Dr. Michael Pazzani is a Distinguished Scientist at the Halıcıoğlu Data Science Institute at University of California, San Diego. He conducts research in machine learning, explainable artificial intelligence, personalization, internet search, and recommendation systems. Prior to UCSD, Dr. Pazzani was the Vice Chancellor for Research and Economic Development at University of California, Riverside where he was also a professor of computer science with additional appointments in statistics and psychology. He received his Ph.D. in Computer Science from UCLA and was an assistant, associate and full professor at the University of California, Irvine, where he also served as Chair of Information and Computer Science.
Expert-Informed, User-Centric Explanations for Machine Learning
Pazzani, M., Soltani, S., Kaufman, R., Qian, S., Hsiao A. (2022). Expert-Informed, User-Centric Explanations for Machine Learning.
Deep Learning Radiographic Assessment of Pulmonary Edema: Optimizing Clinical Performance, Training With Serum Biomarkers
Huynh, J., Masoudi, S., Noorbakhsh, A., Mahmoodi, A., Kligerman, S., Yen, A., Jacobs, K., Hahn, L., Hasenstab, K., Pazzani, M., & Hsiao, A. (2022). Deep Learning Radiographic Assessment of Pulmonary Edema: Optimizing Clinical Performance, Training With Serum Biomarkers. IEEE Access, 10, 48577–48588. https://doi.org/10.1109/access.2022.3172706
A Comprehensive Explanation Framework for Biomedical Time Series Classification
Ivaturi, P., Gadaleta, M., Pandey, A. C., Pazzani, M., Steinhubl, S. R., & Quer, G. (2021). A Comprehensive Explanation Framework for Biomedical Time Series Classification. IEEE Journal of Biomedical and Health Informatics, 25(7), 2398–2408. https://doi.org/10.1109/jbhi.2021.3060997
Manifestation of virtual assistants and robots into daily life: Vision and challenges
Rawassizadeh, R., Sen, T., Kim, S. J., Meurisch, C., Keshavarz, H., Mühlhäuser, M., & Pazzani, M. (2019). Manifestation of virtual assistants and robots into daily life: Vision and challenges. CCF Transactions on Pervasive Computing and Interaction, 1(3), 163–174. https://doi.org/10.1007/s42486-019-00014-1
Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering
Rawassizadeh, R., Dobbins, C., Akbari, M., & Pazzani, M. (2019). Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering. Sensors, 19(3), 448. https://doi.org/10.3390/s19030448
Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches
Rawassizadeh, R., Tomitsch, M., Nourizadeh, M., Momeni, E., Peery, A., Ulanova, L., & Pazzani, M. (2015). Energy-Efficient Integration of Continuous Context Sensing and Prediction into Smartwatches. Sensors, 15(9), 22616–22645. https://doi.org/10.3390/s150922616
Machine Learning for User Modeling
Webb, G. I., Pazzani, M. J., & Billsus, D. (2001). Machine Learning for User Modeling. User Modeling and User-Adapted Interaction, 11, 19–29. https://doi.org/10.1023/a:1011117102175
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