Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics

America/New_York
2nd Floor (Ingrid Daubechies Auditorium)

2nd Floor

Ingrid Daubechies Auditorium

162 5th avenue, 2nd floor, New York NY, 10010
Alex Barnett
Description

FWAM

Flatiron-wide Algorithms and Mathematics (FWAM) is a 2.5 day internal conference with the goal of overviewing/introducing a range of numerical algorithms and tools that are essential to research done at Flatiron and beyond. We also aim to form research connections across (and within) the centers, and showcase some of the research which makes use of these methods. Topics have been chosen that are crucial to two or more centers. There are five half-day topics; each begins with at least one accessible, practical, introductory lecture, then short talks that may teach sub-topics or applications to research.

 

Organization team:

Admin: Marian Jakubiak         SCC: Andras Pataki, Pat Gunn

CCA: Gabriella Contardo, Keaton Burns, Dan Foreman-Mackey

CCB: Mike Shelley, Mariano Gabitto

CCM: Manas Rachh, Alex Barnett

CCQ: Olivier Parcollet, Guiseppe Carleo

Wrangler-in-chief: Alex Barnett

 

ZOOM DETAILS IF JOINING REMOTELY:

Join from PC, Mac, Linux, iOS or Android: https://simonsfoundation.zoom.us/j/536451221

Or Telephone:
    Dial(for higher quality, dial a number based on your current location):
        US: +1 646 558 8656  or +1 669 900 6833
    Meeting ID: 536 451 221
    International numbers available: https://zoom.us/u/ahu1dujLA

    • 08:15 09:00
      Breakfast 45m
    • 09:00 09:10
      Welcome
    • 09:10 10:10
      Optimization Introductory Lecture 1
      Convener: Dr Alex Barnett
      • 09:10
        TBD 1h
        Speaker: Christian Mueller
    • 10:10 10:20
      Break
    • 10:20 11:20
      Optimization Introductory Lecture 2
      • 10:20
        Tutorial: Optimization for Machine Learning 1h
        Speaker: Elad Hazan
    • 11:20 11:40
      Break
    • 11:40 12:30
      Optimization Short Talk
      • 11:40
        A practical introduction to adjoint methods 25m
        Speaker: Leslie Greengard
      • 12:05
        TBD 25m
        Speaker: Megan Bedell (CCA)
    • 12:30 14:00
      Lunch- 1h 30m
    • 14:00 14:45
      Function Approximation and Differential Equations Introductory Lecture
      • 14:00
        Introduction to interpolation, integration and spectral methods 45m

        TBD

        Speaker: Alex Barnett
    • 14:45 14:55
      Break
    • 14:55 15:40
      Function Approximation and Differential Equations Introductory Lecture
      • 14:55
        Overview of various methods to solve differential equations 45m
        Speaker: Keaton Burns (MIT / CCA)
    • 15:40 16:00
      Break
    • 16:00 17:15
      Deep Learning Short Talks
      • 16:00
        PDEs: The long and the short. 25m
        Speaker: Michael Shelley
      • 16:25
        TBD 25m
        Speaker: Jun Wang
      • 16:50
        TBD 25m
        Speaker: Joakim Andén
    • 17:15 18:15
      Reception 1h
    • 08:15 09:00
      Breakfast 45m
    • 09:00 09:45
      Sampling Introductory Lecture
      • 09:00
        TBD 45m
        Speaker: Dan Foreman-Mackey
    • 09:45 09:55
      Break
    • 09:55 10:40
      Sampling Introductory Lecture
      • 09:55
        TBD 45m
        Speaker: Mariano Gabitto
    • 10:40 11:00
      Break
    • 11:00 11:50
      Sampling Short Talk
      • 11:00
        TBD 30m
        Speaker: Prof. Shiwei Zhang (CCQ)
    • 11:50 12:10
      Sampling Short Talk
      • 11:50
        TBD 20m
        Speaker: Emily Cunningham (CCA)
    • 12:10 14:00
      Lunch 1h 50m
    • 14:00 14:45
      Deep Learning Introductory Lecture
      • 14:00
        TBD 45m
        Speaker: Gabriella Contardo
    • 14:45 14:55
      Break
    • 14:55 15:40
      Deep Learning Introductory Lecture
      • 14:55
        TBD 45m
        Speaker: Shirley Ho
    • 15:40 16:00
      Break
    • 16:00 17:15
      Deep Learning Short Talks
      • 16:00
        TBD 25m
        Speaker: Laurence Levasseur
      • 16:25
        TBD 25m
        Speaker: Giuseppe Carleo
      • 16:50
        TBD 25m
        Speaker: Mitya Chklovskii
    • 17:15 18:15
      Reception 1h
    • 08:30 09:15
      Breakfast 45m
    • 09:15 10:30
      Dimension Reduction and Factorization Short Talk
      • 09:15
        TBD 25m
        Speaker: Eftychios Pnevmatikakis (CCM)
      • 09:40
        TBD 25m
        Speaker: Marina Spivak
      • 10:05
        Clustering in low dimensions 25m
        Speaker: Jeremy Magland
    • 10:30 10:50
      Break
    • 10:50 11:35
      Dimension Reduction and Factorization Introductory Lecture
      • 10:50
        TBD 45m
        Speaker: Manas Rachh
    • 11:35 12:25
      Dimension Reduction and Factorization Short Talk
      • 11:35
        The why and how of nonnegative matrix factorization 25m

        Nonnegative matrix factorization (NMF) has become a widely used tool for the analysis of high-dimensional data as it automatically extracts sparse and meaningful features from a set of nonnegative data vectors. I first illustrate this property of NMF on some applications. Then I address the problem of solving NMF, which is NP-hard in general, and review some standard NMF algorithms. Finally, I briefly describe an online NMF algorithm, which scales up gracefully to large data sets.

        Speaker: Johannes Friedrich
      • 12:00
        Introduction to Tensor Network Methods 20m

        Tensor network methods are a family of variational algorithms used to simulate many body quantum systems in a variety of situations. With some brief motivation from physics, I'll explain why anyone would want to use these methods, why it is that they are so effective for certain classes of problems, and some extensions to other fields like machine learning.

        Speaker: Katharine Hyatt
    • 12:25 12:30
      No Lunch will be served: please order from Seamless 5m