I am a Ph.D. candidate in the Applied Mathematics department at the University of Washington. My research involves sparsity-promoting regularization methods in neural networks and data-driven discovery of dynamical systems. I have also worked on developing component-based reduced order models for parameter-dependent elliptic linear partial differential equations. I am advised by Nathan Kutz.
Ph.D. in Applied Mathematics (advanced data science option), in progress
University of Washington
M.S. in Applied Mathematics, 2015
University of Washington
B.S. in Applied Mathematics (specialization in computing), 2013
University of California, Los Angeles
Quarter | Course |
---|---|
Autumn 2018 | AMATH 351: Introduction to Differential Equations and Applications |
Summer 2017 | AMATH 351: Introduction to Differential Equations and Applications |
Winter 2017 | AMATH 352: Applied Linear Algebra and Numerical Analysis |
Summer 2016 | AMATH 352: Applied Linear Algebra and Numerical Analysis |
Quarter | Course |
---|---|
Spring 2017 | AMATH 586: Graduate Numerical Anaylsis of Time Dependent Problems |
Fall 2016 | AMATH 501: Graduate Vector Calculus and Complex Variables |
Spring 2016 | MATH 126: Calculus III |
Winter 2016 | MATH 125: Calculus II |
Fall 2015 | AMATH 301: Beginning Scientific Computing |
Spring 2015 | AMATH 301: Beginning Scientific Computing |
Winter 2015 | MATH 124: Calculus I |
Fall 2014 | MATH 124: Calculus I |