I am a Research Scientist in the Applied Mathematics department at the University of Washington working with Nathan Kutz and Steve Brunton. My research involves data-driven discovery of dynamical systems, sparsity-promoting regularization methods in neural networks, and physics-informed anomaly detection. I have also worked on developing component-based reduced order models for parameter-dependent elliptic linear partial differential equations.

- Machine Learning
- Numerical Analysis
- Scientific Computing
- Reduced Order Modeling

Ph.D. in Applied Mathematics (advanced data science option), 2020

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

**Detecting scam pages**: I deployed three image-retrieval based models and trained a multi-channel page embedding for scam page detection.

Tools used:

- K-nearest neighbors
- Proprietary retrieval methods
- Nonlinear embeddings
- Convolutional and feedforward neural networks
- SQL

**Studying approaches for utilization of cross-domain data**: I investigated different methods of incorporating cross-domain features into in-domain models.

Tools used:

- Sparse nueral networks
- Two-tower sparse neural networks
- SQL

We develop data-driven algorithms to fully automate sensor fault detection in systems governed by underlying physics. The proposed …

Machine learning (ML) and artificial intelligence (AI) algorithms are now being used to automate the discovery of physics principles …

PySINDy is an open source Python package Kathleen Champion and I created for the Sparse Identification of Nonlinear Dynamical systems (SINDy).
We designed the package to make the process of learning governing equations from data as painless as possible for practitioners and to provide a standard implementation of the SINDy method for researchers to build upon.

- Machine Learning (and Machine Learning for Big Data)
- Numerical Linear Algebra
- Numerical Solution of Differential Equations
- Approximation Theory & Spectral Methods
- Data visualization
- Numerical Optimization
- Dynamical Systems
- Data Analysis
- Statistics
- Functional Analysis
- Partial Differential Equations
- Nonlinear Partial Differential Equations
- Finite Volume Methods

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 |