Cranos Williams

Bio

Research Interests: I am currently the director of the EnBiSys Research Laboratory. The EnBiSys Lab is a highly collaborative, multidisciplinary research laboratory, focused on the development of targeted computational and analytical solutions for modeling and controlling biological systems. The solutions we develop are used to build and strengthen the transition from large-scale high-throughput –omics data to highly connected kinetic models in the post-genomic era; models that can be used to attain the depth, understanding, and comprehension needed to manipulate and control biological systems for a defined purpose.

Specific interests in this field include:

– Nonlinear Systems Analysis
– System Identification
– Uncertainty Analysis
– Optimal Experimental Design
– Biological Signal and Data Processing

Patents: S. Chen, L. Ray, N. Cahill, M. Goodgame, and C. Williams, “Method of Image Registration using Mutual Information,” U.S. Patent 7,263,243, Aug. 28, 2007.

Area(s) of Expertise

Computational Intelligence, Machine Learning, Dynamic Systems Modeling, Multi-scale Modeling, Data Mining, Gene Regulatory Networks, Metabolic Pathway Modeling

Publications

• Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence , COMPUTERS AND ELECTRONICS IN AGRICULTURE (2024)
• Dynamics of BMP signaling and stable gene expression in the early Drosophila embryo , BIOLOGY OPEN (2024)
• Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes , SMART AGRICULTURAL TECHNOLOGY (2024)
• Predicting sweetpotato traits using machine learning: Impact of environmental and agronomic factors on shape and size , COMPUTERS AND ELECTRONICS IN AGRICULTURE (2024)
• Spatiotemporal dynamics of NF-κB/Dorsal inhibitor IκBα/Cactus inDrosophilablastoderm embryos , (2024)
• The Black American experience: Answering the global challenge of broadening participation in STEM/agriculture , PLANT CELL (2024)
• Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response , BMC GENOMICS (2023)
• Multiplex CRISPR editing of wood for sustainable fiber production , SCIENCE (2023)
• Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies , CURRENT OPINION IN PLANT BIOLOGY (2022)
• Dynamics of BMP signaling in the earlyDrosophilaembryo , (2022)