Organizing Committee
Abstract

Modern data analysis presents a variety of challenges, including the size, the dimensionality, the complexity, and the multiple-modality of the data. In an attempt to keep pace with these growing challenges, data scientists combine tools inspired from mathematics, from computer science, and from statistics. This TRIPODS Summer Bootcamp will provide attendees a hands-on introduction to emerging techniques for using topology with machine learning for the purpose of data analysis.

Topological and machine learning techniques potentially play complimentary roles for analyzing data. In topological data analysis, one leverages the fact that the shape of the data often reflects important and interpretable patterns within, although topological techniques alone typically cannot match the predictive power of machine learning. By contrast, machine learning algorithms provide state-of-the-art accuracies on predictive tasks, but the manner by which they arrive at a prediction is often difficult to interpret. Machine learning would benefit if one could use mathematics to provide more interpretability, even in exchange for reduced predictive power. There are by now a variety of ways to combine topology with machine learning, and the diversity of such approaches is growing. The goal of the TRIPODS Summer Bootcamp is to expose attendees to current tools combining topology and machine learning. The bootcamp will focus not only the successes of such algorithms but also on their inherent challenges, in order to inspire the development of novel approaches.

The bootcamp will consist of a hands-on tutorial during days 1-3, and a research conference during days 4-5.

Days 1-3: Introductory tutorial on applied topology and machine learning

The first three days of the bootcamp will include an introductory tutorial on applied topology, on machine learning, and on the marriage between the two. The featured topic from applied topology will be persistent homology, and the featured topic from machine learning will be classical algorithms such as clustering, support vector machines (SVM), and random forests. Finally, featured topics for combining persistent homology with machine learning will include the bottleneck or Wasserstein distances, persistence landscapes, and persistence images. The tutorial will emphasize hands-on coding exercises with real data. Participants will compare the performance and interpretability of standard algorithms on a variety of machine learning tasks, and they will also create and test variants of their own invention.

We will be doing computational exercises to accompany the bootcamp. Please see our tutorial at https://github.com/ICERM-TRIPODS-Top-ML/Top-ML/wiki and our code at https://github.com/ICERM-TRIPODS-Top-ML/Top-ML.

Days 4-5: Research conference on topology and machine learning

The final two days of the bootcamp will feature a research conference on current trends in topology and machine learning. The conference will be targeted at a more expert audience not necessarily present at the preparatory bootcamp tutorials during the first three days.

Confirmed Speakers & Participants

  • Henry Adams
    Colorado State University
  • Shaun Ault
    Valdosta State University
  • Elaine Ayo
    Optoro
  • Mikhail Belkin
    Ohio State University
  • Jeffrey Brock
    Brown University
  • Mathieu Carrière
    Inria Saclay
  • Samir Chowdhury
    The Ohio State University
  • David Damiano
    College of the Holy Cross
  • Matthew Drnevich
    University of Notre Dame
  • Tegan Emerson
    Naval Research Laboratory
  • Mahmoodreza Jahanseir
    University of Connecticut
  • Marija Jelic
    Faculty of Mathematics, University of Belgrade
  • Karim Karimov
    Colorado State University
  • karthik kashinath
    Lawrence Berkeley National Lab
  • Lara Kassab
    Colorado State University
  • Irene Kim
    UC Davis
  • Katherine Kinnaird
    Smith College
  • Iryna Kuz
    University of Florida
  • Michael Marlett
    College of the Holycross
  • Melissa McGuirl
    Brown University
  • Anthea Monod
    Columbia University
  • Alexander Neshitov
    University of Southern California
  • Kristine Pelatt
    St. Catherine University
  • Archit Rathore
    University of Utah
  • Carlos Ronchi
    USP - University of São Paulo
  • Bjorn Sandstede
    Brown University
  • Matthew Schoenbauer
    University of Notre Dame
  • Don Sheehy
    University of Connecticut
  • Joshua Shrader
    Johns Hopkins University Applied Physics Laboratory
  • Yitzchak Solomon
    Brown University
  • Courtney Thatcher
    University of Puget Sound
  • Sarah Tymochko
    Michigan State University
  • Niels uit de Bos
    Universität Duisburg-Essen
  • Bei Wang
    University of Utah
  • Lin Yan
    University of Utah
  • Lori Ziegelmeier
    Macalester College

Workshop Schedule

Monday, August 6, 2018
TimeEventLocationMaterials
8:30 - 8:55Registration - ICERM 121 South Main Street, Providence RI 0290310th Floor Collaborative Space 
8:55 - 9:00Welcome - ICERM Director10th Floor Collaborative Space 
9:00 - 9:50Tutorial- Persistent homology10th Floor Collaborative Space
10:00 - 10:30Coffee/ Tea Break10th Floor Collaborative Space 
10:30 - 11:20Exercises- Persistent homology10th Floor Collaborative Space 
11:30 - 12:20Using Persistent Homology to Bound the Fréchet Distance - Don Sheehy, University of Connecticut10th Floor Collaborative Space
12:30 - 2:30Break for Lunch/ Free Time  
2:30 - 3:20Tutorial- Machine learning10th Floor Collaborative Space
3:30 - 4:00Coffee/ Tea Break10th Floor Collaborative Space 
4:00 - 4:50Exercises- Machine learning10th Floor Collaborative Space 
5:00 - 6:30Welcome Reception11th Floor Collaborative Space 
Tuesday, August 7, 2018
TimeEventLocationMaterials
9:00 - 9:50Tutorial- Topological feature vectors10th Floor Collaborative Space
10:00 - 10:30Coffee/ Tea Break10th Floor Collaborative Space 
10:30 - 11:20Exercises- Topological feature vectors10th Floor Collaborative Space 
11:30 - 12:20Persistence Images- Machine Learning on the Shape of Data - Tegan Emerson, Naval Research Laboratory10th Floor Collaborative Space
12:30 - 2:30Break for Lunch/ Free Time  
2:30 - 3:20Tutorial- Topological feature vectors10th Floor Collaborative Space
3:30 - 4:00Coffee/ Tea Break10th Floor Collaborative Space 
4:00 - 4:50Exercises- Topological feature vectors10th Floor Collaborative Space 
Wednesday, August 8, 2018
TimeEventLocationMaterials
9:00 - 9:50Vignettes- Machine learning and topology10th Floor Collaborative Space
10:00 - 10:30Coffee/ Tea Break10th Floor Collaborative Space 
10:30 - 11:20Exercises- Machine learning and topology10th Floor Collaborative Space
11:30 - 12:20Hierarchical clustering and persistent homology methods for asymmetric networks - Samir Chowdhury, The Ohio State University10th Floor Collaborative Space
12:30 - 12:40Group Photo10th Floor  
12:40 - 2:30Break for Lunch/ Free Time  
2:30 - 3:20Vignettes- Machine learning and topology10th Floor Collaborative Space
3:30 - 4:00Coffee/ Tea Break10th Floor Collaborative Space 
4:00 - 4:50Exercises- Machine learning and topology10th Floor Collaborative Space
Thursday, August 9, 2018
TimeEventLocationMaterials
9:00 - 9:50TBA - Anthea Monod, Columbia University10th Floor Collaborative Space
10:00 - 10:30Coffee/ Tea Break10th Floor Collaborative Space 
10:30 - 11:20Topological Perspectives On Stratification Learning - Bei Wang, University of Utah10th Floor Collaborative Space
11:30 - 12:20An interpolation perspective on modern machine learning - Mikhail Belkin, The Ohio State University10th Floor Collaborative Space
12:30 - 2:30Break for Lunch/ Free Time  
2:30 - 3:20Persistent Homology and Ballistic Deposition - Dave Damiano, College of the Holy Cross10th Floor Collaborative Space
3:45 - 5:00Poster Session and Coffee/ Tea Break11th Floor Collaborative Space 
Friday, August 10, 2018
TimeEventLocationMaterials
9:00 - 9:50A gaussian type kernel for persistence diagrams - Mathieu Carriere, INRIA10th Floor Collaborative Space
10:00 - 10:30Coffee/ Tea Break10th Floor Collaborative Space 
10:30 - 11:20Persistence Images and CROCKER Plots as Topological Feature Vectors - Lori Ziegelmeier, Macalester College10th Floor Collaborative Space
11:30 - 12:20TDA-Inspired Song Comparison - Katherine Kinnaird, Smith College10th Floor Collaborative Space 
12:30 - 2:30Break for Lunch/ Free Time  
2:30 - 3:20Discussion10th Floor Collaborative Space 
3:30 - 4:00Coffee/ Tea Break10th Floor Collaborative Space 

Request Reimbursement

Acceptable Costs
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https://icerm.brown.edu/money/

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Reimbursement Tips
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Reimbursement Timing

6 - 8 weeks after all documentation is sent to ICERM. All reimbursement requests are reviewed by numerous central offices at Brown who may request additional documentation.

Reimbursement Deadline

Submissions must be received within 30 days of ICERM departure to avoid applicable taxes. Submissions after thirty days will incur applicable taxes. No submissions are accepted more than six months after the program end.