The Promising Reality of Artificial Intelligence

UTSA’s AI Consortium harnesses the power of artificial intelligence to solve real-world problems

Technological advances in computing capability, access to large datasets and innovative algorithms have increased the impact of artificial intelligence (AI) in our daily lives. Recent analyses predict that AI will add approximately $8 trillion in gross value to the U.S. economy by 2035. This technology will drive creative solutions for real-world problems in biomedicine, health care delivery, food insecurity, human trafficking, cybersecurity and transportation. The complexity of these global problems will require a highly prepared workforce capable of managing exponentially expanding volumes of data and utilizing machine learning. MATRIX, the UTSA AI Consortium for human wellbeing, is prepared to address these challenges.

The AI Consortium is an interdisciplinary collaboration that fosters innovative research in AI. The MATRIX brings students, practitioners and researchers from multiple institutions under one umbrella, utilizing their unique skillsets to address emerging research challenges. Dr. Dhireesha Kudithipudi ’06 is the director of MATRIX. She is a professor of electrical and computer engineering and computer science, and the Robert F. McDermott Chair in Engineering. Her research expertise is in neurally inspired AI algorithms, AI accelerators, energy-efficient machine learning and novel computing substrates.

“The goal of the AI initiative at UTSA is to strategically collaborate and engage with the private sector, academia, the Greater San Antonio community and key international partners to advance the state of the art with transdisciplinary solutions,” Kudithipudi says. “UTSA has been carving a niche in this space with strategic cluster hires over the past few years. The university and its partners already have a strong presence in neuroscience, brain health, cybersecurity and applied domains. We are building a new sandbox for research teaming that builds on these strengths.”

MATRIX is composed of external research partners and internal UTSA members specializing in computer science, electrical and computer engineering, cybersecurity, biomedical engineering, neuroscience, geoscience, medicine, and psychology. Dr. Sushil Prasad, professor and chair of the Department of Computer Science and a core member in MATRIX, agrees that the consortium is uniquely positioned to address emerging problems in human-aware AI by leveraging the broad expertise of its members in a collaborative framework. “Basic AI algorithms and allied techniques have been around for some time but have become increasingly effective in solving societal problems with the advent of massively parallel computers and accelerators,” Prasad says. “Exploration of advanced AI algorithms, software and hardware techniques and their interplay with big data and advanced cyberinfrastructure will continue. However, opportunities have opened up for deep societal questions to be explored, including how well AI systems can interact with people and how safe, secure and ethical these are. Such questions call for truly multidisciplinary, collaborative teams involving all stakeholders, and AI Consortium is creating such collaborations.”

Christopher Mentzer, assistant director of research and development at the Southwest Research Institute, co-leads the consortium’s Machine Learning and Deployment group alongside assistant professor Dr. Murtuza Jadliwala of UTSA’s Department of Computer Science. “The consortium is important as it is bringing world-renowned experts together from top institutions within San Antonio to share their backgrounds and experience to collaborate and help solve new and challenging problems using AI in a wide variety of fields,” Mentzer says. Other external partners include researchers from UT Health San Antonio and Texas Biomedical Research Institute.

“The MATRIX AI Consortium at UTSA has brought together researchers from multiple colleges at UTSA and institutions in and around the San Antonio area to leverage these advances and collaboratively address some of the most difficult multidisciplinary research challenges in these areas,” adds Jadliwala. “Addressing these multidisciplinary research problems requires significant domain expertise in specific areas and fields, and the confluence of expertise currently available within MATRIX will definitely help with that challenge.”

The consortium facilitates training opportunities through seminars, lab rotations and hackathons that foster innovative transdisciplinary research. The consortium currently has funded research projects from both industry and federal agencies, including the National Science Foundation, National Institute of Health, Air Force Research Laboratory and the Defense Advanced Research Projects Agency.

“I think the consortium’s focus on employing AI and machine learning tools and techniques to address problems that impact human health and well-being is something very unique and noteworthy,” says Jadliwala. “In light of the COVID-19 pandemic, and the continuous threat to human well-being through similar threats, the need for such a data- and AI-centric focus on human health has assumed greater significance.” During the pandemic, the consortium published the COVID-19 Resources & Recovery Site, a website that populated a recovery map with real-time data so the public could assist with locating scarce resources during the pandemic.

“Our COVID-19 tracking maps for Texas and San Antonio took advantage of the web-based Geographic Information System (Web GIS): ArcGIS Online, a spatial data science and visualization tool,” says Dr. Hongjie Xie, professor and chair of the Department of Geological Sciences. “These maps interactively display the number and progression of confirmed infections, fatalities and administered tests by county or zip code in real time. This data provides valuable information for future studies on which factors—including geographic, environmental, demographic, social and economic—would have contributed to such differences spatially and temporally.”