Name: Mehrdad Moradi
Type: User
Company: University of Antwerp
Bio: PhD on dependability analysis, robustness checking, validation, and verification technique for CPSs using model-based fault injection and ML.
Location: Antwerp, Belgium
Blog: https://www.linkedin.com/in/mehrdad-moradi/
Mehrdad Moradi's Projects
Espressif deep-learning library for AIoT applications
Virtual whiteboard for sketching hand-drawn like diagrams
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.
Fast Falsification of Hybrid Systems using Probabilistically Adaptive Input
Analyse package dependency networks at the call graph level
Import and export Functional Mock-up Units with Simulink
Simulate Functional Mockup Units (FMUs) in Python
FMI Compliance Checker for validation of FMUs 1.0 and 2.0
Tooling for GANs in TensorFlow
GANs in slanted land
A toolkit for reproducible reinforcement learning research.
Open source robotics simulator.
Wrappers, tools and additional API's for using ROS with Gazebo
Google Research
A toolkit for developing and comparing reinforcement learning algorithms.
This is a dataset of handwritten cities in Iran in Arabic/Persian that has been used in my Master project. This dataset is collected for sorting postal packages.
A minimalist environment for decision-making in autonomous driving
Hidden physics models: Machine learning of nonlinear partial differential equations
A library for transfer learning by reusing parts of TensorFlow models.
Fit interpretable models. Explain blackbox machine learning.
Interpretable Machine Learning with Python, published by Packt
JavaCC - a parser generator for building parsers from grammars. It can generate code in Java, C++ and C#.
Deep Learning for humans
Bootstrap Kubernetes the hard way on Google Cloud Platform. No scripts.
A customisable 3D platform for agent-based AI research
An open-source linear control toolbox for MATLAB.
Local Interpretable Model-Agnostic Explanations (R port of original Python package)
Lime: Explaining the predictions of any machine learning classifier