Julie Eaton, Ph.D.

Degrees
Ph.D.
Mathematics
University of Washington Seattle
2010
M.S.
Statistics
University of Washington Seattle
2009
B.A.
Mathematics
University of Washington Seattle
2002
Current Research

My expertise is in variational analysis, optimization, statistics and data visualization.

  • Variational analysis and optimization - I am interested in the variational properties of polynomial root functions and spectral functions, which arise in eigenvalue optimization problems. Examples of spectral functions are the spectral abscissa (largest real part of an eigenvalue) and spectral radius (largest eigenvalue in modulus), which are tied to the asymptotic behavior of dynamical systems.
    • (with J. Burke) Variational properties of spectral max functions - In preparation
    • (with J. Burke) On the subdifferential regularity of max root functions for polynomials, Journal of Nonlinear Analysis: Theory, Methods & Applications, 75(3):1168-1187, 2012.
       
  • Statistics, data visualization applied to public health surveillance - My work in statistics and data visualization revolves around my involvement in a public health project for influenza surveillance. This work consists of three main components: data visualization, identifying patterns of error in data and developing an automated routine for error detection, and generating prediction intervals for the proxy flu rate.
    • The accrued package in R for visualizing data quality of partially accruing data can be found here: http://cran.r-project.org/
    • (with I. Painter, D. Olson, D. Revere, B. Lober) Generation of Prediction Intervals to Assess Data Quality in the Distribute System Using Quantile Regression In 2011 JSM Proceedings, Statistics in Defense and National Security Section. Alexandria, VA: American Statistical Association, pp 5172-5179.
Scholarly Interests

Interests include variational analysis, polynomial optimization, and data visualization.

Teaching

Courses I have taught or am currently teaching at UW Tacoma:

  • TMATH 110: Introduction to Statistics and its Applications
  • TMATH 120: Precalculus
  • TMATH 124: Calculus I
  • TMATH 125: Calculus II
  • TMATH 126: Calculus III
  • TMATH 307: Differential Equations
  • TMATH 310: Statistics with applications for environmental science
  • TCSS 321: Discrete Structures I
Affiliations
  • Mathematical Association of America
Academic Service
  • Faculty Searches
    • 2013: Cluster Hire in Hydrology Search Committee Member
    • 2013: Cluster Hire in Hydrology Interview Committee Member
    • 2013: 3-year Mathematics Lecturer Interview Committee Member
    • 2013: 3-year Mathematics Lecturer Search Committee Member
    • 2012: 3-year Chemistry Lecturer Interview Committee Participant
       
  • Campus-Wide Quantitative Literacy Task Force (2012-present)
    • We have developed a comprehensive plan for addressing the quantitative literacy including student support, faculty support, and identifying sources of funding. Specific personal contributions include a thorough survey of the quantitative gaps reported by instructors of courses that have Introductory Statistics with Applications (TMATH 110), Precalculus (TMATH 120), and Calculus I (TMATH 124) as prerequisites; developing a diagnostic quiz for Calculus I, proposing and coordinating the quantitative boot-camps with the TLC and instructors in chemistry, physics, business, and computer science.
Honors and Awards
  • AWM-NSF Mentoring Travel Grant
CV or Resume
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