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Welcome to my page👋

I received my PhD from the University of Antwerp, Belgium. My research is concerned with dependability analysis, robustness checking, validation, and verification technique for Cyber-Physical Systems (CPS) using model-based fault-injection. My main focus is on the optimization of model-based fault-injection using a combination of machine learning and model-based techniques.

I am currently working on an Reinforcement Learning (RL)-based method to optimize fault-injcention. I am currently learning different machine learning approches that can be applied on black-box (FMI standard based model) and white-box (Simulink-based model) CPSs for dependability analysis.

Research interest

  • Dependability Analysis:
    • Safety Assessment
    • Fault-Injection
    • Hazard Analysis
    • Validation and Verification
  • Model-Based System Engineering
  • Machine Learning
  • Embedded System Design and Analysis
  • Electronic Design Automation (ASIC/FPGA)
  • Image Processing

Mehrdad Moradi's Projects

esp-dl icon esp-dl

Espressif deep-learning library for AIoT applications

excalidraw icon excalidraw

Virtual whiteboard for sketching hand-drawn like diagrams

explainx icon explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

falstar icon falstar

Fast Falsification of Hybrid Systems using Probabilistically Adaptive Input

fasten icon fasten

Analyse package dependency networks at the call graph level

fmpy icon fmpy

Simulate Functional Mockup Units (FMUs) in Python

gan icon gan

Tooling for GANs in TensorFlow

garage icon garage

A toolkit for reproducible reinforcement learning research.

gazebo icon gazebo

Open source robotics simulator.

gym icon gym

A toolkit for developing and comparing reinforcement learning algorithms.

highway-env icon highway-env

A minimalist environment for decision-making in autonomous driving

hpm icon hpm

Hidden physics models: Machine learning of nonlinear partial differential equations

hub icon hub

A library for transfer learning by reusing parts of TensorFlow models.

interpret icon interpret

Fit interpretable models. Explain blackbox machine learning.

javacc icon javacc

JavaCC - a parser generator for building parsers from grammars. It can generate code in Java, C++ and C#.

lab icon lab

A customisable 3D platform for agent-based AI research

lc_toolbox icon lc_toolbox

An open-source linear control toolbox for MATLAB.

lime icon lime

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

lime-1 icon lime-1

Lime: Explaining the predictions of any machine learning classifier

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