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.
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
- Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies , CURRENT OPINION IN PLANT BIOLOGY (2023)
- Identification of Transcription Factors Regulating SARS-CoV-2 Tropism Factor Expression by Inferring Cell-Type-Specific Transcriptional Regulatory Networks in Human Lungs , VIRUSES-BASEL (2022)
- POPEYE intercellular localization mediates cell-specific iron deficiency responses , PLANT PHYSIOLOGY (2022)
- Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras , OPTICS EXPRESS (2022)
- A multiscale model of lignin biosynthesis for predicting bioenergy traits in Populus trichocarpa , COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2021)
- Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery , Computers and Electronics in Agriculture (2021)
- Enzyme Complexes of Ptr4CL and PtrHCT Modulate Co-enzyme A Ligation of Hydroxycinnamic Acids for Monolignol Biosynthesis in Populus trichocarpa , FRONTIERS IN PLANT SCIENCE (2021)
- High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales , EMERGING TOPICS IN LIFE SCIENCES (2021)
- Internal defect scanning of sweetpotatoes using interactance spectroscopy , PLOS ONE (2021)
- Multiscale Modeling of Cross-Regulatory Transcript and Protein Influences , MODELING TRANSCRIPTIONAL REGULATION (2021)
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
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.
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 CarolinaÃƒÂ¢Ã¢â€šÂ¬Ã¢â€žÂ¢s sweet potato growers.
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.