Sirlanci, M., Albers, D., Kwak, J., Smith, C., Bennett, T..D, Bair, S.M. (2022) Applying Machine Learning and Mechanistic Modeling to Improve Outcome Prediction in Cellular Immunotherapy for Cancer. Submitted.
Albers, D., Claassen, J., Der-Nigoghossian, C., Gluckman, B., Hripcsak, G., Levine, M., Sirlanci, M. Interpretable Forecasting of Physiology in the ICU Using Constrained Data Assimilation and Electronic Health Record Data, in preparation.
Kryshchenko, A., Sirlanci, M., & Vader, B. (2021). Nonparametric Estimation of Blood Alcohol Concentration from Transdermal Alcohol Measurements Using Alcohol Biosensor Devices. In Advances in Data Science (pp. 329-360). Springer, Cham.
Albers, D. J., Levine, M. E., Sirlanci, M., & Stuart, A. M. (2019). A simple modeling framework for prediction in the human glucose-insulin system. arXiv preprint arXiv:1910.14193.
Sirlanci, M., Rosen, I. G., Luczak, S. E., Fairbairn, C. E., Bresin, K., & Kang, D. (2018). Deconvolving the input to random abstract parabolic systems: a population model-based approach to estimating blood/breath alcohol concentration from transdermal alcohol biosensor data. Inverse problems, 34(12), 125006.
Banks, H. T., Flores, K. B., Rosen, I. G., Rutter, E. M., Sirlanci, M., & Thompson, W. C. (2018). The Prohorov Metric Framework and aggregate data inverse problems for random PDEs. Commun. Appl. Anal, 22, 415-446.
Sirlanci, M., Luczak, S. E., Fairbairn, C. E., Kang, D., Pan, R., Yu, X., & Rosen, I. G. (2019). Estimating the distribution of random parameters in a diffusion equation forward model for a transdermal alcohol biosensor. Automatica, 106, 101-109.
Sirlanci, M., Luczak, S. E., & Rosen, I. G. (2019). Estimation of the distribution of random parameters in discrete time abstract parabolic systems with unbounded input and output: Approximation and convergence. Communications in applied analysis, 23(2), 287.
Sirlanci, M., Luczak, S., & Rosen, I. G. (2017, May). Approximation and convergence in the estimation of random parameters in linear holomorphic semigroups generated by regularly dissipative operators. In 2017 American Control Conference (ACC) (pp. 3171-3176). IEEE.