Machine learning advances are used in self-driving cars, speech recognition systems, and translation software. However, the COVID-19 pandemic has highlighted the urgency of translating such advances to the domain of biomedicine. Such a pivot requires new machine learning methods to build long-term vaccines and therapeutic strategies, predict immune avoidance, and better repurpose small molecules as drugs.The ICML Workshop on Computational Biology (WCB) will highlight how machine learning approaches can be tailored to making both translational and basic scientific discoveries with biological data. Practitioners at the intersection of computation, machine learning, and biology are in a unique position to frame problems in biomedicine, from drug discovery to vaccination risk scores, and WCB will showcase such recent research. Commodity lab techniques lead to the proliferation of large complex datasets and require new methods to interpret these collections of high-dimensional biological data, such as genetic sequences, cellular features or protein structures and imaging datasets. These data can be used to make new predictions towards clinical response, uncover new biology, or aid in drug discovery.This workshop aims to bring together interdisciplinary machine learning researchers working in areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community. We especially encourage interdisciplinary submissions that might not neatly fit into one of these categories.