I am an Assistant Professor of the Practice in Quantitative Analysis at Wesleyan University. I have a PhD in Computer Science and Engineering from the University of South Florida, and completed my postdoctoral training in the Cognitive Neuroscience of Creativity Lab at the Pennsylvania State University. Previously, I was a research scientist intern at Amazon Alexa, Meta, and Interdigital.
My work spans artificial intelligence, machine learning, psychometrics, and creativity. I'm interested in how to build better tests of human cognition using AI, and how measure and improve equivalent constructs in AI systems. I'm especially interested quantitative analysis as it pertains to the measurement of creativity and STEM learning; my vision for my work is that the clever (and careful) use of AI can improve the quality of modern education.
My dissertation used psychometrics to analyze the validity and reliability natural language inference, a popular assessment of inferential reasoning in natural language processing. As a postdoc, my research focused on the application of large language models to the development of educational and psychological assessments, especially creativity tests. My work has been published in high-impact journal and conferences across disciplines, including ACL, EMNLP, CogSci, PACA, and IMPS.
If you're an undergraduate at Wesleyan, I am actively looking for research assistants! If my work sounds interesting and you'd like to get involved, please email me!
For anyone else, I'm always looking for new collaborators, and welcome any inquiries about my work. I can be reached either by email or on LinkedIn.
My work spans artificial intelligence, machine learning, psychometrics, and creativity. I'm interested in how to build better tests of human cognition using AI, and how measure and improve equivalent constructs in AI systems. I'm especially interested quantitative analysis as it pertains to the measurement of creativity and STEM learning; my vision for my work is that the clever (and careful) use of AI can improve the quality of modern education.
My dissertation used psychometrics to analyze the validity and reliability natural language inference, a popular assessment of inferential reasoning in natural language processing. As a postdoc, my research focused on the application of large language models to the development of educational and psychological assessments, especially creativity tests. My work has been published in high-impact journal and conferences across disciplines, including ACL, EMNLP, CogSci, PACA, and IMPS.
If you're an undergraduate at Wesleyan, I am actively looking for research assistants! If my work sounds interesting and you'd like to get involved, please email me!
For anyone else, I'm always looking for new collaborators, and welcome any inquiries about my work. I can be reached either by email or on LinkedIn.
Updates
July 2025:
Joined Wesleyan as an Assistant Professor of the Practice
May 2025:
New Paper: Creative Preference Optimization
April 2025:
New Paper: Automated scoring of creative problem solving with large language models: A comparison of originality and quality ratings.
February 2025:
New Paper: How do Humans and Language Models Reason About Creativity?
Update: Accepted at CogSci 2025
Joined Wesleyan as an Assistant Professor of the Practice
May 2025:
New Paper: Creative Preference Optimization
April 2025:
New Paper: Automated scoring of creative problem solving with large language models: A comparison of originality and quality ratings.
February 2025:
New Paper: How do Humans and Language Models Reason About Creativity?
Update: Accepted at CogSci 2025
August 2024:
New Paper: Automated Item Generation for Creativity
July 2024:
Gave keynote presentation on automated item generation at ICCPAE 2024
New Paper: Automated Item Generation for Creativity
July 2024:
Gave keynote presentation on automated item generation at ICCPAE 2024