I am a student of Daffodil International University and have acquired the priceless experience that has made me what I am in Software Engineering. Labs and group assignments helped me gain hands-on skills that are a combination of both theory and practical. It is worth noting that the OOP, Data Structures, and Algorithms labs enhanced my programming skills and problem-solving strategies, and the Robotics lab taught me the practical skills of the underlying hardware fundamentals. Moreover, the Software Project course has enabled me to work in teams, project management, and innovation, which prepares me to face complicated challenges as a team. Such experiences have not only been able to increase my technical competence,  but also contributed to my eagerness to learn and develop. The courses before the journey as a student are many, and I am highly optimistic about them.

Hands-on experience in applying deep learning and machine learning to both computer vision and neurotechnology problems. Built and evaluated CNN-based models for age and gender prediction using the UTKFace facial image dataset, focusing on accuracy, robustness, and fairness across different age, gender, and ethnic groups. The work included systematic bias analysis and the use of explainable AI techniques to visualize which facial regions influenced the model’s decisions, helping to interpret predictions and identify potential bias in the learned representations.​ In parallel, contributed as a co-author to a Scopus-indexed journal article titled “EEG-Based Neurofeedback for ADHD in Children: Enhancing Attention and Reducing Impulsivity Using Machine Learning.” This research involved collecting and processing EEG data from children with ADHD, performing feature extraction and channel selection, and training multiple classifiers to support diagnosis and evaluate neurofeedback as a non‑pharmacological intervention. The study demonstrated that combining EEG-based neurofeedback with machine learning can improve attention control and reduce impulsive behavior, highlighting the potential of data-driven methods in clinical decision support for pediatric ADHD.