معرفی
Kiley Graim, Ph.D., is an Assistant Professor in the Department of Computer & Information Science & Engineering at the University of Florida. Her research bridges computer science and life sciences, focusing on machine learning models that integrate large-scale genomics data to understand human disease mechanisms and develop personalized therapies. She leads a lab dedicated to probing complex biological networks through computational approaches.
Education:
- Ph.D., Biomolecular Engineering (2016), University of California Santa Cruz
- M.S., Computer Science (2012), Colorado State University
- B.S., Computer Science (2008), Colorado State University
Research interests emphasize equitable machine learning to reduce bias in genomic studies, multi-omic analyses of sepsis and cancer subphenotypes, and comparative oncology leveraging canine models. She also explores AI applications in biomedical education and extracellular vesicle engineering for drug delivery systems.
Her recent publications highlight trends in:
- Cross-species cancer genomics (human/dog comparisons)
- Machine learning frameworks for ancestry-agnostic disease signatures
- Lipidomic and transcriptomic profiling of sepsis subtypes
- Validation of consumer genetic testing tools
Although no explicit scientific awards are listed, her work demonstrates contributions to critical areas like genomic equity and translational bioinformatics. Advising focuses on interdisciplinary graduate students working at the bioinformatics-biomedicine interface. Active grants likely support her lab's work in disease modeling and AI-driven precision medicine systems.
Labs/Teams: Graim Lab at University of Florida, which develops computational tools for genomic analysis and molecular disease modeling.
