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ebrahimpichka/README.md

About


I’m an engineering graduate and ML enthusiast/researcher. My interests primarily include deep RL, applications of machine learning in mathematical optimization and operations research, graph representation learning, and geometric deep learning. Also, I always have an eye for advances and research in more fundamental topics of AI such as optimization techniques, and learning methods.

Stack


Python TensorFlow Keras PyTorch PyTorchLighning pyg sklearn
JAX C++ julia julia Colab openaigym scipy

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  1. awesome-optimization awesome-optimization Public

    A curated list of mathematical optimization courses, lectures, books, notes, libraries, frameworks and software.

    711 73

  2. GNN-RecSys GNN-RecSys Public

    Implementation and testing of LightGCN with some variations. Graph-Neural-Network-based recommendation system.

    Jupyter Notebook

  3. attn-PG-RL-tsp attn-PG-RL-tsp Public

    A PyTorch implementation of the attention-based Policy Gradient RL for learning to solve Travelling Salesperson Problem based on the paper https://doi.org/10.1007/978-3-319-93031-2_12

    Jupyter Notebook 9 1

  4. OptionPricingLib OptionPricingLib Public

    C++ library for pricing options using various numerical methods, designed for quantitative finance applications

    C++ 3

  5. GAT-pt GAT-pt Public

    PyTorch implementation of the Graph Attention Networks (GAT) based on the paper "Graph Attention Network" by Velickovic et al - https://arxiv.org/abs/1710.10903v3

    Python 51 11

  6. colat-opt-jax colat-opt-jax Public

    Large-scale financial collateral optimization using the Primal-Dual Hybrid Gradient (PDHG / Chambolle-Pock) algorithm implemented in JAX

    Python 2