Jason Deglint

Jason Deglint is an Adjunct Professor at the University of Waterloo, affiliated with the Adjunct Faculty group. His work bridges medical imaging technologies and environmental science, leveraging machine learning for practical applications.

Research Interests include Medical Imaging, Machine Learning, Biomedical Engineering, Environmental Monitoring, and Computer Vision. He focuses on developing AI-driven tools for real-time diagnostics in critical care settings, enhancing ultrasound imaging through deep learning, and creating privacy-preserving federated learning systems for ecological data analysis. Additionally, his research addresses toxin detection using microfluidic devices and advanced microscopy techniques for spectral and morphological analysis of biological samples.

His recent publications highlight a dual focus on improving medical diagnostics and environmental monitoring. In medical imaging, he has explored deep learning models for lung ultrasound artifact identification and parenchyma classification, emphasizing real-time validation in intensive care units. In environmental science, his work on phytoplankton classification and harmful algal bloom detection demonstrates the application of federated learning and synthetic dataset generation. He also investigates multispectral microscopy and signal processing methods for non-invasive health measurements, as seen in projects like SAPPHIRE and SALT.

• Scientific Awards: Distinguished Paper Award (2015)

In advising, Jason Deglint does not currently list any formal advisees. Details about grants or funding are not provided in the available texts. He collaborates on innovative projects such as SAPPHIRE and SALT, which involve advanced imaging systems and ocean model improvements for tracking sea lice. His contributions also extend to field-portable microscopes and real-time networked medical imaging solutions through collaborations with interdisciplinary teams.