Reflecting on my research activities as a PhD candidate in Computer Science and Engineering, my focus spans across several innovative domains, including computer vision, bioinformatics, multimodal analysis, and deep learning.

Additionally, my research extends to multimodal risk prediction and survival analysis. This involves integrating various types of data and computational techniques to predict patient outcomes, particularly in oncology. By analyzing diverse data sets, from clinical metrics to imaging data, I aim to develop models that can more accurately forecast patient prognosis and survival rates. This approach is crucial in personalizing patient care and improving treatment strategies.

Another significant part of my research revolves around the interpretability of computational models in clinical settings. I strive to make these advanced models accessible and understandable to healthcare professionals, enhancing their practical utility in patient care. The overarching goal of my research is to bridge the gap between complex technological solutions and their real-world applications in healthcare, ultimately aiming to improve patient outcomes and advance the field of medical technology. My research activities combine theoretical exploration with practical application, all driven by the aspiration to make meaningful contributions to healthcare through innovative technology.