Data Scientist and Algorithm Engineer

Data Science & Research Salt Lake City, Utah, United States

Profile
Contacts

Summary

I will graduate in May 2022 with a Ph.D. in Mathematics. I specialize in applied mathematics and numerical analysis, where I design algorithms for solving high-dimensional stochastic differential equations more accurately and faster than current methods. Additionally, I utilize my mathematical expertise to solve a variety of real-world problems. I have worked on the development of a probabilistic model which minimizes the risk of bacterial contamination during blood transfusions, the simulation of storm surges to predict the risk of flooding for warning systems, and modeling Arctic melt pond evolution to increase the accuracy of global climate models.

In 2013, I began my first software job at a local software company. Now, during my Ph.D., I work in Python to develop algorithms and mathematical models daily. I was selected to work on two data driven projects with the U.S. Army Corps of Engineers. For the first project, I modeled the ocean tide using harmonic and time series analysis of data. For the second project, I worked on a team to predict ocean wave breaker types (plunging, spilling, surging waves) using machine learning. I am excited about opportunities to continue solving problems using tools from computer science, mathematics, and engineering.

In October 2019, I participated in an Arctic expedition where I spent six weeks in the Central Arctic on a large, icebreaker-like research vessel studying the Arctic region on a multidisciplinary team. I led the deployment of 4 seasonal ice mass balance buoys while learning from leading scientists from around the world. This experience, combined with teaching mathematics courses for the University of Utah, has prepared me to communicate even the most difficult concepts to others effectively.

Expectations

I am looking to use my mathematics and computer science skills to continue solving challenging problems. I enjoy writing algorithms and data-driven models to simulate real-world phenomena to gain new insights and understand how critical processes work.