SPUR 2018 Projects: Engineering

Materials Science & Engineering | College of Engineering

Balancing performance and sustainability in new materials discovery via machine learning
Taylor Sparks, Assistant Professor

Many of the most exciting new developments in engineering and technology can be traced back to the discovery of new materials which exhibit exciting new properties. However, one of the main challenges in discovering new materials is that the process has traditionally been a high-risk, low-reward endeavor with low odds of identifying a new material with exciting new properties. In fact, many of the most exciting new materials from high temperature superconductors to teflon fluoropolymers have been discovered by "accident" by fortuity and chance. Moreover, even when materials are designed for a given application little thought is given to economic, environmental, and other sustainability factors that may limit the eventual deployment of new materials. For example, rare-earth laden magnets, noble metals in catalysts, or toxic heavy-metals in solar cells and batteries. Going forward, a fundamentally new approach must be utilized that will enable the discovery of new materials at a radically increased pace by reducing the risk associated with exploring chemical "whitespace." One exciting new field, which the PI is a pioneer in, is using machine learning techniques to predict material performance before synthesis ever occurs. The advent of big data in materials science and other science fields has put machine learning in a position to provide exciting new developments that far outpace traditional one-at-a-time synthetic approaches. However, at the heart of machine learning is gathering, curating, and preparing data to be utilized as training datasets in machine learning algorithms. There is an enormous amount of work to be done in collecting this data especially with an eye towards sustainability metrics.

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School of Computing | College of Engineering

Automated Dungeon Mastering
Rogelio Cardona-Rivera, Assistant Professor

This project targets the development of an artificial intelligence (AI) agent capable of running (in whole or in part) a tabletop role-playing game (i.e. serving as a "dungeon master"). The development of such an agent is said to be a "grand challenge" of the interactive storytelling research community. The student(s) will develop an AI agent that automates a lot of the necessary minutiae that happens during a session of the role-playing game Dungeons & Dragons (D&D). In D&D, the dungeon master (DM):

"is the game organizer and participant in charge of creating the details and challenges of a given adventure, while maintaining a realistic continuity of events. In effect, the Dungeon Master controls all aspects of the game, except for the actions of the player characters (PCs), and describes to the players what they see and hear."

Storytelling is increasingly relied upon in technology applications to entertain, educate, and engage our society in more compelling ways. This project will explore technology that may enable the automatic production of on-demand, user-specific narrative tailored to a person's specific background and information needs. The research will further advance society by serving as a technological foundation for novel systems leveraging narrative in contexts where we already see the use of manually/semi-manually created narratives in virtual environments, such as training and learning, entertainment, rehabilitation therapy, intelligence analysis, cognitive intervention for aging populations, automated news generation, and health care communication.

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