Cranos Williams
Department of Electrical and Computer Engineering
Data-Driven Plant Science Platform Director, N.C. PSI
Professor
College of Engineering
College of Agriculture and Life Sciences
Department of Plant and Microbial Biology
3320 Plant Sciences Building
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.
Education
Ph.D. Electrical Engineering North Carolina State University 2008
M.S. Electrical Engineering North Carolina State University 2002
B.S. Electrical Engineering NC A&T State University, Greensboro 2001
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)
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies , CURRENT OPINION IN PLANT BIOLOGY (2022)
- FER and LecRK show haplotype-dependent cold-responsiveness and mediate freezing tolerance in Lotus japonicus , PLANT PHYSIOLOGY (2022)
- Multiplex CRISPR editing of wood for sustainable fiber production , SCIENCE (2023)
Grants
The Science and Technologies for Phosphorus Sustainability (STEPS) Center is a convergence research hub for addressing the fundamental challenges associated with phosphorus sustainability. The vision of STEPS is to develop new scientific and technological solutions to regulating, recovering and reusing phosphorus that can readily be adopted by society through fundamental research conducted by a broad, highly interdisciplinary team. Key outcomes include new atomic-level knowledge of phosphorus interactions with engineered and natural materials, new understanding of phosphorus mobility at industrial, farm, and landscape scales, and prioritization of best management practices and strategies drawn from diverse stakeholder perspectives. Ultimately, STEPS will provide new scientific understanding, enabling new technologies, and transformative improvements in phosphorus sustainability.
A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain
At peanut buying stations across the U.S. South East, peanut grading is currently implemented using labor-intensive equipment. Many of the steps related to grading have been unchanged for decades. A critical reason for this involves political pressures against updating or expediting the grading process. However, like many other economic sectors, new labor-force pressures are requiring that more be done with fewer people. Given that (1) labor is more challenging to come by; and (2) political pressure exists to maintain the status quo, we propose to update key steps in the existing process to simplify and/or expedite data collection. This project���s goal is to develop automated imaging and weighing technologies that can serve as a bridge, toward more fully automated systems, by addressing key bottlenecks in the existing grading process. We will achieve this by the following objectives: (1) Automate the weighing and grading of peanuts either traveling down or entering the rollers during pod pre-sizing; and (2) Automate the detection of splits and, if possible, sound versus unsound splits, by adding vision systems to the existing sheller.
One of the grand challenges facing humanity is to secure sufficient and healthy food for the increasing world population. This requires maintaining sustainable cultivation of crop plants under changing climate conditions. Plant roots and soil microbes have been associated since the emergence of plants on land. Nevertheless, the mechanisms that coevolved to control and regulate microbiota associations with healthy plants are largely unexplored. The photosynthetically active green leaf tissues supply assimilated carbon to roots for development and also to feed its associated microbes. To maintain balanced growth, plants have to integrate this underground demand and regulate the rate of photosynthetic CO2 fixation, and sugar allocation needs to be coordinated between root and shoot. Research on plants and their naturally associated microorganisms is therefore in a prime position to provide new perspectives and concepts for understanding plant function, plant performance and plant growth under limited input conditions with a reduced environmental footprint and could also define breeding targets and develop microbial interventions. InRoot aims to: 1. Disentangle the effects of climate and soil type from the impact of root-microbe interactions through transplantation experiments and exploit natural variation to identify the plant genetic components responsible for adaptation to the local microbiota. 2. Identify key bacterial taxa governing the establishment of host-driven microbial networks in the rhizosphere by analysing the microbe-microbe and microbe-host interactions established in tailored synthetic communities (SynComs) with direct consequences on host performance. 3. Define the plant genetic components that control infection of plant roots by ubiquitous and host-specific endophytes using advanced genetic screens and new methods for quantifying root cellular responses to microbes 4. Understand molecular mechanisms integrating root-microbe interactions into whole-plant physiology by investigating systemic physiological responses induced by SynComs using whole plant phenotyping. 5. Predict plant performance as a function of plant and microbiota genotypes by building multiscale models based on genotype, phenotype, and mechanistic data thereby providing knowledge for application. InRoot perspective: Provide knowledge and tools for science-based development of new crop varieties and associated microbial interventions that will improve productivity, reduce the need for fertilizers and pesticides, and alleviate negative environmental impact.
Minimizing crop loss and increasing output, across the food supply chain, will increase the economic viability of US growers and the global economic competitiveness of industry and stakeholder partners. We have assembled a diverse team across different National and International Universities with faculty that have track records of convergent research, education, and outreach. We will be well positioned to implement a Networks of Networks with diverse backgrounds, ethnicities, genders, experiences, and disciplines to drive research and innovation. Students and postdocs will be exposed to hands-on learning, on-farm technology training, cooperative extension, commercialization, industry engagement, and transdisciplinary education to create a highly trained workforce that is equipped to address food security and safety challenges.