Cassidy Krause

Department of Mathematics
University of Kansas
1460 Jayhawk Blvd
Lawrence, Kansas 66045-7567

Contact information:

Office: 562 Snow Hall
Office Phone: (785)-864-4306
Email: ckrause@ku.edu
URL: http://www.people.ku.edu/~c444k116/

About me:

I am in my fourth year as a Self Graduate Fellow at KU. The Self Graduate Fellowship is a four year fellowship that provides professional development training in the areas of communication, leadership, and management. The skills learned through this fellowship are valuable assets to those interested in pursuing a career in government or industry after completing a PhD. Find out more about the SGF and its unique opportunities here.

My research interests include numerical analysis and dynamical systems, and I am particularly interested in data assimilation and its applications to weather and climate modeling, and I am a member of the Math and Climate Research Network (MCRN). My advisor is Erik Van Vleck. I received my B.S. in Mathematics from the University of Wisconsin-Platteville in 2015, and my M.A. in Mathematics from KU in 2017.

Service and outreach are important to me; I am the graduate student organizer of the Math Awareness Month activities in April, where local fifth graders spend the day with graduate students doing fun math activities. I am also the past president of the KU chapter of the Association for Women in Mathematics (2016-2018), and past president for the Math Graduate Student Organization (2017-2018).

Research Interests:

I research data assimilation (DA), which is the process of combining noisy data with physical models to make predictions about the state of a dynamical system.

Currently, I am interested in combining data assimilation with adaptive moving mesh techniques. Adaptive meshes are highly useful for solving PDEs, and many problems which use adaptive meshes could also benefit from ensemble DA approaches, such as the Ensemble Kalman Filter (EnKF). DA methods like the EnKF use an ensemble of solutions to predict the truth. A key part of EnKF algorithm requires the computation of the mean and covariance of the ensemble members. However, if each ensemble member is allowed to have its own independent adaptive mesh, these calculations become more difficult. Two of my current projects focus on how to do this effectively, through the use of an adaptive common mesh.

Come see my poster at Snowbird (SIAM Dynamical Systems 2019)! I'm presenting at the poster session on Tuesday evening.

Presentations:

SIAM Dynamical Systems (Snowbird) 2019 Poster Session: Data Assimilation with Adaptive Moving Mesh Techniques

Teaching Experience:

Though I am fortunate to have most of my graduate education funded by the Self Graduate Fellowship, I had the exciting opportunity to teach during the 2018-2019 academic year.

Fall 2018 - Math 115 (Business Calculus)
Spring 2019 - Math 115 Enhanced