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. I study how humans learn and make sequential decisions from experience, and I do so by building computational cognitive models of human and artificial decision making and through behavioral experimentation.

Visit my Academic website.
Download my CV.

Interests
  • Sequential Decision Making
  • Learning in Humans and Machines
  • Computational Social Science
Education
  • PhD Cognitive Decision Science, 2025

    Carnegie Mellon University

  • ScB Statistics with Honors, 2020

    Brown University

  • AB Behavioral Decision Sciences, 2020

    Brown University

Skills

Statistics
R
Python

Experience

 
 
 
 
 
Graduate Researcher
Sep 2020 – Present Pittsburgh, PA
PI: Cleotilde Gonzalez
 
 
 
 
 
President
Sep 2019 – May 2020 Providence, RI
 
 
 
 
 
Undergraduate Researcher
Sep 2019 – Sep 2020 Providence, RI
PI: Steven Sloman. Studied trust in machines in the workplace through behavioral experimentation.
 
 
 
 
 
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.
 
 
 
 
 
Sales Analytics & Insights Intern
May 2019 – Aug 2019 Orlando, FL
Parks, Experiences, and Consumer Products
 
 
 
 
 
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

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

(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.

PDF Cite Code Poster

(2018). SE and SnL Diagrams: Flexible Data Structures for MIR. In ISMIR.

PDF Cite

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)