Erin H. Bugbee

Erin H. Bugbee

Cognitive Decision Science PhD Student at Carnegie Mellon

Carnegie Mellon University

Biography

I am currently a PhD student in the Department of Social & Decision Sciences at Carnegie Mellon University. In my research, I study how humans learn and make sequential decisions from experience, and I do so by building computational cognitive models of human decision making and through behavioral experimentation.

Visit my Academic website.
Download my CV.

Interests
  • Sequential Decision Making
  • Human and Machine Learning
  • Computational Social Science
Education
  • PhD Cognitive Decision Science, 2025

    Carnegie Mellon University

  • MS Social and Decision Sciences, 2022

    Carnegie Mellon University

  • ScB Statistics with Honors, 2020

    Brown University

  • AB Behavioral Decision Sciences, 2020

    Brown University

Skills

Statistics
R
Python

Experience

 
 
 
 
 
Amazon
Applied Scientist Intern
May 2022 – Aug 2022 Seattle, WA
AWS Deep Learning, Machine Learning University
 
 
 
 
 
Dynamic Decision Making Lab @ Carnegie Mellon University
Graduate Researcher
Sep 2020 – Present Pittsburgh, PA
PI: Cleotilde Gonzalez. Studying sequential decision making through behavioral experimentation and cognitive modeling.
 
 
 
 
 
Brown Data Science Club
President
Sep 2019 – May 2020 Providence, RI
Led team of students and organized annual Brown Datathon and data science workshops.
 
 
 
 
 
Sloman Lab @ Brown University
Undergraduate Researcher
Sep 2019 – Sep 2020 Providence, RI
PI: Steven Sloman. Studied trust in machines in the workplace through behavioral experimentation.
 
 
 
 
 
Learning, Memory & Decision Lab @ Brown University
Undergraduate Researcher
Jan 2019 – May 2020 Providence, RI
PI: Matthew Nassar. Used reinforcement learning models to understand how place field remapping might be used to improve learning in dynamic environments through simulations of the multi-armed bandit task.
 
 
 
 
 
The Walt Disney Company
Sales Analytics & Insights Intern
May 2019 – Aug 2019 Orlando, FL
Parks, Experiences, and Consumer Products
 
 
 
 
 
Microsoft
Explore Intern
May 2018 – Aug 2018 Redmond, WA
Microsoft Support Engineering Group, Cloud & AI Platform
 
 
 
 
 
Undergraduate Researcher
Jun 2017 – Aug 2017 Providence, RI
Applied techniques from topological data analysis to music information retrieval.

Recent Publications

(2022). Leveraging Cognitive Models for the Wisdom of Crowds in Sequential Decision Tasks. Virtual MathPsych/ICCM 2022.

Video

(2021). Hipsters and the Cool: A Game Theoretic Analysis of Identity Expression, Trends and Fads. Psychological Review.

Cite OSF DOI

(2021). Diverse Experience Leads to Improved Adaptation: An Experiment with a Cognitive Model of Learning. Virtual MathPsych/ICCM 2021.

PDF Video

(2020). SuPP and MaPP: Adaptable Structure-Based Representations for MIR Tasks. In ISMIR.

Cite PDF DOI Code Poster

News

2022

Completed Applied Scientist Internship at Amazon Science and Amazon Web Services. Published interactive article on Logistic Regression, featured on Amazon Science Blog.

Presented first-authored paper at CogSci 2022, titled “Making Predictions Without Data: How an Instance-Based Learning Model Predicts Sequential Decisions in the Balloon Analog Risk Task.”

Presented first-authored paper at Virtual MathPsych/ICCM, titled “Leveraging Cognitive Models for the Wisdom of Crowds in Sequential Decision Tasks.”

Featured in a press release on our paper: Emily is so 2000: Research explores why popular baby names come and go.

2021

Passed Ph.D. qualifying exams.

2020

Started a Ph.D. in Cognitive Decision Science in the Department of Social and Decision Sciences at Carnegie Mellon University.

Graduated Magna Cum Laude from Brown University with a Bachelor of Science with Honors in Statistics and a Bachelor of Arts in Behavioral Decision Sciences. Won the Thesis Award for Statistics and the Premium for Excellence in Behavioral Decision Sciences.

Featured in the Meeting Street Podcast: The History and Science of Virtual Reality, Cogut Institute for the Humanities at Brown University.

Won the American Statistical Association StatsGrad Award.

2019

Interviewed by the Brown Department of Computer Science regarding Computer Science for Societal Good.

2018

Featured by the Brown Data Science Initiative regarding my experiences in data science, titled “The Wonderful World of Women in Data Science."

Presented our paper at the International Society for Music Information Retrieval in Paris, France.

Won the Outstanding Poster Award at the Joint Mathematics Meetings.

Teaching

Carnegie Mellon University

  • Thinking in Person vs. Thinking Online, Prof. Danny Oppenheimer (Fall 2020)

Brown University

  • NEUR 1660: Neural Computations in Learning and Decision Making (Spring 2020)
  • CSCI 0100: Data Fluency for All, Head Teaching Assistant (Fall 2019)
  • CLPS 0220: Making Decisions (Spring 2019)
  • CSCI 1951A: Data Science (Spring 2019)
  • PHP 1501: Essentials of Data Analysis (Fall 2018)
  • APMA 1655: Statistical Inference I (Fall 2018)
  • CSCI 0100: Data Fluency for All (Fall 2017)