Joanne Wardell, M.S.

Ph.D student at the Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science (TReNDS) center

Research Expertise

machine learning
deep learning
predictive neuroimaging
python
pytorch
mentorship
tutoring
academic editing/proofreading
scientific communication

About

Joanne Wardell is a computer scientist with a strong passion for research and teaching. She receives her Ph.D. in Computer Science from Georgia State University in 2027, where she specializes in artificial intelligence and machine learning. She also holds a B.S. in Computer Science from Valdosta State University, graduating in 2019 with honors. Throughout her academic career, Joanne has excelled in both her research and teaching roles. As a Graduate Research Assistant at the TReNDS Center, she has contributed to several projects focused on using machine learning techniques to analyze brain imaging data. Her work has been published in top journals and presented at international conferences. In addition to her research, Joanne has also been a dedicated Graduate Teaching Assistant at Georgia State University, where she has taught various undergraduate and graduate courses in computer science. She is known for her excellent communication skills and ability to make complex concepts easy to understand for her students. Joanne has also gained practical experience in the industry as an Associate Member of Technical Staff at Engineers' Consulting Group. Here, she worked on developing software solutions for clients in various industries, further honing her programming and problem-solving skills. With her strong academic background and diverse experience, Joanne is well-equipped to take on new challenges and make valuable contributions in the field of computer science. She is committed to using her skills and knowledge to advance the field and inspire the next generation of computer scientists.

Publications

Combining fast and slow fMRI sampling rates can enhance predictive power in resting-state data

NeuroImage / Jan 01, 2026

Wardell, J., Solovyeva, K., Danks, D., Huotari, N., Kiviniemi, V. J., Korhonen, V. O., DeRamus, T., Pearlson, G. D., Calhoun, V., & Plis, S. (2026). Combining fast and slow fMRI sampling rates can enhance predictive power in resting-state data. NeuroImage, 325, 121579. https://doi.org/10.1016/j.neuroimage.2025.121579

Classifying Schizophrenia Patients and Healthy Individuals: Whole Brain SPECT Functional Connectivity Using Support Vector Machine Classification

Jan 01, 2025

Harikumar, A., Wardell, J., Keator, D., Amen, D., Plis, S. M., & Calhoun, V. D. (2025). Classifying Schizophrenia Patients and Healthy Individuals: Whole Brain SPECT Functional Connectivity Using Support Vector Machine Classification. https://doi.org/10.2139/ssrn.5338394

Education

Georgia State University

Ph.D., Computer Science / May, 2027 (anticipated)

Atlanta, Georgia, United States of America

Valdosta State University

B.S., Computer Science / May, 2019

Valdosta, Georgia, United States of America

Experience

TReNDS Center

Graduate Research Assistant / January, 2023Present

Georgia State University

Graduate Teaching Assistant / January, 2022January, 2023

Engineers' Consulting Group

Associate Member of Technical Staff / June, 2019

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