Artificial Intelligence for Science : A Deep Learning Revolution

This unique collection introduces AI, Machine Learning (ML), and deep neural network technologies leading to scientific discovery from the datasets generated both by supercomputer simulation and by modern experimental facilities.

Huge quantities of experimental data come from many sources - telescopes, satellites, gene sequencers, accelerators, and electron microscopes, including international facilities such as the Large Hadron Collider (LHC) at CERN in Geneva and the ITER Tokamak in France. These sources generate many petabytes moving to exabytes of data per year. Extracting scientific insights from these data is a major challenge for scientists, for whom the latest AI developments will be essential.

The timely handbook benefits professionals, researchers, academics, and students in all fields of science and engineering as well as AI, ML, and neural networks. Further, the vision evident in this book inspires all those who influence or are influenced by scientific progress.

Dr Alok Choudhary is a professor, researcher and an entrepreneur. He is the Henry and Isabel Dever chaired Professor of Electrical and Computer Engineering, professor of Computer Science and also teaches at Kellogg School of Management at Northwestern University. Dr Alok Choudhary is also the founder, chairman and chief scientist of 4C insights, a data science software company. 4C was acquired by <a href="https://www.mediaocean.com/" target="blank">MediaOcean</a> in 2020.

Dr Alok Choudhary has <a href="http://cucis.ece.northwestern.edu/publications/" target="blank">published</a> more than 450 <a href="http://cucis.ece.northwestern.edu/publications/" target="blank">papers</a> in various journals and international conferences and has graduated 40+ PhD students, including 10+ women PhDs. He has given keynotes in almost all major international conferences in his fields, and given more than 100 invited talks at conferences, businesses and universities.

Geoffrey Fox received a PhD in Theoretical Physics from Cambridge University, where he was Senior Wrangler. He is now a Professor in the Biocomplexity Institute & Initiative and Computer Science Department at the University of Virginia. He previously held positions at Caltech, Syracuse University, Florida State University, and Indiana University. after being a postdoc at the Institute for Advanced Study at Princeton, Lawrence Berkeley Laboratory, and Peterhouse College Cambridge. He has supervised the PhD of 75 students. He has an hindex of 85 with over 41,000 citations. He received the High-Performance Parallel and Distributed Computing (HPDC) Achievement Award and the ACM – IEEE CS Ken Kennedy Award for Foundational contributions to parallel computing in 2019. He is a Fellow of APS (Physics) and ACM (Computing) and works on the interdisciplinary interface between computing and applications. He is currently active in the Industry consortium MLCommons/MLPerf.

Tony Hey is a Fellow of the Royal Academy of Engineering, the Association for Computing Machinery, and the American Association for the Advancement of Science. At the University of Southampton in the UK, his parallel computing research group designed and built one of the first distributed memory message-passing computers using innovative Inmos transputers. He was later Head of the Electronics and Computer Science Department at Southampton and also Dean of Engineering. In 2005 he was awarded a CBE for Services to Science after leading the UK's eScience initiative.

After 10 years as Corporate Vice President for Technical Computing in Microsoft in the US, he returned to the UK and has been Chief Data Scientist at STFC's Rutherford Appleton Laboratory since 2015. He was one of the originators of the MPI message passing standard in 1992 and was awarded the 2019 Lifetime Achievement Award by the International Open Benchmark Council. In 2020 he chaired a US Department of Energy subcommittee that explored 'the opportunities and challenges from Artificial Intelligence and Machine Learning for the advancement of science and technology' or, as a shorthand, 'AI for Science'.