MSc Artificial Intelligence student at University of Amsterdam, passionate about developing ML solutions for real-world problems.
I am Mohammad Hafeez Khan, currently pursuing my MSc in Artificial Intelligence at the University of Amsterdam (Sep 2024 - Sep 2026). My research interests lie at the intersection of Machine Learning, Natural Language Processing, and Computer Vision.
I have a strong foundation in data science and AI from my previous MSc in Data Science & Artificial Intelligence at Queen Mary University of London, where I graduated with Distinction. My work focuses on developing transformer-based models and deep learning solutions, particularly for low-resource language translation and computer vision applications.
I am passionate about solving complex problems through data-driven approaches and am particularly interested in making AI technologies more accessible and beneficial for underrepresented languages and communities.
Developed a transformer-based NMT system using PyTorch and HuggingFace for translating low-resource Indian languages (Bodo, Dogri, Maithili, and Konkani). Implemented transfer learning techniques and achieved significant improvements in BLEU scores compared to baseline models.
Designed and implemented a custom CNN architecture achieving 90% accuracy on CIFAR-10 dataset. Explored various optimization techniques including batch normalization, dropout, and data augmentation. Conducted extensive hyperparameter tuning to optimize model performance.
Currently exploring the application of vision transformers and CNNs for medical image segmentation and classification tasks. Focus on developing robust models for disease detection with limited labeled data.
Programming Languages: Python, R, SQL, JavaScript, HTML/CSS
ML/DL Frameworks: PyTorch, TensorFlow, HuggingFace Transformers, Scikit-learn, Keras
Research Areas: Natural Language Processing, Computer Vision, Deep Learning, Transfer Learning, Multilingual Models