8000 Khamies (Waleed Khamies) · GitHub
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khamies/README.md

Waleed Khamies – Applied Research Scientist

I am an Applied Research Scientist at NTWIST, where I focus on building secure and efficient AI systems. My core interests lie in reinforcement learning, self-supervised learning, and optimization-based approaches to real-world automation and decision-making challenges.

Previously, I was a Research Engineer Intern at Mila – Quebec AI Institute, working under the supervision of Liam Paull on deep learning for robotics in uncertain and partially observable environments.

I hold a Master’s in Mathematical Sciences with a focus on Machine Learning from AMMI – African Master’s in Machine Intelligence, where I conducted research on reinforcement learning interpretability with George Konidaris. I also earned a Bachelor’s in Electrical and Electronics Engineering from the University of Khartoum, with research collaboration under Benjamin Rosman.


Explore My Work

If you're interested in my work or potential collaborations, feel free to explore the following links:


Contact

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  1. LSTM-Variational-AutoEncoder LSTM-Variational-AutoEncoder Public

    A PyTorch Implementation of Generating Sentences from a Continuous Space by Bowman et al. 2015.

    Python 32 9

  2. T5-Fine-tune-Wikihow-XSum T5-Fine-tune-Wikihow-XSum Public

    This is a Pytorch implementation of a summarization model that is fine-tuned on the top of Google-T5 pre-trained model.

    Python 2

  3. Piano-VAE Piano-VAE Public

    A pytorch Implementation of VAE-based musical model to generate and interpolate piano'notes using Nottingham dataset.

    Python 8 1

  4. montrealrobotics/BIV montrealrobotics/BIV Public

    IV for Deep Learning

    Python 1 1

  5. montaserFath/Reinforcement-Learning-for-Prosthetics montaserFath/Reinforcement-Learning-for-Prosthetics Public

    Apply Reinforcement Learning (RL) to enable prosthetics to calibrate with differences between humans and differences between walking environments

    Jupyter Notebook 9 3

  6. comparative-study-brain-registration comparative-study-brain-registration Public

    Comparative study of classical and deep learning methods for multi-modal brain image registration using the RIRE dataset.

    Jupyter Notebook 1

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