This spring UT San Antonio welcomed its founding dean of the College of AI, Cyber and Computing, Jinjun Xiong, who hopes to lead the college toward collaboration with local industry and discoveries that help meet society’s needs.
Xiong officially joined the university on March 1, leaving his previous role at the State University of New York as an Empire Innovation Professor, a prestigious state-funded role, and director of the Institute for Artificial Intelligence and Data Science at the University at Buffalo.
UT San Antonio’s College of AI, Cyber and Computing was inaugurated in September 2025 and is located in the Downtown Campus’ San Pedro I building. It houses a wide array of degree programs including artificial intelligence, computer engineering, cybersecurity and more.
Xiong joined UT San Antonio just after the merger with UT Health San Antonio, a move that aims to capitalize on the institutions’ research power and which officials hope will lead to broader and stronger collaborations among colleges.
Xiong earned a PhD in computer engineering from the University of California, Los Angeles, and a Master of Science in electrical and computer engineering from the University of Wisconsin–Madison.
He started his career as a research scientist at IBM, where he remained for 15 years, eventually becoming program director and research scientist at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York.
The center serves as the global headquarters for IBM research, and it is where most of the company’s breakthroughs in artificial intelligence, quantum computing and semiconductors have taken place. One of its most well-known developments is IBM’s Watson, the computer that beat the best human Jeopardy! player in a televised contest in February 2011.
During his tenure he cofunded and codirected several centers including the IBM-Illinois Center for Cognitive Computing Systems Research at the University of Illinois Urbana-Champaign, known as UIUC, which also led to the creation of the IBM-Illinois Discovery Accelerator Institute in 2021, a $200 million research investment by IBM, UIUC and the State of Illinois.
Throughout his career, Xiong has focused on solutions-based technology, or what he calls “earthly AI,” a term he coined for his quest to make technology accessible enough for it to become a tool that helps meet society’s earthly needs.
While at IBM he realized that part of creating access included engaging college students to help identify issues, needs and answers. That’s part of what led him to higher education.
Xiong sat down with the San Antonio Report to talk about this next chapter of his career leading UT San Antonio’s new college, which launched in September of 2025. His answers have been edited for brevity and clarity.
Tell us more about “earthly AI” and what led you to adopt this term as a guiding principle of your work?
On the one hand, technology companies are developing technology. Yet in society, there’s so many, what I call “earthly needs” that cannot be met. And I can understand from working in the industry, that the industry is always tracing for a return of investment and to respond to the investors and how they can focus on the sectors that can bring the most profit. But yet in a lot of the society there are needs, especially in the nonprofit sector. These are very [specific] needs and also there’s not many opportunities for making a profit, which means the industry will not have the interest, or pay attention to those needs.
So that’s why I started to think, ‘What would be the way to scale? To make technology more accessible to meet those earthly needs?’
What answers did you find and how?
In 2011 I started to look into the different collaboration model with the university and that’s why I actually built this collaboration with the University of Illinois Urbana-Champaign and the state of Illinois.
I thought that by working with the education sector, in this case universities, we can lower the cost of research, lower the cost of development for the company and yet we can still make some technology scale. That was my initial attempt while I was still working in the industry, working with the university education sector and to make this technology lower cost for development purposes.
Later I found that maybe I should really join the education sector, to really tap into this pool of talents, to make technology more connected to people’s lives. And that’s why I left the industry.
What new opportunities or doors opened in terms of research once you started to include college students?
All my research is non-traditional. I call it user-driven to try to build the solutions to meet societal needs. So we identify research gaps using the PhD students, the master’s degree students, and graduate students, as part of their education and training.
I found that it is a really good recipe in a sense that all my students have told me that they feel like their research is more grounded, that they can see why I’m asking them to do certain research. And they can also see some of their research being used and having impact. So it’s more fulfilling for their journey as well.
And, of course, my industry experience helped me to anchor myself.
What has been the reception to your user-driven, solutions-focused research style with both the education and industry sectors?
I was lucky to have a strong partner in Illinois, who helped me nourish that kind of mindset. And It takes a little bit of time. There’s a lot of faculty and industry colleagues who see this as a good model, but it’s not scalable if I’m the only one in the industry spending a lot of time building relationships. So the question is how can we take it to scale? How can more universities adopt this model?
I’ve been talking about this at many places, trying to get more people excited about the approach and the way to make this more convincing is by doing it right. So in Buffalo, I was able to build the first national AI Institute for Exceptional Education and that is the first national AI Institute in the state of New York — the Institute for Artificial Intelligence and Data Science at the University at Buffalo — funded by the National Science Foundation and the Institute of Education Sciences of the US Department of Education.
So I’ve been pretty open to share my experience and I hope it resonates. I’m trying to do it again, with a similar model. Of course every time you have to fine tune the model based on the local needs and the local dynamics, too. And I just really hope that more and more faculty can buy into this idea and see this is a successful path.
Did you see an opportunity to do more of that by leading a new college here at UT San Antonio?
Definitely, not only the college, I think it’s the whole university. I think that also a very important point is the leadership and its vision. I felt like President Taylor Eighmy and Provost Heather Shipley, and all the people who trusted me to bring me here, they must believe in this model as well.
This younger university has the ambition to grow. And I think everyone feels the energy and wants to do something new and different so that way we can accelerate our growth and also differentiate ourselves from the rest. I feel this is making everyone a little more receptive.
And because we are young in a sense, we do not have a lot of baggage and the people are willing to work with you to build this process.
AI has had an interesting evolution in the college classroom, to say the least, do you think this solutions-focus approach helps soften the perception of it as a tool?
Oh totally. The thing is that in academics there are a lot of institutes doing wonderful AI work, but it is still far from people’s lives. But if I say, we are using AI to help children with disabilities, that’s not an abstract definition, not an abstract claim, right.
We did build the first AI screening tools. We worked with researchers to identify the most effective evidence-based screening practices and we worked with the developers of that methodology. We used AI and automated that whole process called AutoRSR (Automated Redmond Sentence Recall). It is a screening test with 16 sentences that children need to read and based on the children’s repeating of the sentence we can do the scoring. And based on the score we can show you the likelihood that you might need to see a speech pathologist.
This is a very simple kind of screening tool that in the past was labor intensive, but now you can scale it. We used AI to automate it and we put it online for free so that people can use it. Now you can see people can start to see that your AI research is no longer abstract and that you are developing something that people can use.
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