PhD in Computer Science . I am having 13+ years of university teaching and research experience, specializing in deep learning, computer vision, biomedical image analysis, neural network design, multimodal information fusion, optimization, and explainable AI. Experienced in designing, implementing, training and evaluating customized deep neural architectures for medical image segmentation and classification, including DeepLabV3+, CNNs, residual/bottleneck networks, attention mechanisms, stacked autoencoders and hybrid models. Hands-on experience with Python-based AI development, PyTorch, TensorFlow/Keras, scikit-learn, NumPy, Pandas, OpenCV, Jupyter, Git and GPU/CUDA workflows. Currently supervising finalyear students developing AI-based healthcare solutions using these technologies. Research interests include high-content cellular imaging, phenotypic representation learning, multimodal biological data integration, computational biology and AIdriven drug discovery.

• AI Based solutions in Health care, Geography, Agriculture and Human Activity Recognition.