Hi, welcome to my personal page! 🎉

I'm a BS/MS Student in the Utah School of Computing studying Computer Science and Human-Centered Computing with a focus in Computational Information Design. My previous work and experience is in developing tools, techniques and abstractions for visualizing data on the web and currently I am working on projects in distributed systems, visualization and data. During the 2020-2021 school year, I was a research assistant in the Utah Visualization Design Lab where I worked on MultiAggr, a novel visualization technique for aggregating and visualizing multivariate networks (MVNs) using categorical node attributes and the adjacency matrix layout of an MVN as part of the MultiNet Project. Currently I am working as a Graduate Teaching Assistant for CS 5630/6630: Visualization for Data Science and previously TA'd CS 6017 Graduate Data Analytics and Visualization and introductory computer science classes in the Utah School of Computing.

I will graduate with my Bachelor of Science in Computer Science and MS Computing(Human Centered Computing) from the University of Utah in December 2022. Previously I have worked as a software engineer, data analytics/BI developer, and data analyst in the contract electronic manufacturing industry, electric transit industry and biomedical sciences. Outside the classroom, I have volunteered as a student coordinator for the Utah Data Science Club as part of the Utah Center for Data Science where I helped organize guest presentations by Shirley Wu, Eunice Jun, Alberto Cairo and create workshops for learning visualization and python. In 2022, I was a student volunteer at the Eyeo Festival. In my spare time I love to participate in outdoor activites, study piano, watch Top Chef and read Flowing Data.

If you are hiring for data visualization developers, full-stack data development, and backend roles please reach out.


MultiAggr: A Technique for Aggregating Multivariate Networks

Submitted and accepted to the Proceedings of the 2021 IEEE VIS Conference.

Developed a novel visualization technique for aggregating multivariate networks using categorical node attributes. Users can interactively explore the relationship between different aggregate groups and members of a specific aggregate group. The technique is domain agnostic and can be used to explore multivariate networks in different data domains.

MultiAggr: A Technique for Aggregating Multivariate Networks

Undergraduate thesis on categorical node attribute aggregation for multivariate networks using the adjacency matrix layout.

Projects and Contributions

UTA Electric Bus Transit Optimization

Visual Analytics Tool for helping the Utah Transit Authority and Utah Civil/Environmental Engineering Transportation Group understand and visualize different electric bus transportation optimization plans, environmental factors and bus routes.

Style Transfer Work Bench

Machine learning workbench prototype for visually interpreting and exploring activation values in the style transfer problem using SqueezeNet Neural Network Architecture. Developed with Jared Amen and Alan Weber.


Visual data wrangling technique for aggregating dense multivariate networks across a single axis with a single categorical node attribute.

Wasatch Climbing Policy

An interactive visual story about climbing policy in the Wasatch

Gap Minder

A redesign of Hans Rosling Gap Minder project visualization

FIFA 2018 Statistics

A visualization the match statistics from the 2018 FIFA World Cup

Interactive Anscombe Quartet

Interactive visualizations of the different Anscombe Quartet datasets

2018 New York Weather Bar Chart Dashboard

Bar chart histogram dashboard analyzing different features of 2018 New York Weather Data. Exercise from Amelia Wattenberger’s FullStack-D3 Book

2018 New York Weather Line Chart

Line chart featuring 2018 New York Weather Data for the entire year. Exercise from Amelia Wattenberger’s Full-Stack D3 Book

Motion Blur

Motion blur raytracing scene


A raytracing program implemented with python that creates a bear with geometric shapes using phong shading, illumination, and reflections

Fire Mining

A data mining exploration of forest fires in South America

HCI Visualization + Data Analytics Study

An HCI study using the interview method and Amazon Mechanical Turk to study how software developers and researchers create human-centric visualizations with a focus on accessibility and the tools used to create visualizations.

Work Experience

  • Teaching Assistant

    University of Utah School of Computing

    CS 1420 (Accelerated Introduction to Computer Science)(Fall 2021, Spring 2022)
    CS 6017 MSD (Data Analytics and Visualization)(Summer 2021).
    Grade students' assignments, teach labs and hold office hours for answering questions related to the class.

  • Research Assistant

    Utah Visualization Design Lab

    Contributed to the MultiNet Project. Working with members of VDL and Kitware Inc., developed MultiAggr, a technique for performing visual aggregation of the adjacency matrix representation of a multivariate network. In addition to MultiAggr, I also contributed to the MultiMatrix user interface. MultiAggr was accepted for the IEEE VIS 2021 Poster Submission.

  • Software Engineering Intern


    Compiled a report on different kinds of machine learning methods including classification, random forests, and neural nets. Derived equations for softmax and RELU functions and the math behind a 3 layer neural network. Wrote programs to analyze different kinds of data including stock prediction using quandl with matplotlib, pandas and bokeh.