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About Me


I am a second year PhD candidate in statistics at Northwestern. I'm interested in statistics as it applies to computer science and social science. 

Prior to beginning at Northwestern, I was an undergraduate in statistics at UCLA.


Kayla Schroeder

Statistics PhD Student at Northwestern



  • LinkedIn
June 2020 - September 2020

Customer Success Engineering Intern

Palo Alto Networks

  • In Tableau, developed visualizations of cloud security data for use by both business and technical teams to make product decisions. Worked to customize dashboards to explain the data effectively and provide insight across the customer success team. Leveraged visualization best practices to develop engaging and interactive Tableau dashboards.

  • Worked with BigQuery and the company data lake to analyze cloud security accounts and develop data views. Used strong analytical and problem solving skills to understand new business processes and complex relationships between data systems.

  • Created a mapping of the cloud security data lake including a visualization in LucidChart. Worked to create documentation that was understandable and useful to all throughout the customer success team.

January 2018 - June 2020

Statistics Brain Imaging Researcher

UCLA Brain Mapping Center

  • Developed, in R, linear mixed modeling capabilities to analyze output from automated MRI imaging software. Also significantly shortened analysis time and simplified the package for the user. 

  • Used R to add data visualizations to the region of interest and volumetric statistical analysis output.

  • Made the statistical analysis output more understandable and straightforward to reproduce.

  • Working to publish a paper on the completed work.

June 2019 - September 2019

Data Science Intern

Lawrence Livermore National Laboratory

  • Worked with a team to develop a statistical model for determining which models were most effective at forecasting the movement of hazardous waste. This work helped drastically reduce the time and computational intensity required to produce an accurate prediction for the hazardous waste movement.  

  • Developed a model in Python to detect novel observations and search for structure within hazardous material release weather data.

  • Used Python to predict target values from weather uncertainty data predictor variables. 

  • Preparing a paper for submission on the completed work.

June 2018 - September 2018

Nuclear Physics Data Analytics Intern

Lawrence Livermore National Laboratory

  • Working with a project team, I developed parts of a model that predicts cloud movement and final height. This model is being implemented into the National Atmospheric Release Advisory Committee’s fallout model to more accurately predict potential nuclear detonation effects on surrounding areas.

  • In order to model safety zones in a nuclear blast, I analyzed cloud rise from nuclear detonation films using a Microsoft Visual Basic tool.

  • Developed key components of a program that reads in data and determines final cloud height while relying on external data as little as possible to improve the accuracy of the model’s output.   


Doctoral Degree


PhD Candidate in Statistics

Expected June 2025


Bachelors Degree


Bachelors of Science, Statistics

Minor in Mathematics

Cum Laude








Research and Publications
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