The accelerated pace by which biomedical big data is being generated in the healthcare enterprise holds enormous promise in developing individualized diagnostic and therapeutic strategies towards precision medicine. By combining genotypic and phenotypic information, new patterns and predictive markers can be uncovered and translated into clinical tools improving patient care. However, the size, complexity, and heterogeneity of the data pose critical barriers in discovering markers that are reproducible and clinically meaningful.
My research contributions have been focused in four areas:
- Adapting and validating novel decision models and uncertainty analysis to discover optimal strategies for individual patients;
- Formalizing literature for treatment selection and experiment planning;
- Improving the generalizability of predictive models; and
- Developing translational applications that assist clinical and translational researchers with interpreting available evidence for precision medicine.
These developments are being practically implemented in a variety of clinical domains, including lung cancer, brain cancer (glioblastoma multiforme), prostate cancer, and breast cancer.
I am interested in adapting and validating novel algorithms, translating them to applications that enable precision medicine. My students and I work on problems related to data wrangling, knowledge representation, modeling, and interpretation. We utilize a wide spectrum of approaches from logistic regression and statistical approaches to sequential pattern mining, deep learning, multi-arm/contextual bandits, depending on the problem at hand. While the approaches and methodologies that we develop have broad applicability across the biomedical domain, an emphasis of our work is on enhancing the role of imaging in diagnosis and patient management.
My research is highly multidisciplinary. My students and I collaborate with faculty members from centers across UCLA such as the Center for Domain Specific Computing, Integrative Center for Learning & Memory, Institute for the Risk Sciences, and the Center for Computer Vision and Imaging Biomarkers. We also are engaging in collaborative efforts with industrial partners such as Siemens Healthineers and ProSocial Applications.