I am Abu Humayed Azim Fahmid, a dedicated Lecturer in the Department of Computer Science and Engineering at United International University. I have graduated from the Bangladesh University of Engineering and Technology (BUET) in 2024. I have a robust theoretical foundation in computer science and I am skilled across a variety of programming languages.

My practical virtual internship played a significant role in enhancing my software development skills, providing hands-on full-stack development experience.

As an educator, I strive to create an engaging learning environment that imparts knowledge and inspires curiosity among my students. My passion for teaching and research is complemented by my commitment to the academic and personal growth of my students. I believe in the power of education to transform lives. I am dedicated to contributing to the development of the next generation of computer engineers and computer scientists.

I am involved in research and projects within the domains of Cybersecurity, Machine Learning, and Bioinformatics. I am particularly passionate about contributing to the field of Computational Biology, with a special focus on Computational Genomics. My undergraduate thesis, Structural Variant Calling in Genome using Deep Learning, focused on developing a structural variant caller using deep learning methodologies to enhance the detection of structural variants in genomes. This work involves applying advanced machine learning techniques to analyze genetic data, aiming to identify variations that could have significant implications for understanding genetic disorders and variations. Under the guidance of Dr. Atif Hasan Rahman, an Associate Professor at BUET, my thesis represents a crucial step in improving the accuracy and efficiency of genetic variant analysis, contributing valuable insights to the field of Computational Genomics.

I am currently working on the project Psychosis Classification using rsfMRI under the supervision of Dr. Mohammad Saifur Rahman, Professor at BUET. In this research project, I have developed a modified Connectome Convolutional Neural Network (CCNN) model to differentiate between schizophrenia and bipolar disorder from a connectome-based dataset. Various machine learning models and feature engineering techniques were used in this project. The model has demonstrated a competitive edge and potential to produce promising outcomes in connectome-based datasets so far. I want to work with the raw neuro-images to create a better connectome-based dataset from scratch and then apply the model on the dataset for the next step of this project.

Some of my other research works include the development of an Incident Response pipeline for Container-based Systems and Environments, studying and analyzing the impact of roadside objects on traffic flow and developing a dynamic route-guiding system that is suited to Dhaka City's heterogeneous traffic conditions.