Cranos Williams

Biography

Dr. Cranos Williams is the Goodnight Distinguished Professor of Agricultural Analytics at NC State University with primary and secondary appointments in the electrical and computer engineering and plant and microbial biology departments, respectively.  He also serves as the Platform Director of the Data-Driven Plant Sciences research platform of the North Carolina Plant Sciences Initiative (https://cals.ncsu.edu/psi/) and is the head of the EnBiSys Research Laboratory (https://research.ece.ncsu.edu/enbisys/).  Dr. Williams’ research lab develops methodologies familiar to other areas of electrical and computer engineering (e.g. computational intelligence, machine learning, system identification, nonlinear systems analysis and control, computer vision, and signal processing) to quantify, model, and predict the impact that genetic and environmental perturbations have on overall plant response. The results from these works will have direct implications on key challenges associated with engineering plants for efficient biofuel production, increased adaptability to changing environments, and improved defense to biotic and abiotic stresses.

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

• Advancing sweetpotato quality assessment with hyperspectral imaging and explainable artificial intelligence (2024)
• Dynamics of BMP signaling and stable gene expression in the early Drosophila embryo (2024)
• Evaluating two high-throughput phenotyping platforms at early stages of the post-harvest pipeline of sweetpotatoes (2024)
• Predicting sweetpotato traits using machine learning: Impact of environmental and agronomic factors on shape and size (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 (2024)
• Cellular clarity: a logistic regression approach to identify root epidermal regulators of iron deficiency response (2023)
• Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies (2022)
• FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus (2022)
• Multiplex CRISPR editing of wood for sustainable fiber production (2023)