Name: Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics
Start: 2019-10-30T08:15:00-04:00
End: 2019-11-01T17:00:00-04:00
Location: Ingrid Daubechies Auditorium

Wednesday, October 30
- 8:15 AM
  
  Breakfast
- Welcome
- Optimization Introductory Lecture 1
  - 1
    
    Optimization landscapes - a gentle multi-disciplinary introduction to optimization
    
    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 the stage for the latter focused talks, and will provide links to other topics covered in the FWAM conference.
    
    Speaker: Christian L. Mueller (CCM / LMU)
    
    FlatironConference2019.pdf
- Break
- Optimization Introductory Lecture 2
  - 2
    
    Tutorial: Optimization for Machine Learning
    
    A 1-hour version of the MLSS Buenos Aires tutorial notes, focusing on parts 1 and 4, available at the links below.
    
    Speaker: Elad Hazan (Princeton U)
    
    Berkeley lectures youtube, pt 1
    
    Berkeley lectures youtube, pt 2
    
    Boyd & Vandenberghe online book
    
    Elad Hazan's webpage
    
    Lecture notes on optimization for machine learning
    
    MLSS Tutorial notes (parts 1 and 4 were covered at Flatiron)
- Break
- Optimization Short Talk
  - 3
    
    A practical introduction to adjoint methods
    
    Speaker: Leslie Greengard (CCM / NYU)
    
    adjoint_methods.pdf
  - 4
    
    Research Applications of Optimization: Data-Driven Spectroscopy
    
    Speaker: Megan Bedell (CCA)
    
    FWAM2019-Bedell.pptx
- 12:30 PM
  
  Lunch-
- Function Approximation and Differential Equations Introductory Lecture
  - 5
    
    Introduction to interpolation, integration and spectral methods
    
    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 higher-dimensional methods and codes.
    
    Lecture notes (see Lecture I) and codes for demo figures at:
    https://github.com/ahbarnett/fwam-numpde
    
    Speaker: Alex Barnett (CCM)
    
    numpde_handout.pdf
- Break
- Function Approximation and Differential Equations Introductory Lecture
  - 6
    
    Overview of various methods to solve differential equations
    
    We overview various numerical methods to solve ODEs and PDEs.
    
    For source of notes see: https://github.com/ahbarnett/fwam-numpde
    
    Speaker: Keaton Burns (MIT / CCA)
    
    See Lecture II in our notes
- Break
- Function Approximation and Differential Equations Equations Short Talk
  - 7
    
    PDEs: The long and the short.
    
    Speaker: Michael Shelley (CCB)
  - 8
    
    Introduction to Integral Equation Methods
    
    Speaker: Jun Wang (CCM)
    
    bie_fwam.pdf
  - 9
    
    Wavelets
    
    Speaker: Joakim Andén (CCM)
- 5:15 PM
  
  Reception
Thursday, October 31
- 8:15 AM
  
  Breakfast
- Sampling Introductory Lecture
  - 10
    
    Introduction to Markov chain Monte Carlo
    
    Speaker: Dan Foreman-Mackey (CCA)
- Break
- Sampling Introductory Lecture
  - 11
    
    Scalable Bayesian Inference.
    
    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. Second, I describe some popular advancements such as stochastic variational inference, and variational autoencoders. During the talk, I will establish some connections with mathematical problems in different centers at Flatiron.
    
    Speaker: Mariano Gabitto (CCB)
- Break
- Sampling Short Talk
  - 12
    
    The quantum-ness in quantum Monte Carlo: mathematical and algorithmic implications
    
    Speaker: Prof. Shiwei Zhang (CCQ)
- Sampling Short Talk
  - 13
    
    Hierarchical Modeling and Stellar Velocities
    
    An introduction to Bayesian hierarchical modeling, with an example from my own research modeling repeated velocity measurements of distant stars in the Milky Way.
    
    Speaker: Emily Cunningham (CCA)
- 12:10 PM
  
  Lunch
- Deep Learning Introductory Lecture
  - 14
    
    Introduction to Deep Learning
    
    A Deep Learning 101 to get familiar with machine/deep learning principles, neural networks, back-propagation, convolution nets and representation learning.
    
    Speaker: Gabriella Contardo (CCA)
    
    DeepLearning_101_FWAM.pdf
- Break
- Deep Learning Introductory Lecture
  - 15
    
    Introduction to Deep Learning
    
    We will introduce state of the art deep learning methods and showcase some of its applications to astrophysical challenges.
    
    Speaker: Shirley Ho (CCA)
- Break
- Deep Learning Short Talks
  - 16
    
    Uncertainty Estimation with Neural Networks
    
    Speaker: Laurence Levasseur (CCA / U. de Montreal)
    
    Perreault-Levasseur_FWAM.pdf
  - 17
    
    Deep Generative Modeling for Statistical and Quantum Physics
    
    Speaker: Giuseppe Carleo (CCQ)
  - 18
    
    Biological neural network algorithms
    
    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 neuroscience-based machine learning by deriving algorithms and neural networks from objective functions based on the principle of similarity preservation. Similarity-based neural networks rely exclusively on biologically plausible local learning rules and solve important unsupervised learning tasks such as dimensionality reduction, clustering and manifold learning. In addition, to modeling biological networks, similarity-based algorithms are competitive for Big Data applications. For further information please see http://www.offconvex.org/2018/12/03/MityaNN2/
    
    Speaker: Mitya Chklovskii (CCB)
- 5:15 PM
  
  Reception
Friday, November 1
- 8:30 AM
  
  Breakfast
- Dimension Reduction and Factorization Short Talk
  - 19
    
    Randomized linear algebra and matrix approximation
    
    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.
    
    Speaker: Eftychios Pnevmatikakis (CCM)
    
    0909.4061.pdf
    
    1505.04650.pdf
    
    main.pdf
    
    woodruff.pdf
  - 20
    
    Spectral Clustering and Dimensionality Reduction
    
    Speaker: Marina Spivak (CCM)
  - 21
    
    Clustering in low dimensions
    
    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
    
    Speaker: Jeremy Magland (CCM)
    
    FWAM-clustering-2019 (1).pdf
- Break
- Dimension Reduction and Factorization Introductory Lecture
  - 22
    
    Fast algorithms for hierarchically structured matrices
    
    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.
    
    Speaker: Manas Rachh (CCM)
    
    pres.pdf
- Dimension Reduction and Factorization Short Talk
  - 23
    
    The why and how of nonnegative matrix factorization
    
    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 (CCB)
    
    NMF.pdf
  - 24
    
    Introduction to Tensor Network Methods
    
    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 (CCQ)
- 12:25 PM
  
  No Lunch will be served: please order from Seamless

Flatiron Internal Conference: Flatiron-wide Algorithms and Mathematics

2nd Floor

Ingrid Daubechies Auditorium