EIC computing joint activities

[Jerome LAURET <jeromel AT bnl.gov>]

        Hi Mark,

        Thanks a bunch for your interest. I discussed with Nicole and
Meifeng and we are going to look into this i.e. if we can have it
recorded (technically yes but we need the OK from the speakers as
a first starting point and initiated the process). Will keep you
all informed.

        Best regards,


On 2021-11-17 11:11, mdbaker AT mdbpads.com wrote:
> Jerome,
>
> Will this be recorded (At least the presentations)?
>
> Mark
>
> On 2021-11-17 10:04, Jerome LAURET via Eic-comp-l wrote:
> > -------- Forwarded Message --------
> > Subject:     Beginner Series of the Machine Learning (ML) and Artificial
> > Intelligence (AI) for Scientists Tutorials (MAST)
> > Date:     Wed, 17 Nov 2021 16:02:20 +0000
> > From:     Medaglia, Nicole <medaglia AT bnl.gov>
> >
> >
> >
> > Good morning,
> >
> > You are invited to attend the Beginner Series of the Machine Learning
> > (ML) and Artificial Intelligence (AI) for Scientists Tutorials (MAST),
> > to be held online via Zoom on *December 6-15, 2021, at noon-2pm EST
> > every other day*. The MAST series is organized by the Brookhaven
> > National Laboratory’s Computational Science Initiative, in
> > coordination with the BNL AI and ML working group.
> >
> > *Registration: *
> >
> > To register for the tutorials, please go to
> > https://indico.bnl.gov/e/mast2021 <https://indico.bnl.gov/e/mast2021>.
> > The registration is free but required for participation. In accordance
> > with the BNL’s Guests, Users and Visitors’ rules, if you do not have
> > an active BNL employee or guest ID, you may be required to submit your
> > information through BNL’s Guest Information System (GIS):
> > http://www.bnl.gov/guv <http://www.bnl.gov/guv>. It may take 2 weeks 
> > to get
> > your registration approved, so please register early if you fall under
> > this category.
> >
> > The deadline for registration is *11:59pm EST, December 3, 2021*.
> >
> > *Event Description:*
> >
> > Machine learning and data science-driven techniques have proven to be
> > transformative methods in the last decade. Lying at the heart of
> > Brookhaven National Laboratory (BNL) and the Department of Energy's
> > computational initiatives, we believe that the applications of these
> > techniques have major potential in almost every field of science. To
> > this end, the Computational Science Initiative (CSI) recently
> > christened the Center for Computing Sciences Education and Support
> > (CCSES) program to help enable BNL personnel and their collaborators
> > leverage machine learning and data-driven methods to accelerate their
> > own research. As one of the first efforts made by CCSES, we are
> > excited to present the first of a three-part tutorial series on AI and
> > machine learning. This first series, intended for beginners in
> > software and/or machine learning, will cover the following
> > introductory topics:
> >
> >  1. Basic Python, including installation and usage
> >  2. Basic NumPy (numerical Python), including usage and best practices
> >  3. Introduction to Machine Learning
> >  4. Introduction to automatic gradient-enabled tensors using PyTorch,
> >     and your first neural networks
> >  5. A first use case: convolutional neural networks for image 
> > classification
> >
> > The speakers are early-career researchers in CSI's AI / machine 
> > learning group.
> >
> > Speakers:
> >
> >   * David Dakota Blair
> >   * Matthew Carbone
> >   * Yi Huang
> >   * Sandeep Mittal
> >   * Yihui (Ray) Ren
> > We look forward to your participation!
> >
> > Best Regards,
> >
> > The MAST Organizing Committee
> >
> > */Nicole Medaglia/*
> >
> > */Administrative Assistant/ Proposal Coordinator/*
> >
> > */Brookhaven National Laboratory/*
> >
> > */Computational Science Initiative/*
> >
> > */Building 725 – Room 2-135/*
> >
> > */Phone:  631-344-6245/*
> >
> > */_medaglia AT bnl.gov <mailto:medaglia AT bnl.gov>_/*
> >
> > signature_179879281 <https://www.bnl.gov/>
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> > >//
> >
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--
             ,,,,,
            ( o o )
         --m---U---m--
             Jerome
          <he-him-his>