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Wassim Chouachi

@wassimchouachi

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I build AI models for computer vision, image processing, and large-scale data analysis.

China

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What I'm looking for

I'm seeking a junior opportunity in robotics, computer vision, perception, SLAM, or AI engineering where I can apply deep learning, image processing, and algorithmic research skills.

I'm pursuing a Master's in Artificial Intelligence at NUIST after completing Artificial Intelligence studies, with hands-on work in computer vision, image processing, deep learning, and algorithmic research.

In NUIST's Image Processing Laboratory, I researched block-matching and 3D collaborative filtering for image denoising. I designed MNIST and CIFAR-10 experiments, evaluated results with PSNR and SSIM, and published the work in IJIRCCE in 2025.

I've also built a multi-task BERT framework for supply-chain crisis detection and a sentiment-analysis pipeline for approximately 50,000 social-media records, using Python, PyTorch, Transformers, and classical machine-learning models.

Experience

Work history, roles, and key accomplishments

Multi-Task BERT for Supply Chain Crisis Detection

NUIST

Jan 2025 - Jun 2026 (1 year 5 months)

Developed a multi-task deep learning framework for risk prediction, crisis classification, and sentiment analysis from global news. Processed large-scale text data and implemented the model using PyTorch and BERT/Transformers.

PyTorch BERT Transformers Deep Learning NLP Focal Loss Class Imbalance Risk Prediction Sentiment Analysis

Undergraduate Researcher

Image Processing Laboratory

Sep 2023 - Jan 2025 (1 year 4 months)

Conducted research on block-matching and 3D collaborative filtering for image denoising. Designed experiments on MNIST and CIFAR-10 and evaluated image quality using PSNR and SSIM.

Python Matlab SQL PyTorch OpenCV Image Processing Experimental Design PSNR SSIM

NLP Sentiment Analysis System

NUIST

Oct 2024 - Dec 2024 (2 months)

Built an end-to-end sentiment classification pipeline and preprocessed approximately 50,000 social-media text records. Implemented Naive Bayes, SVM, and Logistic Regression; achieved 85% binary classification accuracy.

Python NLP Naïve Bayes SVM Logistic Regression Sentiment Analysis