The Narrative

From Digital Craftsman to Solution Architect

This Grand Challenge is rooted in Dr. Weaver's career-long history of building bridges between the physical and digital worlds. In the 1980s and 90s, making a computer talk to an experiment was a high-Gothic art form, requiring deep expertise in esoteric programming languages. As a graduate student and later a defense contractor, Dr. Weaver developed comprehensive software systems that served as the central information frameworks for cutting-edge research. These systems integrated the energy (lasers, electronics) and material (sensors, samples) components developed by his colleagues, providing the crucial information architecture that turned disparate parts into a functioning whole.

Today, we face a new challenge: a "gap" has formed between the generation of "digital craftsmen" who understood the messy physics of data acquisition and the new generation of "app-native" scientists who are brilliant at data analysis but have little experience with the underlying hardware.

To bridge this gap, we will leverage the JPL Stack, a powerful trinity of modern technologies:

• LabVIEW: For orchestrating the complex, real-time world of physical hardware and sensors.

• Python: The global engine of data science and artificial intelligence, for sophisticated analysis.

• JavaScript: The universal language for creating interactive, accessible user interfaces and dashboards.

By teaching students to architect systems that integrate these "best-of-breed" tools, we are not just teaching them software platforms. We are training them to become the true Solution Architects of the future—leaders who can build the bridge between the world of bits and the world of atoms.

For the purposes of our Grand Challenge, we define "agriculture" in its broadest sense: the active production of useful plants or animals in ecosystems that have been created by people. This holistic view means our work in precision agriculture is equally applicable to optimizing soil conditions for crops as it is to maintaining water quality for aquatic research models. Both are controlled biological systems that benefit from advanced sensing and data integration. 

Introducing SHIFU