Cranos Williams

Biography

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.

Education

• Ph.D. 2008
  Electrical Engineering
  North Carolina State University, Raleigh
• Master's 2002
  Electrical Engineering
  North Carolina State University, Raleigh
• Bachelor's 2001
  Electrical Engineering
  NC A&T State University, Greensboro

Recent Publications

• Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies (2023)
• FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus (2023)
• Dynamics of BMP signaling in the earlyDrosophilaembryo (2022)
• Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs (2022)
• POPEYE intercellular localization mediates cell-specific iron deficiency responses (2022)
• Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras (2022)
• A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa (2021)
• Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery (2021)
• Enzyme Complexes of Ptr4CL and PtrHCT Modulate Co-enzyme A Ligation of Hydroxycinnamic Acids for Monolignol Biosynthesis in Populus trichocarpa (2021)
• High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales (2021)