According to the World Health Organization (WHO), cancer is the second leading cause of death globally, and was responsible for an estimated 9.6 million deaths in 2018. Approximately 70% of deaths from cancer occur in low- and middle-income countries (LMIC), in large part due to lack of proper access to screening, diagnosis and treatment services. As the economic impact of cancer increases, disparities in diagnosis and treatment options prevail. Recent advances in the field of machine learning have bolstered excitement for the application of assistive technologies in the medical domain, with the promise of improved care for patients. Unfortunately, cancer care in LMIC faces a very different set of challenges, unless focused efforts are made to overcome these challenges, cancer care in these countries will be largely unaffected. The purpose of this workshop is to bring together experts in machine learning and clinical cancer care to facilitate discussions regarding challenges in cancer care and opportunities for AI to make an impact. In particular, there is an immense potential for novel representation learning approaches to learn from different data modalities such as pathology, genomics, and radiology. Studying these approaches have the potential to significantly improve survival outcomes and improve the lives of millions of people.