Joint Mathematics Meetings AMS Special Session

Current as of Saturday, January 14, 2023 03:30:04

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• Deadlines
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• Inquiries: meet@ams.org

2023 Joint Mathematics Meetings (JMM 2023)

• John B. Hynes Veterans Memorial Convention Center, Boston Marriott Hotel, and Boston Sheraton Hotel, Boston, MA
• January 4-7, 2023 (Wednesday - Saturday)
• Meeting #1183

Associate Secretary for the AMS Scientific Program:

Steven H. Weintraub, Lehigh University shw2@lehigh.edu

AMS Special Session on Mathematical Methods in Machine Learning and Optimization I

• Wednesday January 4, 2023, 9:00 a.m.-12:00 p.m.
  AMS Special Session on Mathematical Methods in Machine Learning and Optimization I
  Room 209, Hynes Convention Center
  Organizers:
  Carlos M. Ortiz-Marrero, Pacific Northwest National Laboratory carlos.ortiz@pnnl.gov
  Ryan W. Murray, North Carolina State University
  

  • 9:00 a.m.
    An Introduction to the Regularity Theory of Optimal Transport
    Gabriel Khan*, Iowa State University
    (1183-35-18290)
  • 9:30 a.m.
    Analytical and geometric insights into adversarial robustness
    Nicolas Garcia Trillos*, University of Wisconsin Madison
    (1183-49-22468)
  • 10:00 a.m.
    Variational approach to multi-dimensional scaling
    Ryan W. Murray, North Carolina State University
    Adam Pickarski*, North Carolina State University
    (1183-49-22011)
  • 10:30 a.m.
    Bures-Wasserstein Barycenters and Low-Rank Matrix Recovery
    Thibaut Le Gouic, Ecole Centrale de Marseille
    Tyler Maunu*, Brandeis University
    Philippe Rigollet, Massachusetts Institute of Technology
    (1183-90-21483)
  • 11:00 a.m.
    Optimal Investment: Robo-advising Under Small Changes of Risk Aversion
    Maxim Bichuch, University at Buffalo
    Nadejda Drenska*, Johns Hopkins University, AMS
    (1183-49-21824)
  • 11:30 a.m.
    Improving Trip Distribution Modeling with Sparse Regression
    Jesús Muñuzuri, Universidad de Sevilla
    Javier Rubio-Herrero*, University of North Texas
    (1183-90-18599)
• Wednesday January 4, 2023, 2:00 p.m.-5:00 p.m.
  AMS Special Session on Mathematical Methods in Machine Learning and Optimization II
  Room 209, Hynes Convention Center
  Organizers:
  Carlos M. Ortiz-Marrero, Pacific Northwest National Laboratory carlos.ortiz@pnnl.gov
  Ryan W. Murray, North Carolina State University
  

  • 2:00 p.m.
    Kernel SVM and Neural Networks
    Tony Chiang*, Pacific Northwest National Lab
    (1183-15-19561)
  • 2:30 p.m.
    Topological properties of ReLU network functions, at initialization and during training
    Marissa Masden*, University of Oregon
    (1183-68-19366)
  • 3:00 p.m.
    The SVD of Convolutional Weights: A CNN Interpretability Framework
    Davis Richard Brown*, Pacific Northwest National Laboratory
    Carlos M. Ortiz-Marrero, Pacific Northwest National Laboratory
    Brenda Praggastis, Pacific Northwest National Laboratory
    Emilie Purvine, Pacific Northwest National Laboratory
    Madelyn Shapiro, Pacific Northwest National Laboratory
    Bei Wang, University of Utah
    (1183-68-21949)
  • 3:30 p.m.
    Optimizing quantum circuits with Riemannian gradient flow
    Nathan Killoran, Xanadu Quantum Technologies
    Roeland Cornelis Wiersema*, Vector Institute
    (1183-53-19641)
  • 4:00 p.m.
    Machine Learning on Large-Scale Graphs
    Luana Ruiz*, Simons-Berkeley Institute
    (1183-68-20480)
  • 4:30 p.m.
    Communities in Data
    Kenneth S. Berenhaut*, Wake Forest University
    (1183-62-20113)
• Thursday January 5, 2023, 9:00 a.m.-12:00 p.m.
  AMS Special Session on Mathematical Methods in Machine Learning and Optimization III
  Room 209, Hynes Convention Center
  Organizers:
  Carlos M. Ortiz-Marrero, Pacific Northwest National Laboratory carlos.ortiz@pnnl.gov
  Ryan W. Murray, North Carolina State University
  

  • 9:00 a.m.
    Optimal Learning
    Andrea Bonito*, Texas A&M University
    (1183-65-19300)
  • 9:30 a.m.
    Ensuring Exploration and Exploitation in Graph-Based Active Learning
    Kevin Miller*, Oden Institute of Computational Engineering and Sciences
    (1183-62-18714)
  • 10:00 a.m.
    Numerical methods for Bayesian inference for inverse transport problems
    Jae-Youn Kim, Department of Mathematics, University of Houston
    Andreas Mang*, University of Houston
    (1183-49-21282)
  • 10:30 a.m.
    Hamilton-Jacobi equations for statistical depths
    Martin Molina-Fructuoso*, Brandeis University
    Ryan W. Murray, North Carolina State University
    (1183-49-21754)
  • 11:00 a.m.
    Data-Driven Random Tessellation Forests
    Eliza O'Reilly*, Caltech
    (1183-60-21828)
  • 11:30 a.m.
    Distributed Stochastic Gradient Descent: Nonconvexity, Nonsmoothness, and Avoiding Saddle Points
    Soummya Kar, Carnegie Mellon University
    Ryan W. Murray, North Carolina State University
    H. Vincent Poor, Princeton University
    Brian Swenson*, Pennsylvania State University
    (1183-68-21945)