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- Ryan Dehoff
- Daniel Jacobson
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- Vincent Paquit
- Viswadeep Lebakula
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- Roger G Miller
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- Sudarsanam Babu
- Taylor Hauser
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- Venkatakrishnan Singanallur Vaidyanathan
- Vipin Kumar
- Vlastimil Kunc
- William Peter
- Xiuling Nie
- Yan-Ru Lin
- Ying Yang
- Yukinori Yamamoto

Mechanism-Based Trait Inference in Plants Using Multiplex Networks, AI Agents, and Translation Tools
This system enables the modular design and optimization of complex plant traits by organizing genes and regulatory mechanisms into interpretable clades.

Mechanism-Based Biological Inference via Multiplex Networks, AI Agents and Cross-Species Translation
This invention provides a platform that uses AI agents and biological networks to uncover and interpret disease-relevant biological mechanisms.

Often there are major challenges in developing diverse and complex human mobility metrics systematically and quickly.

Understanding building height is imperative to the overall study of energy efficiency, population distribution, urban morphologies, emergency response, among others. Currently, existing approaches for modelling building height at scale are hindered by two pervasive issues.

Water heaters and heating, ventilation, and air conditioning (HVAC) systems collectively consume about 58% of home energy use.

High strength, oxidation resistant refractory alloys are difficult to fabricate for commercial use in extreme environments.

In manufacturing parts for industry using traditional molds and dies, about 70 percent to 80 percent of the time it takes to create a part is a result of a relatively slow cooling process.

MAPSTER is a lightweight software package that automatically searches deployed laptops for geospatial data and complies metadata (GPS coordinates, file size, etc) at a central checkpoint.

This technology combines 3D printing and compression molding to produce high-strength, low-porosity composite articles.