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Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics

October 30, 2019 to November 1, 2019
Ingrid Daubechies Auditorium
America/New_York timezone

Marian Jakubiak

Contribution List

24 out of 24 displayed
  1. 7. Optimization landscapes - a gentle multi-disciplinary introduction to optimization
    Christian L. Mueller (CCM / LMU)
    10/30/19, 9:10 AM

    In this lecture, I will give an introduction to the field of continuous optimization. I will emphasize instances of optimization problems that appear in biology and physics through the concept of optimization landscapes. I will review sampling-based approaches as well as gradient-based methods and focus on concepts rather than derivations of specific algorithms. The lecture is intended to set...

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  2. 8. Tutorial: Optimization for Machine Learning
    Elad Hazan (Princeton U)
    10/30/19, 10:20 AM

    A 1-hour version of the MLSS Buenos Aires tutorial notes, focusing on parts 1 and 4, available at the links below.

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  3. 9. A practical introduction to adjoint methods
    Leslie Greengard (CCM / NYU)
    10/30/19, 11:40 AM
  4. 10. Research Applications of Optimization: Data-Driven Spectroscopy
    Megan Bedell (CCA)
    10/30/19, 12:05 PM
  5. 11. Introduction to interpolation, integration and spectral methods
    Alex Barnett (CCM)
    10/30/19, 2:00 PM

    I overview key concepts and practical methods for efficient and accurate numerical function approximation, integration and differentiation. This is the basis for spectral and other ODE/PDE solvers coming up in the next talk. I will teach concepts such as convergence rate, local/global, adaptivity, rounding error, polynomial and Fourier bases. The focus is on 1D, with pointers to...

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  6. 12. Overview of various methods to solve differential equations
    Keaton Burns (MIT / CCA)
    10/30/19, 3:10 PM

    We overview various numerical methods to solve ODEs and PDEs.

    For source of notes see: https://github.com/ahbarnett/fwam-numpde

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  7. 13. PDEs: The long and the short.
    Michael Shelley (CCB)
    10/30/19, 4:00 PM
  8. 14. Introduction to Integral Equation Methods
    Jun Wang (CCM)
    10/30/19, 4:25 PM
  9. 15. Wavelets
    Joakim Andén (CCM)
    10/30/19, 4:50 PM
  10. 16. Introduction to Markov chain Monte Carlo
    Dan Foreman-Mackey (CCA)
    10/31/19, 9:00 AM
  11. 17. Scalable Bayesian Inference.
    Mariano Gabitto (CCB)
    10/31/19, 9:55 AM

    In this short tutorial, I will review variational inference (VI), a method to approximate posterior probability distributions through optimization. VI became popular as it provides faster convergence than more traditional sampling methods.

    This tutorial aims to provide both an introduction and an overview of recent developments. First, I will provide a review of variational inference....

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  12. 18. The quantum-ness in quantum Monte Carlo: mathematical and algorithmic implications
    Prof. Shiwei Zhang (CCQ)
    10/31/19, 11:00 AM
  13. 19. Hierarchical Modeling and Stellar Velocities
    Emily Cunningham (CCA)
    10/31/19, 11:50 AM

    An introduction to Bayesian hierarchical modeling, with an example from my own research modeling repeated velocity measurements of distant stars in the Milky Way.

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  14. 20. Introduction to Deep Learning
    Gabriella Contardo (CCA)
    10/31/19, 2:00 PM

    A Deep Learning 101 to get familiar with machine/deep learning principles, neural networks, back-propagation, convolution nets and representation learning.

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  15. 21. Introduction to Deep Learning
    Shirley Ho (CCA)
    10/31/19, 2:55 PM

    We will introduce state of the art deep learning methods and showcase some of its applications to astrophysical challenges.

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  16. 24. Uncertainty Estimation with Neural Networks
    Laurence Levasseur (CCA / U. de Montreal)
    10/31/19, 4:00 PM
  17. 22. Deep Generative Modeling for Statistical and Quantum Physics
    Giuseppe Carleo (CCQ)
    10/31/19, 4:25 PM
  18. 23. Biological neural network algorithms
    Mitya Chklovskii (CCB)
    10/31/19, 4:50 PM

    Although traditional artificial neural networks were inspired by the brain they resemble biological neural networks only superficially. Successful machine learning algorithms like backpropagation violate fundamental biophysical observations suggesting that the brain employs other algorithms to analyze high-dimensional datasets streamed by our sensory organs. We have been developing...

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  19. 26. Randomized linear algebra and matrix approximation
    Eftychios Pnevmatikakis (CCM)
    11/1/19, 9:15 AM

    The goal of this talk is to show how probabilistic methods can be used to accelerate standard matrix factorizations (e.g. SVD) with provable characteristics in terms of speed and accuracy.

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  20. 27. Spectral Clustering and Dimensionality Reduction
    Marina Spivak (CCM)
    11/1/19, 9:40 AM
  21. 28. Clustering in low dimensions
    Jeremy Magland (CCM)
    11/1/19, 10:05 AM

    I will focus on clustering data points in low dimensions (mostly 2d) and provide an overview of some popular clustering algorithms.

    The accompanying live notebook is linked from my homepage: https://users.flatironinstitute.org/~magland

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  22. 25. Fast algorithms for hierarchically structured matrices
    Manas Rachh (CCM)
    11/1/19, 10:50 AM

    In this talk, I will discuss what hierarchically structured matrices are, where they occur in practice, and present algorithms for factorizing these structured matrices. I will demonstrate how the factorization enables subsequent matrix operations (applying the matrix, computing its inverse, and its determinant) in linear CPU time.

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  23. 29. The why and how of nonnegative matrix factorization
    Johannes Friedrich (CCB)
    11/1/19, 11:35 AM

    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...

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  24. 30. Introduction to Tensor Network Methods
    Katharine Hyatt (CCQ)
    11/1/19, 12:00 PM

    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.

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