CHICAGO — For the fourth consecutive year, DePaul University and Rosalind Franklin University are awarding research grants to support faculty collaborations across computing, health sciences and medicine. This year, faculty will receive $200,728 in funding to advance projects spanning wearable biosensors, immunological profiling and AI-driven drug discovery.
“The program’s primary goal is to foster new interinstitutional research collaborations that leverage emerging technologies, including artificial intelligence and quantum computing, to address complex challenges in healthcare and biomedical science,” said Daniela Stan Raicu, associate provost for research and professor in DePaul’s Jarvis College of Computing and Digital Media.
2026 research award recipients:
Development of a non-invasive wearable biosensor for research applications
David Hanley, assistant professor of computing in DePaul’s Jarvis College of Computing and Digital Media, is partnering with Holly Hunsberger, assistant professor of neuroscience, center for neurodegenerative disease and at Rosalind Franklin University, on a project to advance monitoring methods in small-scale biomedical research.
The project addresses a gap in current research tools: while wearable technology has become increasingly sophisticated in human health and consumer applications, comparable innovations have not been widely adopted in foundational biomedical research. Existing approaches often rely on invasive sensors or periodic measurements that capture only limited data points.
Hanley and Hunsberger are developing a non-invasive wearable biosensor designed to continuously measure multiple physiologic biomarkers in real time. By enabling long-term, high-resolution monitoring without the need for surgical implantation or repeated testing, the system has the potential to improve data quality and support more efficient experimental design.
Comprehensive immunological responses to Epstein–Barr virus in heart transplant recipients who do and do not develop post-transplant lymphoproliferative disease
Leonard Jason, professor of psychology in DePaul’s College of Science and Health, Thiru Ramaraj, assistant professor of computing in the Jarvis College of Computing and Digital Media, and Ben Katz, professor of pediatrics in Northwestern's Feinberg School of Medicine are collaborating with David Everly, associate professor of microbiology and immunology, center for cancer cell biology, immunology and infection at Rosalind Franklin University, to better understand immune responses to Epstein–Barr virus (EBV) following heart transplantation.
Heart transplant recipients face a heightened risk of cancer, with approximately 10% developing malignancies within 10 years. Post-transplant lymphoproliferative disease (PTLD) is among the most common. Up to 90% of PTLD cases are associated with EBV; however, current clinical monitoring typically focuses on a limited set of viral antigens.
The project will use an EBV-specific peptide microarray to analyze serum samples from 40 patients who develop PTLD and 80 who do not, with the goal of identifying distinct immunological profiles linked to disease progression. Researchers hypothesize that antibody responses to specific EBV antigens may help predict clinical outcomes, offering potential biomarkers for earlier detection and improved patient management.
Integrating quantum computing and medicinal chemistry to accelerate hCNT inhibitor discovery
Jindi Wu, assistant professor of computing in DePaul’s Jarvis College of Computing and Digital Media, is working with John K. Buolamwini, chair of pharmaceutical sciences at Rosalind Franklin University, on a project that combines quantum computing and medicinal chemistry to advance drug discovery.
Human concentrative nucleoside transporters (hCNTs) play a critical role in the uptake of drugs used in cancer chemotherapy and antiviral treatments. Despite their importance, there are currently no potent or subtype-selective inhibitors, and the standard reference compound demonstrates only modest activity. Only about 45 compounds have been characterized; a dataset too small for traditional AI methods that require large amounts of training data.
To address this challenge, the team will develop a closed-loop system that integrates AI-driven predictions with experimental validation. The approach includes a relative activity predictor designed for small datasets and a quantum-based compound generator to propose novel candidates. Promising compounds will be synthesized and evaluated in vitro, with results feeding back into the model to refine predictions over multiple iterative cycles.
Media Contact:
Kenneth Adusah
KADUSAH@depaul.edu
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