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Cranos Williams

CW
Cranos Williams Headshot

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

919-513-1923

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

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Grants

Date: 10/01/21 - 9/30/27
Amount: $19,587,488.00
Funding Agencies: National Science Foundation (NSF)

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.

Date: 10/01/20 - 8/31/26
Amount: $749,441.00
Funding Agencies: National Science Foundation (NSF)

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.

Date: 06/15/22 - 6/14/26
Amount: $649,722.00
Funding Agencies: USDA - National Institute of Food and Agriculture (NIFA)

The Agricultural DECision Intelligence moDEling System for huMan-AI collaboRative acTion Elicitation and impRovement (DECIDE-SMARTER) project will lay the foundations of democratized access to Decision Intelligence (DI) technology for stakeholders across the agriculture value chain, filling a longstanding gap between technology and decision makers. Through a process of participatory design, the project team will work with stakeholders in the sweetpotato value chain to: 1) Create a software asset that helps growers with an otherwise difficult decision; 2) conduct experiments that inform the best software interfaces possible to support complex agricultural decision making (through characterizing, understanding, and leveraging human cognitive abilities; 3) identify potential sources of bias in the DI process that would present barriers to democratized access to the technology; and 4) develop a reference architecture and prototype implementation of a modeling, simulation, and visualization framework for implementing multiple DI models with agriculture stakeholders. The project will leverage the ongoing research, data acquisition, and stakeholder efforts by the Sweetpotato Analytics for Produce Provenance and Scanning (Sweet-APPS) team, a multi-disciplinary endeavor that aims to reduce agricultural waste and maximize yield for North Carolina������������������s sweet potato growers.

Date: 01/15/21 - 1/14/26
Amount: $238,500.00
Funding Agencies: USDA - National Institute of Food and Agriculture (NIFA)

A Pipeline of a Resilient Workforce that integrates Advanced Analytics to the Agriculture, Food and Energy Supply Chain

Date: 01/01/23 - 12/31/25
Amount: $85,000.00
Funding Agencies: NC Peanut Growers Association, Inc.

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.


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