The Northwestern Network for Collaborative Intelligence announced five strategic leaders in April to develop data science and artificial intelligence education, research and infrastructure.
The move marked the network’s next step in its goal to promote interdisciplinary work within the University’s AI initiatives.
The University launched NNCI last June, announcing McCormick Prof. V.S. Subrahmanian and Feinberg Prof. Abel Kho as the founding co-directors. The network consists of three pillars: Education, Research and Infrastructure.
Medill Prof. Jeremy Gilbert and McCormick Prof. Sara Owsley Sood co-lead the Education Pillar. Feinberg Prof. Scott Budinger and McCormick Prof. Chris Wolverton co-lead the Research Pillar. Joseph Paris, associate vice president of information technology services and support, leads the Infrastructure Pillar.
“We’d like Northwestern to become a world leader in AI + X, for many different Xs,” Subrahmanian said. “We don’t know what those are yet, and that’s part of what we’re trying to isolate.”
NNCI’s AI + X efforts focus on the ways AI can impact and interact with diverse disciplines at the University, which Subrahmanian said is leveraged to increase NU’s competitiveness in this emerging technology.
The strategic leaders said they are currently in the process of learning more about the current AI education and research climate across all fields at NU to inform future initiatives.
Kho said NNCI was created from the Data Science and Artificial Intelligence Steering Committee, with Kho and Abel as co-vice chairs. This initiative, Kho added, was a “presidential priority” of former President Michael Schill.
In the process of choosing the five strategic leaders, Kho said he and Subrahmanian sought to find individuals who “embodied” multidisciplinary work, can engage new parts of the University with NNCI and recognize the “places of excellence.”
“At the end of the day, AI is truly great, but people are what make things really go far,” Kho said.
Education Pillar leaders seek interdisciplinary opportunities for AI learning
Gilbert said NNCI aims to add to existing NU research and education initiatives and developments.
“Hopefully, we can amplify that work and help other people who haven’t started to explore what artificial intelligence might mean for their work,” Gilbert said.
Sood said the Education Pillar must engage three areas of AI education: encouraging students to build AI systems, leveraging AI in diverse disciplines and addressing AI’s impact in the classroom.
The end goal for Sood is to meet the needs of students across the University with shared, outward-facing resources and courses to give them access to opportunities to engage with AI. This requires education offerings to be tailored depending on student needs, Sood said.
“There’s widely varying needs for students in terms of AI, and so (it’s about) making sure that we have a wealth of opportunities so that students can engage at the right level for them,” Sood said.
Gilbert added that offering credit for educational offerings would allow students to demonstrate their AI skill sets to employers.
On top of educating the current NU student body, Gilbert said he hopes that NNCI can also educate the broader NU community, particularly in helping alumni adjust to how AI has shaped the workforce.
“Just because you went to school at Northwestern four years ago or 40 years ago, we shouldn’t say you don’t need our help in learning about AI in the domain in which you work or the domain in which you’re interested,” Gilbert said.
In the next few months, Gilbert said the first steps for him and Sood are to understand the current educational landscape at NU, start building up conversations with alumni and support departments and schools in creating course and program offerings.
Research Pillar leaders aim to increase collaboration among diverse research backgrounds
Wolverton, a professor in the materials science and engineering department, uses computations to understand, predict and design materials. Over the last decade, Wolverton said the emergence and continual improvement of machine learning and AI models has completely shifted his research.
Now, these models, rather than costly calculations, can predict the traits of materials. The speed has raised the bar for discoveries
Budinger’s research in the Feinberg School of Medicine centers around using machine learning and AI tools to develop disease models for patients with lung disease, which help to identify disease and targets for therapy.
Increasing interdisciplinary collaboration through the Research Pillar involves bridging domain-specific researchers with experts who build AI systems, Wolverton said.
Although Budinger said he doesn’t need to “look far to find excellence” at NU, he said some AI research is currently out of view, leaving faculty unaware of developments across NU’s campuses.
“There’s a real opportunity for us to combat a perception that we don’t have a lot of AI research going on at Northwestern by bringing together the investigators that are doing (AI research) and to leverage some of the unique strengths that we have,” Budinger said.
Wolverton said connecting researchers across fields can also spark unexpected technical overlaps.
“It’s entirely possible that the models I’m building to try and predict materials somehow would also be useful to people who are trying to build models of medical data or other types of data,” Wolverton said.
For Budinger, growing these collaborations means increasing resources such as federal or industry funding while also building shared institutional goals for research.
To do so, Budinger said he hopes that the NNCI becomes visible as part of NU’s national research profile while serving every student on campus.
“What I would like to see is that the NNCI becomes something that’s recognizable to everybody in the Northwestern community as a community within the community, where they can go to access AI resources,” Budinger said.
Infrastructure Pillar leader works to eliminate ‘friction’ in tech usage
Paris wrote in a statement to The Daily that he oversees both everyday technology support as well as large research computing systems like the University’s Quest supercomputer.
When considering infrastructure for AI advancement, he wrote that adaptability is key.
“If you haven’t used any given tool in the past month, it’s almost like you’ve never used it at all,” Paris wrote. “The space is evolving that quickly. Keeping up is a challenge we all face as we build a foundation at the same time our community drives for innovation.”
Paris also wrote that infrastructure is a foundational requirement for advancement in computationally costly research, spanning everything from high-performance computing to everyday AI productivity tools.
Spearheading the Infrastructure Pillar for NNCI, Paris wrote that he plans to scale resources while strengthening NU’s AI tools that have strong guardrails and data protections.
“When infrastructure can keep up with our community, innovation can become easier,” Paris wrote. “But when not, progress can slow down.”
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