Dynamic high-level languages such as Julia, Maple®, Mathematica®, MATLAB®, Octave, Python/NumPy/SciPy, R, and Scilab are rapidly gaining popularity with computational scientists and engineers. High-level languages offer the advantage of writing legible and expressive code, which facilitate the rapid prototyping of programs for technical computing. However, high-level languages have a reputation for being subperformant and being difficult to deploy scalably on massively parallel architectures such as clusters, cloud servers, and supercomputers. Thus, some scientific developers resort to prototyping in one language and deploying at scale in another, thus incurring the costs associated with reimplementing a scientific code at least twice. This two-language problem is but one example of the technical challenges associated with the use of dynamic languages on massively parallel platforms. This workshop aims to bring together users, developers, and practitioners of dynamic technical computing languages, regardless of language, affiliation or discipline, to discuss topics of common interest. Disciplines affiliated the broad umbrella of computational science and engineering, such as physical sciences, biological sciences, social sciences, digital humanities, mathematics, statistics, computer science, all share common challenges associated with the implementation of computational models in extant programming languages. Examples of such topics include code performance, the use of abstractions for composability and reusability, the two-language problem, best practices for software development and engineering, and the implications of such code design decisions for applications in visualization, information retrieval and big data analytics. We expect that these challenges are common to researchers and programmers in academia, national laboratories and industry.