Introduction: Congenital diaphragmatic hernia (CDH) is an embryological defect of the diaphragm that typically presents in the neonatal period with respiratory distress.  However, delayed presentations do occur rarely and can pose diagnostic and therapeutic challenges.

Case Report: We describe the case of a 9-month-old male who presented to the emergency department with respiratory distress and was subsequently diagnosed with congenital diaphragmatic hernia.

Conclusion: This case underscores the importance of considering CDH in the differential diagnosis of pediatric patients presenting to the Emergency Department (ED) with unexplained respiratory or gastrointestinal symptoms, even beyond the neonatal period.

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.