Biography
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.
Visit my Academic website.
Download my CV.
- Sequential Decision Making
- Human and Machine Learning
- Computational Social Science
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PhD Cognitive Decision Science, 2025
Carnegie Mellon University
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MS Social and Decision Sciences, 2022
Carnegie Mellon University
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ScB Statistics with Honors, 2020
Brown University
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AB Behavioral Decision Sciences, 2020
Brown University
Skills
Experience
Featured Publications
Recent Publications
News
2022
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.
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)
