RED Talks

The Data Science Initiative at NC State hopes to raise awareness of the breadth and depth of data science across campus as well as to continue to grow and engage the data science community at NC State by sponsoring RED Talks, Where Data Meets Science. RED Talks will consist of an hour long, data science related talk from local and national leaders in data science.  In addition to promoting data science at NC State, our aim is to also spur interdisciplinary collaboration through a series of networking events, following the talks, where faculty and researchers from across campus, and more importantly across disciplines, can meet and engage with each other as well as with the larger data science community within the Triangle.  Further, one RED Talk per semester will also include additional activities to promote existing research on campus and cultivate new interdisciplinary approaches.

Note to CSC and Statistics Graduate Students – these lectures have been approved to count toward the required lectures for graduate students. For Statistics grad students, there will be a sign-in sheet at the front. Download CSC seminar attendance form.


Dr. Cranos Williams

Professor, Electrical and Computer Engineering, North Carolina State University

Date: March 16, 2021
Time: 4:00 PM to 5:00 PM
Talk Title: Computational Intelligence and Machine Learning Approaches for Modeling Plant Systems Within and Across Biological Scale

The next revolution in precision agriculture solutions will require improved understanding of the complex regulatory mechanisms that are instrumental in plant growth, development, and adaptation. Key in these efforts is the ability to acquire and analyze data across biological scales (from molecular to phenotypic scales). High-throughput data that have been collected across biological scales include molecular data such as gene expression profiles and confocal imaging to data capturing plant physiology such as hyperspectral imaging and remote sensing. The diversity of these datasets (in combination with the complexity of plant systems) have created opportunities for the development of novel computational intelligence and machine learning approaches that are capable of modeling plant systems within and across biological scales. In this presentation we provide a brief overview of approaches for analyzing various types of high-throughput biological data. These approaches address the many challenges associated with analyzing biological data, including the need to mitigate high variation and/or uncertainty, the need for novel segmentation and feature extraction, and the integration of disparate datasets for making causal inferences across scale. The application of these approaches has led to scientific contributions such as the modeling of key gene regulatory mechanisms involved in plant stress response, the identification of emergent properties that link molecular activity to phenotypic outcomes, and the development of automated high-throughput phenotyping approaches for early detection of plant diseases. The continued acquisition of high-throughput data across scale and the continued development of novel machine learning and modeling tools will provide opportunities to further push the boundaries of our understanding of plant systems and will be key to better understanding how plants respond to complex environments.

Dr. Cranos Williams is a professor in the Electrical and Computer engineering department at North Carolina State University. He directs the EnBiSys Research Laboratory, which 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 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. These efforts have direct application to efficient biofuel production, plant adaptability to pollution and climate change, and improvement of plant defenses to pathogens and pests. His research addresses four major topics associated with modeling biological systems: experimentation, estimation, implementation, and integration.

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