Erin H. Bugbee

Erin H. Bugbee

Cognitive Decision Science PhD Student at Carnegie Mellon

Carnegie Mellon University


I am currently a 4th year 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 by building computational cognitive models of human decision making and through behavioral experimentation.

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  • Sequential Decision Making
  • Data Science in R and Python
  • 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 2023 – Aug 2023 Seattle, WA
Machine Learning University
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
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
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.

PDF Cite Video

(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 Cite Video

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

Cite PDF DOI Code Poster



Named Opportunity Scholar for posit::conf(2023) in Chicago.

Received Presidential Fellowship from Tata Consultancy Services, which funds my PhD for 2024.

Completed Applied Scientist Internship at Amazon Science and Amazon Web Services, with Machine Learning University. Developed course on bias and fairness in large language models, and taught hundred of Amazon employees topics in machine learning, including generative AI.

Attended and presented at Ellis Alicante-DDMLab Workshop in Alicante, Spain.

Published interactive article on Reinforcement Learning with Amazon’s Machine Learning University.


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

Presented first-authored paper at CogSci 2022.

Presented first-authored paper at Virtual MathPsych/ICCM 2022.

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)