AMS Sectional Meeting AMS Special Session

Current as of Saturday, April 11, 2020 03:30:04

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• Program
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• Deadlines
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• Timetable
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• Room Chart
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• Chronological Index of Talks
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• Registration/Housing/Etc.


• Inquiries: meet@ams.org

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Spring Central Sectional Meeting

• Purdue University, West Lafayette, IN
• April 4-5, 2020 (Saturday - Sunday)
• Meeting #1157

Associate secretaries:

Georgia Benkart, AMS benkart@math.wisc.edu

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Special Session on Contemporary Applications of Gradient Flows and Variational Methods

• Saturday April 4, 2020, 8:30 a.m.-10:50 a.m.
  Special Session on Contemporary Applications of Gradient Flows and Variational Methods, I
  Room 219, University Hall
  Organizers:
  Tao Luo, Purdue University
  Nung Kwan (Aaron) Yip, Purdue University yipn@purdue.edu
  

  • 8:30 a.m.
    Gradient flow formulation and numerical simulation for motion by mean curvature and contact line dynamics on rough surface.
    Yuan Gao*, Duke University
    (1157-65-182)
  • 9:00 a.m.
    Optimal convergence rates for the Cahn-Hilliard equation on the real line.
    Sarah Biesenbach*, RWTH Aachen University
    (1157-35-238)
  • 9:30 a.m.
    Accelerating Langevin sampling with birth-death processes.
    Yulong Lu*, Duke University
    (1157-35-355)
  • 10:00 a.m.
    From clustering with graph cuts to isoperimetric inequalities: quantitative convergence rates of Cheeger cuts on data clouds.
    Nicolas Garcia Trillos*, University of Wisconsin Madison
    Ryan Murray, North Carolina State University
    Matthew Thorpe, University of Cambridge
    (1157-49-151)
  • 10:30 a.m.
    Variational Inference Using Coupled Neural Differential Equations.
    Ruixin Wang, School of Industrial Engineering, Purdue University
    Prateek Jaiswal, School of Industrial Engineering, Purdue University
    Harsha Honnappa*, School of Industrial Engineering, Purdue University
    (1157-90-517)
• Saturday April 4, 2020, 2:00 p.m.-4:20 p.m.
  Special Session on Contemporary Applications of Gradient Flows and Variational Methods, II
  Room 219, University Hall
  Organizers:
  Tao Luo, Purdue University
  Nung Kwan (Aaron) Yip, Purdue University yipn@purdue.edu
  

  • 2:00 p.m.
    Kalman-Wasserstein gradient flows for solving inverse problems.
    Alfredo Garbuno-Inigo, California Institute of Technology
    Franca Hoffmann*, California Institute of Technology
    Wuchen Li, University of California, Los Angeles
    Andrew M. Stuart, California Institute of Technology
    (1157-60-132)
  • 2:30 p.m.
    Stein variational descent as a gradient flow: towards a rigorous theory for kernel selection.
    Ryan Murray*, North Carolina State University
    (1157-49-203)
  • 3:00 p.m.
    A Fourier Analysis Perspective of Training Dynamics of Deep Neural Networks.
    Yaoyu Zhang*, Institute for Advanced Study, Princeton
    Zhiqin John Xu, Institute of Natural Sciences, SJTU
    Tao Luo, Purdue University
    Zheng Ma, Purdue University
    (1157-00-477)
  • 3:30 p.m.
    Deep Network Approximation.
    Haizhao Yang*, Purdue University
    Qiang Du, Columbia University
    Hadrien Montanelli, Columbia University
    Zuowei Shen, National University of Singapore
    Shijun Zhang, National University of Singapore
    (1157-41-29)
  • 4:00 p.m.
    Adaptive Sequential SAA as a Universal Paradigm for Stochastic Optimization.
    Raghu Pasupathy*, Purdue University, Statistics
    Aaron Yip, Purdue University, Math
    David Newton, Purdue University, Statistics
    (1157-90-630)

Inquiries: meet@ams.org

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