Master’s student strengthens AI innovation through internship

Source: https://www.yorku.ca/yfile/2026/03/18/masters-student-strengthens-ai-innovation-through-internship/ Parent: https://www.yorku.ca/gradstudies/about-us/news/

Posted on March 18, 2026

Edited byAshley Goodfellow CraigMarch 18, 2026

A master's student at York University's Lassonde School of Engineering is heading to Tokyo this month where he will help researchers sharpen how AI technology reads and uses data.

Caleb Morgan is the second York student accepted into the competitive international research internship at the National Institute of Informatics (NII) in Japan. The program offers graduate students the opportunity to conduct research at global partner institutions, enhancing international collaboration and research inquiry.

A final-year master’s of applied science candidate, Morgan will spend up to six months at NII working on AI systems that could accelerate the way scientists discover and design new tools, as well as inform real-world progress in AI applications for greener manufacturing, aerospace innovation and faster drug development.

Morgan will begin his internship in late March.

At NII, he will work under Associate Professor Mahito Sugiyama on knowledge graphs – a way of organizing information so AI models can understand individual data points and the relationships between them, much like the the relationship between list of names and a family tree.

Morgan shares an example of how this is applied in practice: in disease prediction, a knowledge graph allows a model to connect a patient's medical history to their location and a specific time period. This produces more accurate results than a model working from isolated data, says Morgan.

"If you throw data into a model without any knowledge graph, the model might learn about people and situations but not be able to relate them to each other," he says. "When we construct a knowledge graph, the model understands that this person was related to this event or this place, and that gives us a more generalized, more insightful output."

He will also work with transformer models – the same foundational architecture behind well-known AI tools like ChatGPT – to decode the language of chemical structures and materials. The goal refining AI systems to make reliable predictions even when data is scarce – a significant bottleneck in scientific research and engineering, notes Morgan.

NII's environment, he says, is what makes it the right place for this research. The institute draws researchers who develop novel AI architectures grounded in advanced mathematics – exactly the kind of computer science apporach he wants to bring back to engineering.

Morgan’s foundation for this field was cultivated at York. In the Lassonde-based Processing Structure Property Performance (PSSP) Lab, supervised by Associate Professor Solomon Boakye-Yiadom, he has been developing AI models to predict defects in metal 3D printing for high-entropy alloys – a newer class of metal blends engineered for extreme environments like aerospace and high-corrosion applications.

Representing atomic compositions as knowledge graphs has already improved prediction accuracy, he notes, and he has presented these findings at several conferences. This combined effort in research and knowledge sharing shaped his successful NII application.

Getting there took persistence, however. Morgan applied to the NII program once before and while he was not selected, he applied again with a sharper, more focused application – one that advocated for why an engineer should cross into computer science.

"I had to steer my application to say ‘Yes, I'm an engineer, but I want to delve into computer science to develop architectures for my domain,’" he says. "I was much more intentional about the second application."

Behind the scenes, York International has been closely involved in his preparation, helping with documentation and accommodation planning in Tokyo – support Morgan says has made the process seamless.

Day-to-day at NII, his work will largely be behind a desk: writing code, reading papers and running experiments with datasets and models to test how well they can extract meaning from structured knowledge.

He will return to York later this year with new collaborations, novel methods and a sharper way of thinking.

"I'm going to have the mindset of a computer scientist and keep my domain knowledge as an engineer and be able to merge them to do new things,” he says.

For York students eyeing similar opportunities, Morgan's path offers its own message.

"Be intentional, tailor your application," he says, "and don't be discouraged by rejection."

With files from Mzwandile Poncana

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