Advanced Computing and Oncology Lab

The Advanced Computing and Oncology Lab, led by physician-scientist, Dr. Tafadzwa L. Chaunzwa, is dedicated to advancing the intersection of clinical oncology and computational sciences, with a primary focus on lung cancer and related thoracic malignancies. With formal training and expertise as an engineer, data scientist, and oncologist, Dr. Chaunzwa brings a uniquely interdisciplinary approach to oncology research. His work leverages artificial intelligence (AI) and machine learning (ML) to analyze multimodal oncology data, uncovering novel insights into cancer pathogenesis, treatment response, toxicity, and prognosis, and developing advanced clinical prediction models to improve cancer care.

Housed at the Mortimer B. Zuckerman Research Center at Memorial Sloan Kettering Cancer Center’s main campus in Manhattan, the Advanced Computing and Oncology/Chaunzwa Lab is positioned at the cutting edge of AI and oncology research, with access to unparalleled resources, core facilities, and large-scale datasets at the world’s first and largest private cancer center. Our mission is to pioneer breakthroughs in cancer research and innovation, transforming cutting-edge AI technologies into real-world improvements in patient care.

Lab members at all levels - including undergraduate, medical, and graduate students, clinical residents, and fellows – work alongside data/computer scientists, engineers, postdoctoral fellows, and the principal investigator to collect data, analyze trends, and develop novel AI/ML prediction models for oncology applications. As part of MSK’s robust collaborative ecosystem, we contribute to a range of initiatives in cancer imaging, and advanced oncology data modeling, integrating radiology, digital pathology, genomics, circulating tumor markers, and patient-reported demographic data. We are part of the Imaging and Radiation Sciences Program at MSK and maintain close collaborative ties with world-leading experts in radiation and medical oncology, medical physics, radiology, thoracic surgery, and computational oncology.

By bridging scientific innovation with clinical relevance, our research aims to drive both methodological advancements in AI and scientific computing, as well as meaningful improvements in patient outcomes. The lab is generously supported by institutional and external grants.