Department of Machine Learning
Artificial intelligence and machine learning algorithms have witnessed tremendous growth as powerful data analytics technologies, however, despite the potentials their role in medicine and oncology has been underwhelming.
In partnership with the research and clinical teams at Moffitt Cancer Center, this pioneering department will leverage the rich oncology data resources to develop new technologies to accelerate cancer discovery and improve clinical care.
The department focuses on machine/deep learning (ML/DL) application in information retrieval and annotation with natural language processing (NLP), outcomes and decision-making research, ML-aided digital pathology, ML-guided translational oncology (drug discovery and repurposing), among other areas.
Mission:
To design, develop, and translate state-of-the-art patient-centered machine and deep learning algorithms for oncology.
Vision:
To transform personalized cancer care and accelerate scientific discovery in cancer research with machine/deep learning
Values:
Patient-centered machine/deep learning for cancer care and research
Unbiased, generalizable, and interpretable machine/deep learning algorithms
Translate machine/deep learning findings into the clinic to improve cancer care and research
Faculty
Issam El Naqa, PhD - Chair/Senior Member
Dr. El Naqa is a renowned leader in the field of data science with formal training in electrical engineering, computer science, biology and medical physics. Dr. El Naqa’s research focuses on developing large-scale data mining methods to identify biomarkers of response to chemoradiotherapy, multimodality image-guided targeted and adaptive radiotherapy, and radiobiology-based treatment outcome modeling.
Joseph (Ross) Mitchell, PhD - AI Officer, Senior Member
Dr. Mitchell drives the development of digital tools and technologies that leverage artificial intelligence (AI) to improve the efficiency and/or quality of cancer care delivery at Moffitt. He is responsible for identifying clinical and business use cases for predictive analytics using machine learning and other advanced technologies, overseeing the development of algorithms, and partnering with clinical, business and technology leaders to deploy AI-enabled tools around the center.
Yoga Balagurunathan, PhD - Assistant Member
Dr. Balagurunathan’s research is focused on understanding the physiology of the tumor and its relationship to the underlying genome. His interests include data integration from various modalities (radiology, pathology, genome) to improve clinical decision support, His diseasefoci are prostate cancer, lung cancer and B-cell lymphomas.
