Kayla Frisoli

Ph.D. Student in Statistics & Data Science at Carnegie Mellon Univeristy


About me

My name is Kayla Frisoli, and I am a Ph.D. student in the Department of Statistics & Data Science at Carnegie Mellon University. I graduated with a Bachelor of Science from the Department of Statistics at UCLA in 2015. My current research interests lie in statistical methodology for social science applications including: record linkage, clustering, topic modeling, and active learning. In my free time, you may find me cooking, watching/playing sports, or listening to live music.


Upcoming events


I work with Dr. Rebecca Nugent on problems in record linkage. Record linkage is the process of identifying records corresponding to unique entities across datasets. We are currently working to merge historical Irish census data from 1901 and 1911. We incorporate known sociodemographic changes, household network structure, and information about the hand labeling process into the linkage to overcome merge challenges. We crowd-source individual and household labels and track human-computer interactions through an R Shiny application found here.

My previous record linkage project was at the U.S. Census Bureau, where I was advised by Dr. John Abowd, Dr. Steve Fienberg, Dr. Samuel Ventura, and Dr. Jared Murray. We developed a semi-supervised approach to expand and enhance current record linkage methods.

I have also worked on exploring the effect of tennis court surfaces on player performance (with Dr. Shannon Gallagher and Dr. Amanda Luby) and creating a comprehensive evaluation of minimax linkage for hierarchical clustering (with Dr. Xiao Hui Tai).

Papers can be found on my CV.


2017-2018 PhD TA Excellence Award
2015-2016 PhD TA Excellence Award


  • Summer 2018: 36-315, Statistical Graphics and Visualization, Syllabus

Course/software developer

  • Manage and coordinate the conversion of 36-202 to R Studio:
    Spring 2018: 36-202, Statistics & Data Science Methods, Syllabus

Teaching assistant

  • Spring 2019: 36-462/662, Data Mining, Syllabus
  • Fall 2018: 36-311, Statistical Analysis of Networks, Syllabus
  • Spring 2018: 36-202, Statistics & Data Science Methods, Syllabus
  • Fall 2017: 36-461/661, Statistical Methods in Epidemiology, Syllabus
  • Spring 2017: 36-200, Reasoning with Data, Syllabus
  • Spring 2016: 36-315, Statistical Graphics and Visualization, Syllabus
  • Fall 2015: 46-923, Introduction to Statistical Inference, Syllabus
  • Fall 2015: 46-921, Introduction to Probability, Syllabus


  • Courses: Algebra, Geometry, Pre-Calculus, Calculus, AP Statistics, Introductory Probability


  • Corey Emery
    "Using Data Visualization to Uncover Demographic Trends in Early 1900s Irish Census Records"
  • Cheyenne Ehman, Jake Parker, Adam Tucker, Yuchao Wu
    "Classifying Items on Bed Bath and Beyond Receipts"
  • Alec Albright, Ani Chowdhury, Kaixin (Katherine) Li, You (Winston) Zheng, Lantian (Sky) Xu
    "Analysis of Steady User Lifetimes – Churn Prediction and Customer Segmentation"


Lead Organizer
Pittsburgh userR Meetup
Pittsburgh Data Jam
Student Representative
American Statistical Association Pittsburgh Chapter

Carnegie Mellon University

Co-President & Community Outreach Chair
CMU Women in Statistics group
Ambassador & Organizer
Women in Data Science (WiDS)
Student Advisory Committee
Department of Statistics & Data Science
Committee Member
CMU Graduate Student Assembly Social Committee
Student Contact
Department of Statistics & Data Science Ph.D. Admissions Committee

University of California Los Angeles

Statistics Club
UCLA DataFest
Commencement Speaker
2015 Department of Statistics Graduation Ceremony
Mentor (current)
UCLA Alumni Mentor Program
Alumni Dinner Organizer (current)
Dinner for 12 Strangers in Pittsburgh, PA, 2016, 2018
Assistant Financial Vice President, Greek Relations Chair
Gamma Phi Beta International

What I'm cooking

View on: Shiny, Github

Contact Me

Department of Statistics & Data Science
Carnegie Mellon University
Pittsburgh, PA, USA