Jordan Harrod is a highly educated and experienced individual in the fields of medical engineering and medical physics. She received her Ph.D. in Medical Engineering and Medical Physics from the prestigious Harvard-MIT Division of Health Sciences and Technology in 2025. Prior to this, she earned her Bachelor of Science in Biomedical Engineering from Cornell University in 2018. Currently, Jordan is a PhD candidate at the Massachusetts Institute of Technology, where she is conducting cutting-edge research in the intersection of medicine and engineering. She is also a skilled AI consultant, using her expertise to assist businesses and organizations with implementing artificial intelligence solutions. Throughout her education and career, Jordan has gained a deep understanding of the complexities of medical technology and the importance of precision and innovation in this field. She is a dedicated and driven individual who is constantly seeking ways to improve healthcare through technology. In addition to her academic and professional pursuits, Jordan is also passionate about promoting diversity and inclusivity in the STEM fields. She actively mentors and supports young women and underrepresented minorities, encouraging them to pursue careers in science and engineering. Overall, Jordan Harrod is a highly accomplished and talented individual with a strong passion for using technology to improve the lives of others. Her education, experience, and dedication make her a valuable asset to any organization in the medical engineering and medical physics fields.

Jordan Harrod

PhD Candidate at Massachusetts Institute of Technology

Boston

Research Expertise

neuroscience

anesthesia

connectomics

deep learning

About

Publications

How Machine Learning is Powering Neuroimaging to Improve Brain Health

Neuroinformatics / Mar 28, 2022

Singh, N. M., Harrod, J. B., Subramanian, S., Robinson, M., Chang, K., Cetin-Karayumak, S., Dalca, A. V., Eickhoff, S., Fox, M., Franke, L., Golland, P., Haehn, D., Iglesias, J. E., O’Donnell, L. J., Ou, Y., Rathi, Y., Siddiqi, S. H., Sun, H., Westover, M. B., … Gollub, R. L. (2022). How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics, 20(4), 943–964. https://doi.org/10.1007/s12021-022-09572-9

Mineral Distribution Spatially Patterns Bone Marrow Stromal Cell Behavior on Monolithic Bone Scaffolds

Acta Biomaterialia / Aug 01, 2020

Zhou, H., Boys, A. J., Harrod, J. B., Bonassar, L. J., & Estroff, L. A. (2020). Mineral Distribution Spatially Patterns Bone Marrow Stromal Cell Behavior on Monolithic Bone Scaffolds. Acta Biomaterialia, 112, 274–285. https://doi.org/10.1016/j.actbio.2020.05.032

Top-Down Fabrication of Spatially Controlled Mineral-Gradient Scaffolds for Interfacial Tissue Engineering

ACS Biomaterials Science & Engineering / May 07, 2019

Boys, A. J., Zhou, H., Harrod, J. B., McCorry, M. C., Estroff, L. A., & Bonassar, L. J. (2019). Top-Down Fabrication of Spatially Controlled Mineral-Gradient Scaffolds for Interfacial Tissue Engineering. ACS Biomaterials Science & Engineering, 5(6), 2988–2997. https://doi.org/10.1021/acsbiomaterials.9b00176

Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Feb 15, 2021

’t Hart, B. M., Achakulvisut, T., Akrami, A., Alicea, B., Beierholm, U., Blohm, G., Bonnen, K., Butler, J. S., Caie, B., Cheng, Y., Chow, H. M., David, I., DeWitt, E., Drugowitsch, J., Dwivedi, K., Fiquet, P.-É., Forest, J., Galbraith, B., Gu, Q., … Wyble, B. (2021). Neuromatch Academy: a 3-week, online summer school in computational neuroscience. https://doi.org/10.31219/osf.io/9fp4v

Deep Policy Learning: Opportunities and Challenges from the Evidence Act

Harvard Data Science Review / Nov 01, 2019

Potok, N. (2019). Deep Policy Learning: Opportunities and Challenges from the Evidence Act. Harvard Data Science Review. https://doi.org/10.1162/99608f92.77e63f8f

Development and Validation of a Risk Score for Age-Related Macular Degeneration: The STARS Questionnaire

Investigative Opthalmology & Visual Science / Dec 19, 2017

Delcourt, C., Souied, E., Sanchez, A., & Bandello, F. (2017). Development and Validation of a Risk Score for Age-Related Macular Degeneration: The STARS Questionnaire. Investigative Opthalmology & Visual Science, 58(14), 6399. https://doi.org/10.1167/iovs.17-21819

Education

Harvard-MIT Division of Health Sciences and Technology

Ph.D. in Medical Engineering and Medical Physics / May, 2025 (anticipated)

Cambridge, Massachusetts, United States of America

Cornell University

B.S. in Biomedical Engineering , Department of Biomedical Engineering / 2018

Ithaca

Experience

Massachusetts Institute of Technology

PhD Candidate / August, 2018 — Present

Self-Employed

AI Consultant / 2021 — Present

Links & Social Media

Research Web Site

Google Scholar

Website

ORCID

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