I am currently a PhD student in the Department of Social & Decision Sciences at Carnegie Mellon University. In Summer 2022, I will be an Applied Scientist Intern at Amazon Science and AWS AI 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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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
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.