Autumn School: Optimization in Machine Learning and Data Science

August 28 - 31, 2017

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General Information

Large parts of the new economy rely on data science and the application of machine learning. For handling these modern computational and statistical problems, optimization algorithms are of utmost importance. The typically large data sets involved in this context urge for new optimization techniques.

Therefore, we are delighted to announce Julian Hall,  Tamara G. Kolda, Stephen Wright as speakers at the upcoming ALOP Autumn School entitled “Optimization in Machine Learning and Data Science.”

Representing three top class authorities in their respective fields of research, reaching from optimization of signal and image processing over large scale linear programming problems to tensor decomposition and many more, they will be sharing their experience and knowledge on theory and application of optimization algorithms.

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Information on scheduled Speakers:

His research interests are the development of algorithmic and computational techniques for solving large scale linear programming (LP) problems using the revised simplex method on both serial and parallel computers. Consequential research interest is the application of these techniques in other areas of computation optimization and linear algebra.

Mr. Hall plans on talking about high performance simplex which will naturally lead into high performance numerical computation. He will look at computational issues in interior point methods and the applicability of “modern” first order methods to the solution of LP problems, as well as talk about one of his current research topics.

Her research interests are computational algorithm design and development, including linear and multi-linear algebra, tensor decomposition, tensor eigenvalues, graph algorithms, machine learning, network science, derivative-free optimization, computational optimization, distributed and parallel computing.

Ms. Kolda will talk about “Optimization Approaches for Fitting the Canonical Tensor Decomposition.” More specifically, she will speak about

   - the theory and background of the CP decomposition
   - standard approaches such as alternating least squares and all-at-once optimization
   - handling missing data
   - practical approaches to choosing the rank
   - alternative objective functions for other statistical models/assumptions
   - Time permitting, she may also talk about HPC, data structures, parallelization

His research interests are optimization, especially problems involving real (as opposed to integer or discrete) variables. Among other things, he is a well-known expert in the theory and practice of numerical aspects in data science.

 

Invited Speakers: 
  • Julian Hall, The University of Edinburgh
  • Tamara G. Kolda, Sandia National Laboratories
  • Stephen J. Wright, University of Wisconsin
Organizers: 
  • Jan Pablo Burgard

  • Ralf Münnich

  • Ekkehard Sachs

  • Volker Schulz

  • Sven de Vries

Important Dates 
Stipend application before June 15, 2017