Dr. Cranos Williams is currently an associate professor in the Electrical and Computer engineering department at North Carolina State University. Over the four years at NC State, Dr. Williams has developed a highly collaborative, multidisciplinary research program focused on the development of targeted computational and analytical solutions for modeling and controlling biological systems. The solutions he develops 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. Currently, his efforts have been focused on developing solutions for understanding the combinatorial interactions of biomolecular, physiological, and structural processes that impact the functionality of plant systems, i.e. plant systems biology. This work will have direct implications on 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.
Fall: ECE 513-Digital Signal Processing
Digital processing of analog signals. Offline and real-time processing for parameter, waveshape and spectrum estimation. Digital filtering and applications in speech, sonar, radar, data processing and two-dimensional filtering and image processing.
Spring: ECE 492/592-Analysis of Nonlinear Complex Systems
This course will focus on nonlinear systems analysis tools used to analyze the structural and dynamical characteristics of biochemical pathway models. We will discuss several common nonlinear state space representations used to model cellular pathways. The theory and implementation of several analysis tools will be covered. These tools include but are not be limited to: phase plane analysis, qualitative assessment of behavior around equilibrium points, Lyapunov stability analysis, bifurcation, limit cycles and existence of periodic orbits, assessment of regions of attraction, continuous dependence on initial conditions and parameter values, quantitative techniques for conservation and reaction flux analysis, parameter estimation, and sensitivity analysis. Topics in this course will be reinforced using state-of-the-art computational tools such as MATLAB and XPPAUT, which will be used to build, visualize, simulate, and analyze these pathways.
**Knowledge of biology is NOT a requirement for this course