I'm in my final year of undergrad at the University of Utah, and I'm currently interested in learning more about Ph.D programs in HCI/Computer Science.
My past vis work has involved visualizing educational data with the Sorenson Impact Center, biological data with Genentech, and environmental health data with Miriah Meyer and Pascal Goffin in the Visualization Design Lab.
Throughout my experiences, I've enjoyed conducting, drafting prototypes, and working with D3. I thrive on dense technical projects, but I also love the human side of vis.
In the future, I hope to complete a Ph.D in Computer Science and conduct applied visualization research. If you have openings in your lab, I'd love to connect with you.
B.Eng in Bioengineering, Computational Emphasis. Minor in Computer Science
May 2019 • 3.95 GPA
Machine Learning: Deep Learning, Visualization for Data Science, Data Structures and Algorithms, Discrete Mathematics, Engineering Computing, Linear Algebra and Differential Equations, Object Oriented Programming, and Computational Methods.
Undergraduate Researcher • March 2018 - Present
Performed user interviews with members of the AirU project to conceptualize and build web-based tools for exploratory data analysis and pollution detection.
Engineering Intern • May 2018 - August 2018
My internship focused on three different projects: purification, oncology, and site-services development.
Planned and executed wetlab experimentation to generate a purification dataset of 17 contaminants, leveraged this dataset to reduce future sample testing. A visualization tool was built using D3.js to explore and communicate the purification data.
Cleaned oncology meta-data for bioinformaticists using R .
Coordinated with site-services to implement a route finding application for their bus pickup locations. Produced a prototype application using leaflet and R shiny which resulted in an investment to construct a full-scale application.
Technical Fellow • September 2017 - May 2018
Worked with a team of 7 data scientists to provide insights on pay for success projects between state governments and private companies. Primarily used R for data visualization, modeling, cleaning, and analysis. Automated data acquisition tasks saving over 100 billable hours for the Venture Analysis team using Python.
Poor air quality impacts public health due to increased incidence of cancers, heart diseases, and various respiratory disorders. Many Utah cities have hazardous air quality episodes as a result of inversions and forest fires; however, despite its impact on our community, the data obtained about PM 2.5 levels is often course- often being measured for an entire zip code. Such granularity is unacceptable as research suggests that microclimates of pollution exist that are not captured by these measurements.
As such, a model was built to provide finer resolution PM 2.5 estimates throughout SLC. However, there didn't exist a way to understand how the model responds to episodes of poor air quality. As such the Air U Debugger (AUDB) was constructed.
I conducted the user interviews to determine the software requirements for AUDB; furthermore, I developed this AUDB through prototyping sessions. The current version of this tool pulls air quality sensor data, model data, and geographic data to display to the user.
This tool was selected to be demoed at the 2018 Utah Data Science Day.Tool Design, Signal Processing, Visualization
Our algorithm is used to find the cheapest flight path from a depature city to a destination city. It accomplishes this through reading csv data into a Network Graph Class, and creates an API for fast querying of the data.
Dijikstra's Algorithm was used to quickly traverse all of the created nodes. This algorithm uses a priority queue to return the cheapest path possible while attempting to find the destination node.
The flight data is stored in a few levels. First, a network graph class has a hashmap that contains each airport in the data set. Each airport contains another hashmap for all of the flight destinations leaving from that airport.
Using these nested hash maps, we were able to acheive O([V+E]*log(V)) run performance, where V is the number of airports and E is the number of flights. We decided to prioritze time over space in our design decisions. This decision was necessary as we wanted this API to be used on a large scale- we wanted a larger upfront memory cost in exchange for faster querying.Data Structures, Algorithms
This web app allows users to determine what Genentech bus picks up closest to their entered address. I created this app after I got tired of putting addresses into google maps and seeing the walking time between potential apartments and the bus stop.
This application quickly spread among the intern community, and within my first week at Genentech, I had a meeting with the director of the Bus Fleet. I demoed the application, and we developed a plan to scale the application and add it to the Genentech website.
R was used for this application due to the variety of factors. Primarily, there existed quite a few tools for analyzing geographic data, which made it easy to compute which pickup stops were the closest to the entered address. Additionally, R Shiny is a framework for making quick prototypes. As this application was origionally not intended to be used by 1000's of people, scalability was not a huge consideration.Geo-Data, App Development, Personal Project
This application was developed to provide diagnostic information for patient's heart health. Through classifying beats as normal or irregular, physicians could take a statistical view of patient's health data. On this project, I worked as the project lead; I established software requirements, orchestrated the development of our classification algorithm, and coordinated meetings with our research advisor.Health-Data, Digital Signal Processing, Project Management