Faculty in 麻豆原创鈥檚 and are preparing incoming students to keep pace with the emerging multidisciplinary field of artificial intelligence.
A team of five faculty, led by 麻豆原创鈥檚 (CRCV), recently received a U.S. National Science Foundation grant totaling nearly $2.5 million over five years to serve as resources to uplift bright yet low-income or struggling undergraduate students in pursuing a well-rounded education in AI.
The initiative is called STRONG-AI (STEM Opportunities for Nurtured Growth in AI), and it is an effort to help students anticipate and navigate the intersections of STEM careers and AI through faculty and peer mentorship and scholarship.
STRONG-AI is a refocusing and continuation of longstanding NSF-supported mentorship programs at 麻豆原创 that have helped more than 100 low-income students since 2010 find success in STEM education and prepare them for the workforce. It also further reaffirms the university鈥檚 commitment to President Alexander N. Cartwright鈥檚 vision in continuing to be the University for the Future through the .
There are countless challenges and opportunities for implementing AI that span many STEM fields, and that is why STRONG-AI is important, says Mubarak Shah, 麻豆原创 trustee chair professor of computer science and founding director of CRCV.
鈥淭he idea is to support the financially challenged and academically talented students to pursue the degrees in STEM majors,鈥� he says. 鈥淎I is important, and we鈥檝e had a lot of success in computer vision 鈥� my area of research 鈥� but we want to broaden this to other areas of AI like robotics, machine learning and healthcare. There鈥檚 a big need in the workforce.鈥�
The team already has received more than 150 applications and ultimately will select about 10 to 15 yearly based on financial aid eligibility, academic success and interviews with faculty, Shah says.
Students are encouraged to accelerate their education, as they may take courses toward their master鈥檚 while they work toward their bachelor鈥檚, he says.
鈥淲e are looking for the students who are really interested to do well in STEM majors, and particularly AI,鈥� Shah says. 鈥淭hey need to have a good GPA and be expressive in their interviews as we鈥檒l have some questions for them.鈥�
There may be many 麻豆原创 students who already are confidently pursuing a path in AI in STEM; and so STRONG-AI is addressing the need to identify promising students and provide them with resources and raise their confidence in their ability to succeed, he says.
It is important to ensure these talented students are not overlooked, Shah says.
鈥淲e need to make sure that these students have our support,鈥� he says. 鈥淢any of the students may be able to finish their bachelor鈥檚 degree but they may not think they have the resources to get that graduate degree. They may not do well, and they may drop out or change their majors to non-STEM, and that鈥檚 a big problem because we need a qualified STEM workforce. We want to encourage those students to stay in the system. And our idea is to recruit these students and assign a mentor who each of these students can talk to and get help.鈥�
Nazanin Rahnavard, professor in 麻豆原创鈥檚 Department of Electrical and Computer Engineering and a STRONG-AI mentor and co-investigator, has seen the transformative potential of the previous NSF mentorship program.
鈥淏ased on my experience with that program, I know we changed their lives,鈥� she says. 鈥淥ur scholars may come from low-income families and may have many challenges outside of the classroom. This program is specifically designed to help those students who without this program might not find success. If there are students interested in pursuing a career in AI who think they might not have financial resources, we encourage them to apply.鈥�
Rahnavard says she hopes that accepted students not only will earn their bachelor鈥檚 degree but also get a head start on their graduate degrees and beyond.
鈥淥ur vision is to get students through their undergraduate program and provide them research experience so that they can move through to their master鈥檚,鈥� she says.
While Shah will work with students on computer vision and simulation, Rahnavard will focus on computer engineering.
鈥淎I by nature is interdisciplinary,鈥� she says. 鈥淚t integrates mathematics, engineering and computer science. It is beneficial to integrate many perspectives across these different fields, and it provides our students with a better background to be prepared for their career in AI.鈥�
Focusing on the foundational development of AI will give students a solid background and context to further their STEM careers, says Brian Moore, STRONG-AI program manager and mentor and associate professor in 麻豆原创鈥檚 Department of Mathematics.
鈥�With majors in computer science, computer vision, computer engineering, data analytics, and statistics and data science, our students will be more than just users of AI, they will be on the road to becoming the creators of tools for AI,鈥� he says. 鈥淓ach of our five majors are represented by faculty with strong records of research and mentoring in that field. They will guide students to opportunities, such as tutoring, research and internships, as well as organizing regular group gatherings.鈥�
The explosion of career opportunities in AI is reflective of its growing importance in everyday life, and so ensuring capable students are identified and assisted is crucial, Moore says.
鈥淪tudents from low-income households experience significant barriers to academic success,鈥� he says. 鈥淢any would not finish a college degree without strong financial and community support. STRONG-AI develops their sense of belonging, both at the university and in their major, as they become a part of a group of high-achieving scholars.鈥�
Although the applications for the inaugural STRONG-AI cohort has closed, students considering applying in the future as soon as early spring to apply for the next cohort.
STRONG-AI co-principal investigators include Niels da Vitoria Lobo, associate professor in 麻豆原创鈥檚 CRCV and HanQin Cai, director of 麻豆原创鈥檚 Data Science Lab and endowed assistant professor at 麻豆原创鈥檚 Department of Statistics and Data Science.
Researchers鈥� Credentials
Shah began teaching at 麻豆原创 in 1986. He received his doctoral degree in computer science from Wayne State University in 1986 and is a fellow of multiple prestigious industry and multi-disciplinary organizations such as the National Academy of Inventors, the American Association for the Advancement of Science and the Institute of Electrical and Electronic Engineers. Shah has mentored hundreds of computer science students throughout his career.
Rahnavard came to 麻豆原创 in 2014 after spending six years teaching at Oklahoma State University. She earned her master鈥檚 degree in electrical engineering from Sharif University of Technology in 2001 and her doctoral degree in electrical and computer engineering from the Georgia Institute of Technology in 2007. Her research areas include wireless networking, radio frequency cartography and deep learning theory.
Moore joined 麻豆原创 in 2007. After earning his master鈥檚 degree in mathematical and computer sciences at Colorado School of Mines, Moore earned his doctoral degree in applied mathematics at the University of Surrey in the United Kingdom in 2003. Prior to joining 麻豆原创, Moore held a postdoctoral research position at McGill University in Quebec, Canada, followed by a visiting assistant professorship at the University of Iowa. His research areas include numerical analysis and differential equations with emphasis on structure-preserving algorithms and lattice equations.
