Former GAI Scholar Nikita Sivakumar Earns Research Fellowship
Nikita Sivakumar, a Biomedical Engineering PhD student at Johns Hopkins University, has received an NTT Research Foundation Fellowship, marking an exciting new milestone in her work at the intersection of computational modeling and human health.
The NTT Research Foundation Fellowship recognizes academic performance, leadership, and community engagement and provides support for doctoral study. The fellowship program awards up to two full-ride fellowships each year, including one for eligible Biomedical Engineering PhD students.
GAI supported Nikita as the 2025–2026 Global Autoimmune Institute Scholar through a $15,000 award in partnership with the ARCS Foundation Metro Washington Chapter. During that time, her research focused on using machine learning, 3D imaging, and computational modeling to understand how B cells and T cells move and interact during immune responses to infection and vaccination.
These interactions are an important part of the adaptive immune response and help the body produce antibodies. But it can be difficult to experimentally isolate how specific patterns of cell movement affect when and how immune cells encounter one another. Nikita develops computer models that simulate these behaviors, giving researchers another way to investigate how the movement and organization of individual cells can shape a larger immune response.
Nikita has continued to advance this area of research at Johns Hopkins, studying how immune cells migrate and communicate during infection and vaccination. Her work has broader implications for understanding infectious and autoimmune diseases, as well as vaccine development.
Her computational expertise has also contributed to research across other areas of health and disease. She has worked on research using machine learning to improve estimates of blood oxygen levels from pulse oximetry and to predict treatment response in chronic spontaneous urticaria, as well as research examining aging and cardiovascular health. In the pulse oximetry study, Nikita contributed to data analysis for machine-learning models designed to address known inaccuracies in estimating blood oxygen levels, including disparities associated with skin tone.
Looking ahead, Nikita hopes to lead her own laboratory developing and validating computational models to better understand the molecular and cellular mechanisms of disease. Teaching and mentoring students are also important parts of the career she hopes to build.
GAI is proud to have supported Nikita and congratulates her on this latest achievement.
Recent Published Works (Open Access):
Woo, J., Stapleton, O., Luo, J., Chuang, C. C., Su, Y., Sankararaman, S., Mukherjee, E., Sivakumar, N., Calligy, K., Duffy, S., Mosier, R., Greenstein, J., Taylor, C. O., & Sen, D. G. (2025). AI-Driven SaO2 prediction from pulse oximetry and electronic health records. BioData mining, 18(1), 90. https://doi.org/10.1186/s13040-025-00511-3
Nidadavolu, L. S., Sosnowski, D. W., Sivakumar, N., Merino Gomez, A., Wu, Y., Laskow, T., Bopp, T., Milcik, N., Le, A., Zhang, C., Khare, P., Zammit, A., Grodstein, F., Walston, J. D., Bennett, D. A., Mathias, R. A., Phillip, J. M., Maher, B. S., Oh, E. S., & Abadir, P. M. (2025). Cardiovascular-Derived Circulating Cell-Free DNA Fragments Are Associated With Frailty and Increased Cardiovascular Events in Older Adults. The journals of gerontology. Series A, Biological sciences and medical sciences, 80(7), glaf081. https://doi.org/10.1093/gerona/glaf081
Simpson S, Sivakumar N, Phillip J, Chen Y, Saini S, Gao L. Machine Learning Predictive Models for Omalizumab Response in Patients with Chronic Spontaneous Urticaria (CSU). Journal of Allergy and Clinical Immunology.2026;157(2):AB426. doi:10.1016/j.jaci.2025.12.949.