  Driven by the recent advances in smart, miniaturized, and mass produced
sensors, networked systems, and high-speed data communication and computing,
the ability to collect and process larger volumes of higher veracity real-time
data from a variety of modalities is expanding. However, despite research
thrusts explored since the late 1990's, to date no standard, generalizable
solutions have emerged for effectively integrating and processing multimodal
data, and consequently practitioners across a wide variety of disciplines must
still follow a trial-and-error process to identify the optimum procedure for
each individual application and data sources. A deeper understanding of the
utility and capabilities (as well as the shortcomings and challenges) of
existing multimodal data fusion methods as a function of data and challenge
characteristics has the potential to deliver better data analysis tools across
all sectors, therein enabling more efficient and effective automated
manufacturing, patient care, infrastructure maintenance, environmental
understanding, transportation networks, energy systems, etc. There is therefore
an urgent need to identify the underlying patterns that can be used to
determine a priori which techniques will be most useful for any specific
dataset or application. This next stage of understanding and discovery (i.e.,
the development of generalized solutions) can only be achieved via a high level
cross-disciplinary aggregation of learnings, and this workshop was proposed at
an opportune time as many domains have already started exploring use of
multimodal data fusion techniques in a wide range of application-specific
contexts.
