Chelsea Parlett
- Education:
- University of California, San Diego, Bachelor of Science
Chapman University, Master of Science
Chapman University, Ph.D.
Biography
EDUCATION/TRAINING
2017 - 2021 (expected)
Ph.D. in Computational and Data Science, Schmid College of Science and Technology, Chapman University (Mentor: Dr. Erik Linstead)
2017 - 2019
M.S. in Computational and Data Science, Schmid College of Science and Technology, Chapman University (Mentor: Dr. Erik Linstead)
2011 - 2015
B.S. in Psychology, Dept. of Psychology, University of California, San Diego
RESEARCH/TEACHING INTERESTS
Chelsea’s research interests center around applying novel statistical and machine learning methods to behavioral data in areas such as Psychology. She specializes in unsupervised machine learning and bayesian statistics. As an instructor, Chelsea values using new, engaging technology and other pedagogical techniques (such as flipped classes, and simulation-based instruction for statistical concepts) in order to better serve students and to help them develop a sense of passion and engagement for their subjects.
Recent Creative, Scholarly Work and Publications
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Parlett-Pelleriti, C. M., Stevens, E., Dixon, D., & Linstead, E. J. (2023). Applications of unsupervised machine learning in autism spectrum disorder research: a review. Review Journal of Autism and Developmental Disorders, 10(3), 406-421.
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Springer, T., Linstead, E., Zhao, P., & Parlett-Pelleriti, C. (2022). Towards QoS-Based Embedded Machine Learning. Electronics, 11(19), 3204.
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Gardner-Hoag, J., Novack, M., Parlett-Pelleriti, C., Stevens, E., Dixon, D., & Linstead, E. (2021). Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study. JMIR Medical Informatics, 9(6), e27793.
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Parlett-Pelleriti, C., Linstead, E. J., Sumner, E. S., Nguyen, H. Q., Stokes, R., Sarnecka, B. W., & Jaeggi, S. M. (2020, December). A Hierarchical Bayesian IRT Analysis of Children’s Risk Propensity. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 942-947). IEEE.
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Ali, R. H., Parlett-Pelleriti, C., & Linstead, E. (2020, June). Cheating Death: A Statistical Survival Analysis of Publicly Available Python Projects. In Proceedings of the 17th International Conference on Mining Software Repositories (pp. 6-10).
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Gharakhanian, A., Larmore, C. Y., & Parlett-Pelleriti, C. (2020). Achieving Externship Success: An Empirical Study of the All-Important Law School Externship Experience. S. Ill. ULJ, 45, 165.