cv

Associate Professor

National School of Applied Sciences-Tetouan

Teaching artificial intelligence courses to Engineering students

• ML
• AI
• Maths

Associate Professor

National School of Applied Sciences-Safi

Teaching artificial intelligence courses to Engineering students

• ML
• AI
• Maths

Associate Professor

School of Artificial Intelligence Euromed

Teaching artificial intelligence courses to Engineering students

• ML
• AI
• Maths

Adjunct Professor

National School of Arts and Crafts

Teaching Numerical Analysis method

• Scientific computing

PhD

National School of Arts and Crafts

Applied Mathematics

• Optimization
• MRF
• Vision

Constrained energy variation for change point detection

Multidimensional Systems and Signal Processing

The problem of change point detection can be solved either by online methods, based on a discrepancy measure, or by offline methods. The former tries to detect the change points one by one with a sliding window and leads to a lower computational time but are more sensitive to noise. Conversely, offline methods consider the entire data to detect all the change points which make them more robust against the noise but at a price of higher computational cost.

Nonconvex Energy Minimization with Unsupervised Line Process Classifier for Efficient Piecewise Constant Signals Reconstruction

Statistics, Optimization & Information Computing

In this paper, we focus on the problem of signal smoothing and step-detection for piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis, and anomaly detection in genetics.

A Novel Online Change Point Detection Using an Approximate Random Blanket and the Line Process Energy

International Journal on Artificial Intelligence Tools,

In this paper, we focus on the problem of change point detection in piecewise constant signals. This problem is central to several applications such as human activity analysis, speech or image analysis and anomaly detection in genetics. We present a novel window-sliding algorithm for an online change point detection.

A DPS filter for nonconvex edge preserving for PieceWise constant signals denoising

4th International Conference on Optimization and Applications

A robust estimator, namely the DPS algorithm, for piecewise constant signals denoising, is revised in this paper. Starting from its Markov random field formulation, which defines the solution as the global minimizer of a non-convex non-smooth energy function.

Recursive reconstruction of piecewise constant signals by minimization of an energy function

Inverse Problems & Imaging

The problem of denoising piecewise constant signals while preserving their jumps is a challenging problem that arises in many scientific areas. Several denoising algorithms exist such as total variation, convex relaxation, Markov random fields models, etc.

A DPS extension to restore blurred and noisy piecewise constant signals

Inverse Problems in Science and Engineering

In this paper, we are interested in restoring piecewise constant signals obtained from a linear operator A (e.g. a blurring operator) and degraded by a white Gaussian noise.

Maximum Entropy learning

Entropy is an old concept in physics. It can be defined as the measure of chaos or disorder in a system[1]. Higher entropy means lower chaos. It is slightly different in information theory. The mathematician Claude Shannon introduced the entropy in information theory in 1948. Entropy in information theory can be defined as the expected number of bits of information contained in an event. For instance, tossing a fair coin has the entropy of 1. It is because of the probability of having a head or tail is 0.5. The amount of information required to identify it’s head or tail is one by asking one, yes or no question — “is it head ? or is it tail?”. If the entropy is higher, that means we need more information to represent an event. Now, we can say that entropy increases with increases in uncertainty. Another example is that crossing the street has less number of information required to represent/ store/ communicate than playing a poker game.

• Reinforcement learning
• Maximum Entropy