I am a deep learning and computer vision researcher with a strong interest in applying AI to real-world problems, particularly in healthcare and other data-intensive domains. I have extensive experience applying advanced deep learning models, including CNNs, Transformers, and diffusion models, to real-world data. My work combines core deep learning techniques with domain-specific challenges, where I develop models for tasks such as medical image analysis, visual recognition, and decision support systems. I am driven by the potential of AI to improve decision-making, enhance diagnostic accuracy, and support impactful research across disciplines, from clinical diagnostics to general computer vision problems.

During my master’s degree, I worked on

• Disease classification using tranformers and CNNs
• Hip fracture detection for clinical use with multi-scale learning

Research Interests

• Computer Vision and Deep Learning
• Transformers and Large Language Models (LLMs)
• Representation Learning
• Medical Imaging
• Explainable AI and Clinical Decision Support Systems

Technical Skills

• Programming: Python, C++
• ML Frameworks & Tools: PyTorch, TensorFlow, Keras, Huggingface, timm, Scikit-learn, OpenCV, NumPy, Pandas, Matplotlib, Jupyter Notebook, CUDA, Git
• Deep Learning and Computer Vision: Transformers, CNNs, Diffusion models, Image Classification, Segmentation, Detection
• Other Skills: Explainable AI (Grad-CAM), Data preprocessing, Data visualization, Experimental benchmarking, Scientific writing