Projects

Benchmarking on Explicability Methods of Graph Neural Networks

I started my Ph.D. by doing a benchmarking on interpretability methods of graph neural networks on two important downstream tasks; Graph Classification and Node Classification. The scope of our project includes at least 15 interpretability methods from different domains. The project is published as a conference paper, and soon it would be available here to download.

Cyclic Adversarial Framework with Implicit Auto-Encoders and Wasserstein Loss

This was my Master's project which is also is submitted to IEEE TNNLS. In this project a new hybrid model was proposed to address the most important problems of GANs that is Mode Collapsing and Missing Mode. The experimental results of the proposed methods by different evaluation metrics demonstrates promising improvements on the state-of-the-art.

Business Intelligence in Knowledge Management Approaches

In this project, based on the datasets provided by East Azarbaijan Technology Park, a new framework was proposed to improve business intelligence of the companies envisaging knowledge management approaches.

E-Commerce Status Analysis

This project was related to analyzing e-commerce status with respect to economical growth. In this project, the focus was mainly on analyzing any consequential impact of new e-commerce on economical growth, or how economical condition reacts to introduction of new e-commerces, and what are the main challenges for them.

Benchmarking on Evaluational Metrics of XAI on Graph Neural Networks

While doing a benchmarking on explicability methods of graph neural networks, it was felt that a benchmarking on the evaluation metrics of interpretability methods is also missing. In this project, the goal is to not only gather all the evaluation metrics of interpretability methods together, but also to analyze which evaluation metric/s work best for which interpretability method.