Arash Sadeghzadeh is an experienced lecturer and researcher with nearly 20 years of academic experience in machine learning, data science, and control systems engineering. He has held research and teaching positions across several universities and international institutions.
He earned his Ph.D. in Automatic Control in 2010 and subsequently served as an Assistant Professor at multiple universities for about a decade. In 2009, he was a visiting researcher at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, collaborating on advanced control systems topics.
In 2019, Arash joined Eindhoven University of Technology (TU/e) as a postdoctoral researcher, where he applied machine learning methods to the modeling of complex mechatronic systems. In 2021, he moved to the French National School of Civil Aviation (ENAC), University of Toulouse, where he continued as a postdoctoral researcher, focusing on the simulation, modeling, control, and formal verification of complex nonlinear aviation systems using machine learning techniques.
Since 2023, Arash has been with Breda University of Applied Sciences (BUas), contributing to both research and education in data science and artificial intelligence.
He has taught a wide range of undergraduate courses, including Machine Learning, Deep Neural Networks, Engineering Mathematics, and various Control Systems modules. At the graduate level, he has delivered specialised courses such as Convex Optimisation, Predictive Control, Robust Control, and Linear Matrix Inequalities in Control.
Arash has extensive experience supervising students, having mentored over 20 undergraduate theses and more than 10 graduate research projects at the Master’s and Ph.D. levels.
Expertise
- Machine Learning
- Artificial Intelligence
- Control Systems
- Data Science
- Deep Learning
- Mechateronics
- Robotics
Teaching
- Mathematics (Y1A2)
- Mathematical Foundations for Machine Learning (Y1B2)
- Time Series Analysis (Y1D1)
Education
- Ph.D. of Automatic Control, Tarbiat Modares University, Tehran, 2006-2010
- M.Sc. of Control Systems, University of Tehran, 2001-2004
- B.Sc. of Biomedical Engineering, Amirkabir University of Technology, Tehran, 1997-2001
Selected Publications
Arash has authored numerous peer-reviewed articles in control systems, machine learning, and intelligent systems, particularly focusing on nonlinear modelling, LPV system theory, and neural network-based control. For a complete list of publications, please visit Arash Sadeghzadeh’s Google Scholar profile. Representative publications include:
- Improved Embedding of Nonlinear Systems in Linear Parameter-Varying Models with Polynomial Dependence. A. Sadeghzadeh, R. Tóth — IEEE Transactions on Control Systems Technology, Vol. 31(1), pp. 70–82, 2023
- Autoencoder Neural Networks for LPV Embedding of Nonlinear Systems. A. Sadeghzadeh, P.L. Garoche — IFAC-PapersOnLine, Vol. 56(2), pp. 9062–9067, 2023
- Reachability Analysis of Linear Parameter-Varying Systems with Neural Network Controllers A. Sadeghzadeh, P.L. Garoche — IEEE Conference on Control Technology and Applications (CCTA), 2022, pp. 1372–1377
- Reachability Set Analysis of Closed-Loop Nonlinear Systems with Neural Network Controllers. A. Sadeghzadeh, P.L. Garoche — American Control Conference (ACC), 2022, pp. 2289–2294
- Affine LPV Embedding of Nonlinear Models with Improved Accuracy and Minimal Overbounding. A. Sadeghzadeh, B. Sharif, R. Tóth — IET Control Theory & Applications, Vol. 14(20), pp. 3363–3373, 2020
- On Exploiting Inexact Scheduling Parameters for Gain-Scheduled Control of LPV Discrete-Time Systems. A. Sadeghzadeh — Systems & Control Letters, Vol. 117, pp. 1–10, 2018
- Gain-Scheduled Continuous-Time Control Using Polytope-Bounded Inexact Scheduling Parameters. A. Sadeghzadeh — International Journal of Robust and Nonlinear Control, Vol. 28(17), pp. 5557–5574, 2018
- Fixed-Order H∞ Controller Design for Systems with Ellipsoidal Parametric Uncertainty. A. Sadeghzadeh, H. Momeni, A. Karimi — International Journal of Control, Vol. 84(1), pp. 57–65, 2011
- Emotional Learning-Based Intelligent Speed and Position Control Applied to a Neurofuzzy Model of a Switched Reluctance Motor. H. Rouhani, A. Sadeghzadeh, C. Lucas, B.N. Araabi — Control and Cybernetics, Vol. 36(1), pp. 75–95, 2007