Chelsea Parlett

Lecturer

Electrical Engineering and Computer Science, Fowler School of Engineering

Office Location: Keck Center for Science and Engineering Swenson Hall N325

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: 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.; Springer, T., Linstead, E., Zhao, P., & Parlett-Pelleriti, C. (2022). Towards QoS-Based Embedded Machine Learning. Electronics, 11(19), 3204.; 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.; 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.; 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).; 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.

Chelsea Parlett

Biography

EDUCATION/TRAINING

RESEARCH/TEACHING INTERESTS

Recent Creative, Scholarly Work and Publications

Faculty Directories by School or College