Erin H. Bugbee

Erin H. Bugbee

Cognitive Decision Science PhD Student at Carnegie Mellon

Carnegie Mellon University


I am currently a PhD student in the Department of Social & Decision Sciences at Carnegie Mellon University. For Summer 2022, I am an Applied Scientist Intern at Amazon Science and Amazon Web Services (AWS) on the Machine Learning University Team. 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.

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Download my CV.

  • Sequential Decision Making
  • Human and Machine Learning
  • Computational Social Science
  • 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




Applied Scientist Intern
May 2022 – Present Seattle, WA
AWS AI, Machine Learning University
Graduate Researcher
Sep 2020 – Present Pittsburgh, PA
PI: Cleotilde Gonzalez. Studying sequential decision making through behavioral experimentation and cognitive modeling.
Sep 2019 – May 2020 Providence, RI
Led team of students and organized annual Brown Datathon and data science workshops.
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

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


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

Cite PDF DOI Code Poster



First-author paper accepted to the Cognitive Science 2022 Conference, titled “Making Predictions Without Data: How an Instance-Based Learning Model Predicts Sequential Decisions in the Balloon Analog Risk Task.”

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


Passed Ph.D. qualifying exams.


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.


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


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.


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)