Adnan Mehonic brings expert knowledge, editorial excellence to new gold open access journal
AIP Publishing is proud to announce APL Machine Learning, a new open access scientific journal — and the appointment of its founding editor-in-chief, Dr. Adnan Mehonic.
APL Machine Learning will feature research addressing how machine learning (ML) and artificial intelligence (AI) can aid physicists, material scientists, engineers, chemists, and biologists in advancing scientific discovery — and how insights from these disciplines can pave the way for development of better AI systems.
Adnan Mehonic, an assistant professor and Royal Academy of Engineering Research fellow at the University College London (UCL), will lead the journal as its editor-in-chief when it begins accepting submissions in mid-2022 with a target to publish in January 2023.
“AIP Publishing is honored to bring Adnan Mehonic on board as the first Editor to lead APL Machine Learning,” said Dr. Penelope Lewis, Chief Publishing Officer for AIP Publishing. “We share a vision of a deeply influential journal on the leading edge of a diverse and growing research community. We are excited to work with Dr. Mehonic to realize that vision by publishing the most groundbreaking and innovative research this emerging field has to offer.”
Mehonic brings a wealth of knowledge in machine learning and novel hardware technologies for AI. He has authored more than 100 journal publications and international conference proceedings on memristive materials and devices as well as neuromorphic and energy-efficient AI technologies. He is a recipient of multiple awards, including the MIT Technology Review’s “35 Innovators under 35,” Wiley’s Advanced Science “Rising Star,” and “One to Watch” award for UCL’s most innovative and entrepreneurial staff.
“There are two natural ways to connect machine learning, or more broadly, AI and applied physical science,” Mehonic said. “The first involves applying ML techniques to physical systems to accelerate scientific discovery, leading to new insights in materials science, engineering, chemistry, biology, and applied physics. The second is to use domain knowledge from these disciplines to develop more efficient and more functional AI systems. I am thrilled to lead APL Machine Learning, which will capture both approaches to serve researchers working in all facets of AI and ML.”
Mehonic will build a diverse, highly motivated, and experienced editorial team with complementing expertise to establish APL Machine Learning as a leading journal that will provide a unique platform for disseminating vibrant and timely research. The journal will waive article processing charges (APCs) in the first year of publication.
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