Research Topic

Machine Learning at Scale: How Big Data and AI are Transforming Engineering

About this Research Topic

The availability of massive volumes and different sources of data coupled with rapid recent advancements in machine learning have begun to unleash new possibilities in artificial intelligence. The explosion of big data is fueled, among others, by the proliferation of sensors measuring an abundance of different parameters, as well as the Internet of Things, which extends Internet connectivity to a diverse range of devices and everyday objects that utilize embedded technology to communicate and interact with the external environment, and the Internet of Everything, which ultimately brings together people, process, data, and things. Advancements in parallel and distributed computing, such as MapReduce, create new capabilities for managing and processing big data sets, while recent developments in machine learning, such as deep learning and recurrent neural networks, provide amazing opportunities in understanding and learning realationships and trends in these massive data sets. For example, deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction thus implicitly capturing intricate structures of large‐scale data reaching previously unobtainable levels of accuracy.

These promising advances have already started bringing about significant development to many areas of interest to the engineering community. The proliferation of data, combined with effective means to obtain, store, manage and analyze massive volumes of data with agility and speed at scale, is driving innovation and appears to be one of the key disruptive enablers in engineering in the coming decade, playing a key role in (or even shaping in some cases) the design of materials, products, and systems ranging from tech, heavy equipment and energy industry to avionics, geospatial technology and healthcare.

The purpose of this Research Topic is to provide a forum for engineers, data scientists, researchers and practitioners to present new academic research and industrial development on big data and machine learning for engineering applications. The Research Topic aims at original research papers in the field, covering new theories, algorithms, systems, as well as new implementations and applications incorporating state-of-the-art machine learning techniques. Review articles and works on performance evaluation and benchmark datasets are also solicited.
Potential topics of interest include but are not limited to the following:
• Novel machine learning algorithms for large volumes of data
• Multimodal data fusion techniques
• Innovative hardware and network architecture for machine learning from big data
• Data mining and management methods
• Analysis, modeling and visualization
• Big data analytics
Indicative domains of application of interest to the Research Topic include:
• Computer vision, language understanding, speech and video analysis, robotics and automation
• Electrical and mechanical engineering, production management and optimization, manufacturing, fail-ure detection, energy management, smart grid
• Civil engineering, construction management and optimization, structural health monitoring, earth-quake engineering, urban planning
• Transportation, hydraulics, water power and environmental engineering
• Surveying and geospatial engineering, remote sensing and geosciences
• Biomedical engineering
• Materials science and engineering


Keywords: Machine learning, big data, engineering applications, artificial intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The availability of massive volumes and different sources of data coupled with rapid recent advancements in machine learning have begun to unleash new possibilities in artificial intelligence. The explosion of big data is fueled, among others, by the proliferation of sensors measuring an abundance of different parameters, as well as the Internet of Things, which extends Internet connectivity to a diverse range of devices and everyday objects that utilize embedded technology to communicate and interact with the external environment, and the Internet of Everything, which ultimately brings together people, process, data, and things. Advancements in parallel and distributed computing, such as MapReduce, create new capabilities for managing and processing big data sets, while recent developments in machine learning, such as deep learning and recurrent neural networks, provide amazing opportunities in understanding and learning realationships and trends in these massive data sets. For example, deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels of abstraction thus implicitly capturing intricate structures of large‐scale data reaching previously unobtainable levels of accuracy.

These promising advances have already started bringing about significant development to many areas of interest to the engineering community. The proliferation of data, combined with effective means to obtain, store, manage and analyze massive volumes of data with agility and speed at scale, is driving innovation and appears to be one of the key disruptive enablers in engineering in the coming decade, playing a key role in (or even shaping in some cases) the design of materials, products, and systems ranging from tech, heavy equipment and energy industry to avionics, geospatial technology and healthcare.

The purpose of this Research Topic is to provide a forum for engineers, data scientists, researchers and practitioners to present new academic research and industrial development on big data and machine learning for engineering applications. The Research Topic aims at original research papers in the field, covering new theories, algorithms, systems, as well as new implementations and applications incorporating state-of-the-art machine learning techniques. Review articles and works on performance evaluation and benchmark datasets are also solicited.
Potential topics of interest include but are not limited to the following:
• Novel machine learning algorithms for large volumes of data
• Multimodal data fusion techniques
• Innovative hardware and network architecture for machine learning from big data
• Data mining and management methods
• Analysis, modeling and visualization
• Big data analytics
Indicative domains of application of interest to the Research Topic include:
• Computer vision, language understanding, speech and video analysis, robotics and automation
• Electrical and mechanical engineering, production management and optimization, manufacturing, fail-ure detection, energy management, smart grid
• Civil engineering, construction management and optimization, structural health monitoring, earth-quake engineering, urban planning
• Transportation, hydraulics, water power and environmental engineering
• Surveying and geospatial engineering, remote sensing and geosciences
• Biomedical engineering
• Materials science and engineering


Keywords: Machine learning, big data, engineering applications, artificial intelligence


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

30 April 2018 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

30 April 2018 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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