Jonathan Gryak

Jonathan Gryak is an Assistant Professor of Computer Science and Data Science at Queens College and the CUNY Graduate Center, where he also serves as Deputy Executive Officer. He leads the Interdisciplinary Data Science Lab (IDSL), focusing on developing novel AI/ML methods for biomedical applications that leverage underlying problem morphology and operational constraints.

Education:

• B.S. in Mathematics and Computer Science (Dual), Rensselaer Polytechnic Institute
• M.S. in Mathematics, Fairfield University
• M.Phil. in Computer Science, CUNY Graduate Center
• Ph.D. in Computer Science, CUNY Graduate Center (2017)

Gryak's research centers on Nonlinear Algebraic Data Analysis, combining tensor analysis, numerical algebraic geometry, deep neural networks, and invariant theory to create more accurate models of complex real-world data. His Sequential Algebrogeometric Analysis (SAGA) approach captures disease progression through heterogeneous, longitudinal medical data. His work bridges the gap between theoretical mathematics and practical clinical applications, particularly in cardiac care, traumatic brain injury, and food allergy diagnostics.

His recent publications demonstrate a strong emphasis on interpretable machine learning methods, especially those employing tropical geometry, for critical healthcare applications. These include predicting heart failure therapy needs, detecting acute respiratory distress syndrome, and improving oral food challenge outcome prediction. His research consistently focuses on methods that incorporate domain knowledge, handle asynchronous data, and leverage intrinsic data structures while maintaining clinical interpretability.

Scientific Awards:

• CUNY Faculty Fellowship Publication Program (2024)
• NSF grant for improving treatments for heart failure patients through machine learning

Gryak actively mentors PhD and undergraduate students in the IDSL, guiding research that spans from theoretical mathematical developments to clinical implementations. His lab collaborates with medical professionals to translate AI techniques into commercial applications that impact patient care. Current projects include multimodal data integration for clinical decision support across various conditions including cancer, traumatic brain injury, and inflammatory bowel disease.

The Interdisciplinary Data Science Lab under Gryak's leadership develops end-to-end solutions, from novel mathematical techniques to industry and clinical partnerships for commercialization. Recent work has produced automated systems for endoscopic disease assessment, hematoma segmentation, and cardiac event prediction, with multiple patents stemming from this research.