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College of Engineering faculty receive prestigious National Science Foundation CAREER Award

Boise State University and the College of Engineering are proud to announce that two faculty members have received this year’s National Science Foundation’s Faculty Early Career Development Program (CAREER) Award, the organization’s most prestigious grant in support of early-career faculty.

Assistant professors Casey Kennington and Mahmood Mamivand, this year’s recipients, each earned grants of approximately $500,000 over five years to advance their career trajectory and potential impact of outstanding early-career faculty. Since the college’s inception in 1997, our faculty have earned a commanding 24 awards, doubling the total of awards in the last five years.

“It is gratifying to see the exceptional work of our early-career faculty being recognized by this prestigious award,” College of Engineering Dean JoAnn S. Lighty said. “These awards, and the others in the past, show the strength of our research contributions and solidify the positive trajectory of our college as a research institution.”

Research and teaching integration is essential to the College of Engineering. The awards highlight the college’s commitment to faculty and students, further leading to funded projects frequently providing cutting-edge opportunities for student researchers as well.

US News and World Report’s 2022 rankings of graduate programs and engineering colleges ranked the Boise State College of Engineering 131 out of 213 schools. In 2016, the College of Engineering was not ranked in graduate education as the university received its first Carnegie research designation. This year marks the 25th anniversary of the college and these rankings reflect the recognition of its quality in research, a reflection of its growth, and a representation of the unshakable focus on learning.

Casey Kennington, Computer Science

Casey Kennington
Graphic by Michele Armstrong

“Integrating Interaction, Embodiment, and Emotion to Transform Language Models”

How might the way children learn language and the ability to speak inform and improve how we engage with robots and other automated systems?

Kennington’s research examines a novel approach for computer systems to learn spoken language that will advance how people and systems communicate. The research will improve language modeling in natural language processing by taking inspiration from how children learn language: they interact with others to learn words that denote physical entities and events, and, like humans, often respond emotionally and embody how they feel in their behavior, whereas researchers currently largely train language models only on static text. The research team will use two robotic platforms for the research and significantly enrich model efficacy and will add knowledge of emotion by modeling it based on human perceptions of robot behaviors. The team will add embodied knowledge by grounding into vision and robot states, and finally, the team will train and evaluate a robot that uses the language model as it interacts with humans to learn language from them. The study will also result in two important datasets: robot behaviors with accompanying descriptions of those behaviors and emotion labels, and longitudinal data of robots interacting and learning language from humans. The objectives are to (1) model emotion: test, and refine through interaction, (2) develop a unified language model, and (3) engage people and robots that learn language with emotional content.

Integrated outreach activities will impact Idaho students and improve diversity in science, technology, engineering, and math targeting underrepresented groups. Hands-on exposure to artificial intelligence and robots can contribute to workforce development in an underserved region, providing students with opportunities to learn to write code and gain an understanding of technology capabilities and limitations.

Mahmood Mamivand, Mechanical and Biomedical Engineering

Mahmood Mamivand
Graphic by Michele Armstrong

“Advancing nanostructure and interface science for permanent magnets without rare earth metals”

Can we develop a viable and sustainable alternative to the rare-earth magnets sourced almost entirely outside the U.S. that we need for our phones, wind turbines, and electric vehicles?

Mamivand’s research aims to advance the fundamental understanding of mechanisms underlying nanostructure formation in multicomponent permanent magnet alloys during magnetic-field-assisted manufacturing. This knowledge paves the path toward developing a novel permanent magnet composed of earth-abundant elements that can outperform state-of-the-art permanent magnets at high temperatures. Current high-temperature permanent magnets, used in electric vehicles and wind power production industries, are based on rare earth elements. The U.S. produces a small fraction globally of industrial rare-earth elements like neodymium and dysprosium. Therefore, developing alternatives to their use can reduce U.S. dependence on these materials and have a positive impact on U.S. national economic and energy security.

This award also supports a unique educational activity for Idaho high school and college students to gain hands-on experience on machine learning through a novel educational curriculum and involvement in authentic machine learning projects together. The project will also provide professional training for high school teachers. The education plan of the project addresses both national and regional workforce shortages in the areas of science, technology, engineering, and mathematics that primarily originate from low entrance and retention rates, particularly for underserved students.

-By Jamie Fink