I'm a second year CS PhD student at Illinois Institute of Technology (IIT). I am fortunate to be advised by Dr. Binghui Wang, as a research assistant at IIT. Previously, I joined Mayo Clinic(the Top-ranked Hospital in the Nation, which is a professional environment to be in and learn) as an AI Research Scientist Intern at the Department of Artificial Intelligence and Informatics last summer. The project was about studying the cause and effect behind transition from Mild cognitive impairment to dementia by biomarkers and chronic diseases.

Before, I worked as an AI product engineer at RegTech Startup-studio in Tehran. Before this, I also spent some fantastic time at Mobarakeh Steel Company, on the problem of real-time fault detection for Steel cold rolling engine bearings by deep learning methods.

I am on the editorial board of American Journal of Lifestyle Medicine, and a peer-reviewer of The Journal of Primary Prevention, JAMA Network Open, and Journal of General Internal Medicine.

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I am glad to be surrounded by excellent collaborators and mentors who helped me push beyond my boundaries. This blog is for sharing and gaining knowledge. I will post about my projects, interests, and what I do.

I am broadly interested in redefining causal Machine Learning (The knowledge of cause and effect) to artificial intelligence as a backbone for theoretical computer science. I am also applying my new algorithms and frameworks on real-world datasets to explore the information within images, graphs, social networks, and protein chains. More technically, I am now working on causal representation learning for language models.

• Description: Explore potential causal explanation of MCI progression by temporal patient data, including chronic diseases, biomarkers, and genetic information, into a graph structure to capture causal effects within variables.
• Outcome: Identified a causal subgraph with informative variables including hypertension, arrhythmia, congestive heart failure, coronary artery disease, stroke, lipid-related issues, and sex.

• Description:A GNN causal explainer based on a graph's causal structure and its corresponding neural causal model. It correctly does the graph classification via causal inference (based on interventional data) by understanding and quantifying cause-and-effect relations between observable variables.
• Outcome: it's the first GNN causal explainer. We leverage the neural-causal connection, design the GNN neural causal models, and train them to identify the causal explanatory subgraph. Our results show the effectiveness of CXGNN and its superiority over the state-of-the-art association-based and causality-inspired GNN explainers

• Description: From the beginning of the outbreak until April 30, 2020, over 90,000 confirmed cases of COVID-19 have been reported in Iran. Due to socio-economic problems of this disease, it is required to predict the trend of the outbreak and propose a beneficial method to find out the correct trend.
• Outcome: A dataset including the number of confirmed cases, the daily number of death cases, and the number of recovered cases is compiled. New rates such as weekly death rate, life rate, and new approaches to mortality rate and recovery rate are created by Gaussian functions.

• Description: Breast cancer is the second reason for cancer mortality. Approximately 30%- 40% of patients suffering from breast cancer will experience recurrence and 10%-15% of them were reported to die of cancer metastasis. Early diagnosis or prediction of metastasis will reduce mortality rate and treatment cost.
• Outcome: Multi-Layer Perceptron Outperforms other methods to predict metastasis.

• Description: Different tumor features are available in various datasets for breast cancer detection. Filtering those to obtain an accurate diagnosis is time-consuming and challenging. Machine learning algorithms are beneficial for finding a significant relationship between various features and malignant tumors.
• Outcome: The suggested framework’s performance works perfectly due to the selection of more appropriate features by the Extra Trees algorithm. Using this framework, breast cancer recovery and therapy will be more successful. Moreover, to evaluate the performance of the proposed framework, it has been implemented on three other medical datasets.

• A great need is felt to analyze the applicability of AI-enabled IoT technologies in energy systems in response to the call for smart and autonomous architecture for multicarrier energy grids. Due to this, the current chapter gives an informative description of new AI-based IoT frameworks concentrating on architecture, elements, variables, applications, and also its challenges. At first, an insight into IoT basics is provided that helps readers to go through the next sections. The required infrastructure for deploying the aforementioned technologies is discussed, explaining electronic and IoT components. Energy internet as the IoT in energy grid’s output has tremendous features that are appropriate for the grid’s performance evaluation, which is explained in this chapter.

• With the development of new artificial intelligence and data science (DS) technologies, their applications for implementing analysis in the Internet of Things (IoT) energy systems are becoming more important for smart grids (SGs). DS approaches integrated with IoT data collection protocols in energy sectors can help in improving efficiency in such areas. Data gathering by sensors is an important step in IoT-based energy grids when it comes to a large amount of data. In the real-time data collection process, the frequency of data rises to become big data and the analysis needs new methods to manage and evaluate this data. The outcome of these analytics comes up as SG intelligence so the system becomes smart, which is depicted as demographics, figures, and informative dashboards.