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Postdoctoral Researcher
Ali Tavasoli
Postdoctoral Research Associate
School of Data Science
Email: at9kf@virginia.edu
Additional Contact Information
Office: Elson 183B
Ali Tavasoli is a postdoctoral research associate at the School of Data Science and is interested in the theory of nonlinear dynamical systems and control, with particular attention to chaos in these systems, and application areas in various technological, biological, and social sciences. Many complex systems possess chaotic behavior, where exact identification and prediction of the system state is not possible due to supercritical properties such as "divergence of nearby trajectories," "mixing flows," or "fractal dimensions."
Tavasoli's research utilizes mathematical and data-driven approaches to study these complex systems. His mathematical approaches are aligned with the theory of nonlinear systems and control, as well as geometrical aspects of dynamical systems and Ergodic theory. He studies modern data-driven approaches in the operator theoretic framework for identifying and controlling nonlinear dynamical systems. While developing accurate models is crucial for properly diagnosing and controlling dynamical systems, Tavasoli believes traditional machine learning techniques lack sufficient stability and convergence properties to deal with the challenges of chaotic systems.
"The conventional deep learning approaches are not scalable to large-scale systems due to the computational complexities of optimal parameter selection and convergence speed. By employing operator theoretic tools based on the spectral analysis of operators such as Koopman and Perron-Frobenius, we are able to decompose the data into coherent patterns of the underlying dynamics. These coherent patterns, which encode the most robust and regular features of the dynamics, are our key to identifying and controlling chaotic systems, as well as representing large-scale systems dynamics on low-order manifolds. We have examined the advantages of this approach in applications of mechanical engineering, neuroscience, and power grids."
Tavasoli earned his Ph.D. in Mechanical Engineering from Amirkabir University of Technology and an M.Sc. in Mechanical Engineering from Shiraz University in Iran.
Education
- Ph.D. in Mechanical Engineering from Amirkabir University of Technology
- M.Sc. in Mechanical Engineering from Shiraz University
- B.Sc. in Mechanical Engineering, Islamic Azad University
Data Science Domains
Systems, Analytics
Selected Publications
A. Tavasoli, H. Shakeri, E. Ardjmand, and W.A. Young (2021). Incentive Rate Determination in Viral Marketing. European Journal of Operational Research, 289 (3): 1169-1187.
E. Ardjmand, M. Singh, H. Shakeri, A. Tavasoli, and W.A. Young (2021). Mitigating the risk of infection spread in manual order picking operations: A multi-objective approach. Applied Soft Computing, 100: 106953
H. Shakeri, A. Tavasoli, E. Ardjmand, and P. Poggi-Corradini (2020). Designing Optimal Multiplex Networks for Certain Laplacian Spectral Properties. Physical Review E, 102 (2): 022302.
A. Tavasoli (2019). Boundary control of a circular curved beam using active disturbance rejection control. International Journal of Control, (5): 1137-1154.
A. Tavasoli (2018). Exponential stabilization of two-dimensional vibration of a boundary-controlled curved beam with tip mass. International Journal of Systems Science, 49 (13): 2847-2860.
A. Tavasoli (2018). Active disturbance rejection boundary control of a Timoshenko beam including tip mass. ISA transactions, 80: 221-231.
A. Tavasoli (2018). Dynamic modelling and adaptive robust boundary control of a flexible robotic arm with two-dimensional rigid body rotation. International Journal of Adaptive Control and Signal Processing, 32(6): 891-907.
A. Tavasoli (2016). Robust boundary stabilization of a vibrating rectangular plate using disturbance adaptation. International Journal of Adaptive Control and Signal Processing, 30(11):1603–1626.
A. Tavasoli (2015). Robust boundary control with adaptive upper-bounds to stabilize two-dimensional vibration of flexible spinning shaft including model uncertainties and external disturbances with unknown bounds. International Journal of Adaptive Control and Signal Processing, 29:537–562.
A. Tavasoli (2015). Dynamic modelling and nonlinear boundary control of hybrid Euler-Bernoulli beam system with a tip mass. Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 229(1):3-15.