McGill researchers are tackling some of today’s most complex challenges that increasingly demand bold thinking that crosses conventionally recognized disciplinary boundaries. That’s exactly what the Convergent Research Themes (CRT) Program was designed to support. Administered by McGill’s Computational and Data Systems Institute (CDSI) and funded by the Faculty of Science, the program seeks out innovative research where computational and data systems are central, and where progress depends on integrating perspectives from multiple fields. The Exploratory phase ($10,000) funds emergent groups that have recently initiated their collaborations, while the Proof-of-Concept phase ($20,000) supports established teams looking to build on exploratory work, with the aim to build towards more conventional grants.
This year, four McGill researchers received CRT funding: Daniele Malomo (Civil Engineering, Exploratory), Ross Otto (Psychology, Proof of Concept), Skyler Wang (Sociology, Exploratory), and Yaoyao Fiona Zhao (Mechanical Engineering, Proof-of-Concept). While their research spans diverse domains, they share a common thread: each is working at unconventional intersections between disciplines to address problems that matter right now.
Predicting disaster debris to protect emergency access
Picture this: a building collapses under extreme load during a natural disaster. When debris blocks roads, access for emergency services can be compromised at the very moment it matters most. Professor Malomo’s CRT-funded project tackles this challenge by fusing real‑time physics engines—originally developed for the videogame industry—with machine learning and transportation network analysis.
The goal is to predict where collapsed buildings are likely to block streets during disasters such as earthquakes, floods, or strong winds, and to assess the resulting effects on evacuation and emergency access. “With the adverse climate change effects making the old building stock of Canada even more vulnerable, this interdisciplinary research offers unique pathways and modelling tools supporting disaster mitigation strategies,” Malomo says.
Understanding human choice behaviour beyond the lab
While much is known about human decision‑making from studies conducted in controlled laboratory settings, many of these phenomena remain poorly understood—or even unobserved—in everyday contexts. Professor Otto’s CRT‑winning research seeks to address this gap by examining choice behaviour outside the lab, using real-world data.
“Our project employs a data‑driven approach to understand real‑world human choices at the population level as well as the individual consumer, by analyzing a rich retail dataset—which includes over 50 million transactions—from a large grocery chain,” Otto explains. “This research presents an exciting opportunity to integrate multiple domains in psychology and computer science to construct better, and more predictive, models of human choice behaviour.”
Examining the role of perceived gender in AI-based mental health care
As artificial intelligence chatbots become an increasingly common tool in mental health care, surprisingly little is known about how users experience them, including how a chatbot’s perceived gender may influence trust, satisfaction, and even health outcomes. For Professor Wang and his co‑principal investigator, Professor Claire Boone (Economics), the urgency to understand these dynamics is clear.
“At a time when AI tools are rapidly entering sensitive domains like mental health, it is essential to understand not only whether they work but also how social identities and user choice shape the user experience,” Wang says. “Without this knowledge, we risk designing systems that fail to foster meaningful engagement or adequately serve the needs of diverse users.” Bringing together sociology, health economics, computer science, and psychology, Wang notes that CRT funding provides the structure and resources to develop a project “[…] where no single field could build on its own.”
A novel approach to diagnosing voice disorders
Detecting voice disorders early remains challenging as current assessments often rely on subjective listening and specialized clinical examinations. Recent advances in artificial intelligence and digital acoustics, however, open the door to transforming subtle voice signals into clinically useful data.
Professor Zhao’s CRT-supported project brings together mechanical engineering, speech‑language pathology, music acoustics, and machine learning to explore whether variability in vocal vibrato can serve as a non‑invasive digital biomarker for early detection of voice disorders. “Using machine learning, we are investigating whether these vibrato patterns can be analyzed objectively to identify musculoskeletal voice disorders, such as vocal hyperfunction/muscle tension dysphonia and temporomandibular disorders,” Zhao notes.
Finding a common thread across disciplines
When research questions are approached from a single disciplinary perspective, key pieces of the puzzle can remain unresolved. Reflecting on his chatbot research, Wang explains: “Technical expertise can build the chatbot but cannot explain how gendered cues shape trust, whereas social science can theorize those dynamics and experimentally test new designs, and health economics and policy analysis can connect individual experiences to systemic realities.” CRT funding encourages researchers to bring these perspectives together, enabling truly integrated approaches.
Zhao similarly emphasizes how the CRT Program enables the interdisciplinary foundation of her project. “The funding enables us to coordinate collaboration across disciplines while building the technical and methodological foundation needed to test the concept rigorously,” she says.
Forging interdisciplinary connections
Beyond supporting interdisciplinary research, the CRT Program breaks down silos by creating opportunities for researchers to connect across domains—a challenge in academic landscapes that are by default highly specialized. That opportunity is especially meaningful in projects like Malomo’s, where collaborators approach shared tools from different disciplinary perspectives. “Although structural, transportation, and computer engineering visions all use machine learning, the scope and method in which each field uses it differs greatly,” he notes. “I am excited to leverage machine learning as a common language to unify these disciplines in our project.”
CRT support also helps researchers strengthen their teams and extend collaboration beyond McGill. Nearly all the research in Otto’s group is led and carried out by trainees, particularly PhD students, making access to dedicated funding essential. “CRT funding will allow me to recruit new PhD students here at McGill to carry out this work, in tight collaboration with our UK‑based team members,” he says.
For Wang, the CRT‑enabled collaboration has also been personally meaningful. He and Boone met during their first‑year orientation as new professors at McGill and are now pursuing research together. “It’s deeply rewarding to build a research program that bridges our respective interests while working alongside a close friend,” Wang says.
Helping research ideas evolve
CRT funding plays a key role in helping research projects move beyond early promise toward real‑world viability. By supporting work at this transitional stage, the program allows researchers to test, refine, and scale their ideas before pursuing larger clinical studies or external funding. For Otto, this progression builds on earlier CRT support. After receiving CRT’s Exploratory Phase funding in 2023, which led to a preliminary study using retail data, he is now expanding the scope and methods of his work as a 2026 Proof‑of‑Concept Phase recipient.
Zhao sees the CRT Program as providing the support needed to rigorously test and validate the direction her research is taking, providing an early opportunity for course correction. “CRT funding allows us to move from promising pilot results toward a functional proof-of-concept,” she says. “This stage is essential for determining whether the approach can work reliably across diverse voices and recording conditions. Ultimately, it positions the project for larger clinical studies and external research funding aimed at developing practical tools for vocal health monitoring.”
Creating space for thoughtful risk-taking
At its core, the CRT Program is designed to support research at critical moments of uncertainty, when ideas are still emerging, collaborations are still forming, and outcomes are not yet fully defined or envisioned. By providing early‑stage support for interdisciplinary data‑driven projects, CRTs enable researchers to test new directions and empowers them to take thoughtful risks towards meaningful discovery. “The CRT offers the possibility of exploring, which is increasingly rare in our research ecosystem,” Malomo reflects. “It also offers a safe space to experiment, revise strategies, and get prepared for larger funding competitions. I appreciate the role of the CRT as an incubator for interdisciplinary projects—that is where the magic happens!”
