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.
ACAT2021 will be about the frontiers and limits of AI and explore how we can develop sustainable techniques that can be widely used on a diverse range of computing architectures, in particular to exploit co-processors and High Performance Computing (HPC) facilities, to optimise the processing and analysis of scientific data.
As often has happened in the development of AI, theoretical advances in the domain have opened the door to exceptional perspectives of application in the most diverse fields of science, business and society at large. Today the introduction of Machine (Deep) Learning is no exception, and beyond the hype we can already see that this new family of techniques and approaches may usher a real revolution in various fields.
However, as it has happened time and again in the past, we start realizing that one of the limitations of Deep Learning is sheer computing power. While these techniques allow to tackle extremely complex problems on very large amount of data, the computational cost, particularly of the training phase, is rising fast. The introduction of meta-optimizations such as hyper-parameter scans may further enlarge the possibilities of Machine Learning, but this in turn will require substantial improvements in code performance.
At the same time, HPC is also in full evolution, offering new solutions and perspectives, both in hardware and in software. In this version of ACAT we would like to focus on how this renewed momentum in the HPC world may provide the necessary power to fulfill the revolutionary promises offered by recent breakthroughs in AI at large and in Machine Learning in particular.
Fierce competitions between major players such as Google and IBM signals the advent of quantum computing. Research is being actively pushed to apply quantum computing tools to solve the problem which would have been too difficult to obtain the outcome using conventional methods. Are we seeing quantum supremacy? If yes, then in how many layers of sophistication?
You can attend the workshop either via the virtual environment or via the local meeting (pending the government regulation). The local gethering will take place at IBS Science Culter Center (English language button at top right) in Daejeon, Republic of Korea (South Korea).
Daejeon is a business city located at the center of South Korea, and it is a seat of a South Korean government complex. Its history goes back to thousands of years ago, when prehistoric people settled down near water resources around the region. The city is full of major scientific institutes and universities, reflecting the spirit of 1993 Daejeon Expo.
Transportation: Daejeon is connected from/to Incheon Airport by airport limousine and train.
Weather: Average 8°C/-3°C at the end of November or in the beginning of December.
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Many people are working together to bring you this conference! The organization page has some details. David Britton is the chair of the International Advisory Committee and Axel Naumann is the chair of the Scientific Program Committee. Soonwook Hwang and Doris Kim are the co-chairs of the Local Organizing Committee.