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The 20th edition of ACAT will bring together experts to explore and confront the boundaries of computing, automated data analysis, and theoretical calculation technologies, in particle and nuclear physics, astronomy and astrophysics, cosmology, accelerator science and beyond. ACAT provides a unique forum where these disciplines overlap with computer science, allowing for the exchange of ideas and the discussion of cutting-edge computing, data analysis and theoretical calculation technologies in fundamental physics research. ACAT promises to showcase an excellent set of plenary speakers, including (as highlight) Joseph Lykken (Fermilab, on Quantum Computing), Lenka Zdeborova (EPFL, on a Theory of Deep Learning), Barry C. Sanders (University of Calgary, on Quantum Machine Learning), Michael Spannowsky (Durham University, on Unsupervised Machine Learning for New Physics Searches), and Julia Fitzner (WHO, on WHO's Data Analysis Challenges during COVID-19 Pandemic). There is a fundamental shift occurring in how computing is used in research in general and data analysis in particular. The abundance of inexpensive, powerful, easy to use computing power in the form of CPUs, GPUs, FPGAs, etc., has changed the role of computing in physics research over the last decade. The rise of new techniques, like deep learning, means the changes promise to keep coming. Even more revolutionary approaches, such as Quantum Computing, are now closer to becoming a reality. To complement the plenary program, we invite you to submit abstracts for parallel talks and posters. The poster sessions will be provided through a virtual platform, allowing for a more convenient and exciting conference experience. Please join us to explore these future changes, and learn about new algorithms and ideas and trends in scientific computing physics. Most of all, join us for the discussions and the sharing of expertise in the field.