Scholars@Duke

• Background
• 

  Education, Training, & Certifications

  • Ph.D., Brown University 2019
• Expertise
• 

  Subject Headings

  • Applied Topology
• Publications & Artistic Works
• 

  Selected Publications

  • Academic Articles

    • Oudot, Steve, and Elchanan Solomon. “Barcode embeddings for metric graphs.” Algebraic & Geometric Topology 21, no. 3 (August 11, 2021): 1209–66. https://doi.org/10.2140/agt.2021.21.1209.
       Full Text
    • Solomon, Elchanan, Alexander Wagner, and Paul Bendich. “A Fast and Robust Method for Global Topological Functional Optimization.” 24th International Conference on Artificial Intelligence and Statistics (Aistats) 130 (2021): 109-+.
       Link to Item
    • Carter, P., and Y. E. Solomon. “Relaxing the integral test: A challenge for the advanced calculus student.” College Mathematics Journal 48, no. 4 (September 1, 2017): 290–91. https://doi.org/10.4169/college.math.j.48.4.290.
       Full Text
    • Maria, Clément, Steve Oudot, and Elchanan Solomon. “Intrinsic Topological Transforms via the Distance Kernel Embedding,” n.d.
       Link to Item
    • Oudot, Steve, and Elchanan Solomon. “Inverse Problems in Topological Persistence,” n.d.
       Link to Item
    • Solomon, Elchanan, Alexander Wagner, and Paul Bendich. “From Geometry to Topology: Inverse Theorems for Distributed Persistence,” n.d.
       Link to Item
    • Solomon, Elchanan. “Stability of Extended Functional Persistence in Dimensions Zero and One,” n.d.
       Link to Item
    • Wagner, Alexander, Elchanan Solomon, and Paul Bendich. “Improving Metric Dimensionality Reduction with Distributed Topology,” n.d.
       Link to Item
• Teaching & Mentoring
• 

  Recent Courses

  • K_MATH 409: Topology 2021
  • K_STATS 302: Principles of Machine Learning 2021
  • MATH 216: Linear Algebra and Differential Equations 2021
  • MATH 466: Mathematics of Machine Learning 2021
  • MATH 716: Linear Algebra and Differential Equations 2021
  • MATH 766: Mathematics of Machine Learning 2021
  • K_MATH 202: Linear Algebra 2020
  • K_STATS 302: Principles of Machine Learning 2020
  • MATH 216: Linear Algebra and Differential Equations 2020
  • MATH 716: Linear Algebra and Differential Equations 2020
  • MATH 466: Mathematics of Machine Learning 2019
  • MATH 766: Mathematics of Machine Learning 2019

Some information on this profile has been compiled automatically from Duke databases and external sources. (Our About page explains how this works.) If you see a problem with the information, please write to Scholars@Duke and let us know. We will reply promptly.