During the Covid-19 pandemic, in spite of the impressive advances in machine learning (ML) in recent decades, the successes of this field were modest at best. Much work remains, for both ML and global health (GH) researchers, to deliver true progress in GH This workshop will start a lasting and consistent effort to close the gap between advances in ML, practitioners and policy makers working in public health globally. It will focus on difficult public health problems and relevant ML and statistical methods.We will use this opportunity to bring together researchers from different communities to share new ideas and past experiences. We will facilitate rapid communication of the latest methodological developments in ML to parties who are in positions to use them and establish feedback loops for assessing the applicability and relevance of methods that are available and gaps that exist. It will be a unique opportunity to challenge both research communities and demonstrate important, policy-relevant applications of sophisticated methods at one of the most prestigious annual ML conferences.This will be the first ever ML conference workshop on the topic ``Machine Learning & Global Health'', sponsored by the Machine Learning & Global Health Network (MLGH.NET). By showcasing key applied challenges, along with recent theoretical advances, we hope to foster connections and prompt fruitful discussion. We will invite researchers to submit extended abstracts for contributed talks and posters along the themes of:-What lessons can we learn from the COVID-19 pandemic?-What sorts of questions in GH can ML be useful for? What sorts of questions in GH is ML unlikely to be useful for? -The current limitations in the application of ML to solving GH problems and possible solutions to these limitations.-How can we leverage ML in order to: promote public health worldwide; be proactive against future pandemics; understand and address inequalities in health.