My research interests include end-to-end research on medical embedded systems and the application of data mining and machine learning algorithms necessary to make personalized, preventative medical treatments possible through advanced health analytics . My background is in embedded systems design, where I studied sensor fusion, reconfigurable architectures and systems, hardware accelerators, and gpu computing. During my Ph.D. I applied data mining and machine learning techniques to these systems to develop a personalized, exercise-level activity-recognition video game with wearable sensors. I am now primarily concerned with the ability to use supervised and unsupervised techniques to learn more about medical prediction and risk-stratification in order to better develop personalized medical systems, prediction models, comparative effectiveness techniques, and combine wearable sensors and other necessary data to make a clinical impact at the system level, provider level, and patient level.
- Ph.D. in Computer Science, University of California Los Angeles - (Los Angeles, California, United States) 2014
- B.A. in Applied Mathematics (with Specialization in Computer Science Algorithms), University of California, Berkeley - (Berkeley, California, United States) 2007
- B.S. in Electrical Engineering and Computer Science, University of California, Berkeley - (Berkeley, California, United States) 2007
- Mortazavi, B. J., Bucholz, E. M., Desai, N. R., Huang, C., Curtis, J. P., Masoudi, F. A., ... Krumholz, H. M. (2019). Comparison of Machine Learning Methods With National Cardiovascular Data Registry Models for Prediction of Risk of Bleeding After Percutaneous Coronary Intervention. JAMA Network Open. 2(7), e196835-e196835.
- Huang, C., Murugiah, K., Mahajan, S., Li, S., Dhruva, S. S., Haimovich, J. S., ... Krumholz, H. M. (2018). Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine. 15(11), e1002703-e1002703.
- Nathan, V., Paul, S., Prioleau, T., Niu, L. i., Mortazavi, B. J., Cambone, S. A., ... Jafari, R. (2018). A Survey on Smart Homes for Aging in Place. IEEE Signal Processing Magazine. 35(5), 111-119.
- Goel, N., Chaspari, T., Mortazavi, B. J., Prioleau, T., Sabharwal, A., & Gutierrez-Osuna, R. (2018). Knowledge-driven dictionaries for sparse representation of continuous glucose monitoring signals. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 00, 191-194.
- Hezarjaribi, N., Dutta, R., Xing, T., Murdoch, G. K., Mazrouee, S., Mortazavi, B. J., & Ghasemzadeh, H. (2018). Monitoring Lung Mechanics during Mechanical Ventilation using Machine Learning Algorithms. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 00, 1160-1163.
- Solis, R., Pakbin, A., Akbari, A., Mortazavi, B. J., & Jafari, R. (2019). A human-centered wearable sensing platform with intelligent automated data annotation capabilities. 255-260.
- Akbari, A., Liu, P., Mortazavi, B. J., & Jafari, R. (2019). Tagging wearable accelerometers in camera frames through information translation between vision sensors and accelerometers. 2012 IEEE/ACM THIRD INTERNATIONAL CONFERENCE ON CYBER-PHYSICAL SYSTEMS (ICCPS 2012). 174-184.
- Pakbin, A., Rafi, P., Hurley, N., Schulz, W., Krumholz, M. H., & Mortazavi, J. B. (2018). Prediction of ICU Readmissions Using Data at Patient Discharge. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. 00, 4932-4935.
- Alinia, P., Saeedi, R., Mortazavi, B., Rokni, A., & Ghasemzadeh, H. (2015). Impact of Sensor Misplacement on Estimating Metabolic Equivalent of Task with Wearables. 1-6.
- Mortazavi, B., Pourhomayoun, M., Nyamathi, S., Wu, B., Lee, S. I., & Sarrafzadeh, M. (2015). Multiple Model Recognition for Near-Realistic Exergaming. 140-148.