My lab’s general research interests are in the areas of oncology bioinformatics, artificial intelligence, machine learning algorithms, multimodality image analysis, and treatment outcome modeling. I operate at the interface of physics, biology, and engineering. The primary motivation is to design and develop novel approaches to unravel cancer patients’ tumor and normal tissue responses to treatment by synthesizing knowledge from physical, biological, and imaging information into advanced computational models using top-down (machine learning) and bottom-up (first principles) techniques and evaluate their performance in clinical and preclinical settings. These models could be then used to personalize cancer patients’ treatment, help understand the underlying biological response to disease, and optimize clinical decision making. These research interests involve three broad themes: bioinformatics and outcome modeling of radiation response (radiogenomics). multimodality image analysis (radiomics), medical physics (radiation dosimetry).
- Bioinformatics and outcome modeling: design and develop large-scale datamining methods and software tools to identify robust biomarkers of radiotherapy treatment outcomes from clinical and preclinical data using complex systems analysis and machine learning approaches (radiogenomics). Investigate the application of these methods to adapt therapy and design novel clinical trials.
- Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
- Medical physics radiation measurement: design and develop new technologies for radiation using optical and acoustics techniques to interrogate radiation response in real time for online targeting and adaptation.