I am an Associate Member in the Department of Medical Physics. I work to combine
techniques from machine learning and estimation theory with computer vision methods to
solve challenging problems involving multi-modality image analysis including segmentation
of tumors and normal tissues, longitudinal tracking of tumor treatment response and
prediction of subsequent development of toxicities to cancer treatments from computed
tomography (CT), cone beam CT used in radiotherapy treatments, as well as magnetic
resonance (MR) images. I am the director of AI for Image Guided Therapies Lab where we
develop and translate new artificial intelligence (AI) and deep learning methods for clinical
use. Four different AI models developed by my group are used for radiotherapy treatment
planning for prostate, breast, lung, and head and neck cancers with additional disease sites
planned for release into clinical use this year.

After receiving a PhD in computer science in 2006 from the University of Minnesota, Twin
Cities, I developed algorithms for human robot interaction using machine learning and
computer vision as a postdoctoral fellow in the Computer Science Department at Carnegie
Mellon. Subsequently, I worked as a computer vision scientist for General Electric Research,
developing image analysis algorithms for interactive segmentation with active learning for
robust tumor segmentation and techniques for multiple modality segmentation of hard-to-
detect metastatic breast, kidney, and skin cancers.