our long term mission is to decode biology to
radically improve lives - we want to understand biology so well that we can
fix most things that go wrong in our bodies.
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Message: 2
Date: Fri, 3 Aug 2018 10:31:07 -0400
From: Michael Noukhovitch <mnoukhov at gmail.com>
To: mila-tous at mila.quebec, lisa_teatalk at iro.umontreal.ca,
        lisa_seminaires at iro.umontreal.ca, teatalk-orgs at lisa.iro.umontreal.ca
Subject: Re: [Lisa_teatalk] [Tea Talk] Petar Veličković (Cambridge /
        Mila) August 32018 10:30 AA3195
Message-ID:
        <CAMVr0VyJjSYECXhPundbPtBhaOhd9j1LQ41uudOet9oq5SA93w at mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Reminder this is now

On Tue, Jul 31, 2018, 16:12 Michael Noukhovitch <mnoukhov at gmail.com> wrote:

> Correction for the streaming link
> https://mila.bluejeans.com/809027115/webrtc
>
> On Tue, Jul 31, 2018, 15:58 Michael Noukhovitch <mnoukhov at gmail.com>
> wrote:
>
>> This week we have *Petar Veličković * from * Cambridge/Mila * giving a
>> talk on *August 3rd 2018* at *10:30* in room *AA3195*
>>
>> Will this talk be streamed <https://mila.bluejeans.com/1365483656/webrtc>?
>> yes
>>
>> Come to the talk and make sure to pay attention, because this talk should
>> be great to connect the dots!
>> Michael
>>
>> *TITLE* Keeping our graphs attentive
>>
>> *KEYWORDS *graph structured, attention, computational biology
>>
>> *ABSTRACT*
>> A multitude of important real-world datasets (especially in biology) come
>> together with some form of graph structure: social networks, citation
>> networks, protein-protein interactions, brain connectome data, etc.
>> Extending neural networks to be able to properly deal with this kind of
>> data is therefore a very important direction for machine learning research,
>> but one that has received comparatively rather low levels of attention
>> until very recently. Attentional mechanisms represent a very promising
>> direction for extending the established convolutional operator on images to
>> work on arbitrary graphs, as they satisfy many of the desirable features
>> for a convolutional operator. Through this talk, I will focus on my work on
>> Graph Attention Networks (GATs), where these theoretical properties have
>> been further validated by solid results on transductive as well as
>> inductive node classification benchmarks. I will also outline some of the
>> earlier efforts towards deploying attention-style operators on graph
>> structures, as well as very exciting recent work that expands on GATs and
>> deploys them in more general circumstances (such as EAGCN, DeepInf, and
>> applications to solving the Travelling Salesman Problem). Time permitting,
>> I will also present some of the relevant related graph-based work in the
>> computational biology and medical imaging domains that I have been involved
>> in.
>>
>> *BIO*
>> Petar Veličković is currently a final-year PhD student in Machine
>> Learning and Bioinformatics at the Department of Computer Science and
>> Technology of the University of Cambridge. He also holds a BA degree in
>> Computer Science from Cambridge, having completed the Computer Science
>> Tripos in 2015. In addition, he has been involved in research placements at
>> Nokia Bell Labs (working with Nicholas Lane) and the Montréal Institute of
>> Learning Algorithms (working with Adriana Romero and Yoshua Bengio). His
>> current research interests broadly involve devising neural network
>> architectures that operate on nontrivially structured data (such as
>> graphs), and their applications in bioinformatics and medicine. He has
>> published his work in these areas at both machine learning venues (ICLR,
>> NIPS ML4H, ICML WCB) and biomedical venues and journals (Bioinformatics,
>> PervasiveHealth)
>>
>
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