I am currently a Graduate Teaching Assistant and Ph.D. student in the Department of Computer Science at the University of Texas at San Antonio, working under the supervision of Dr. Zijie Zhang. I am doing research in Explainable Artificial Intelligence (XAI) and Trustworthy Machine Learning with a focus on Medical Imaging, bringing together knowledge from Computer Vision, Machine Learning, and Deep Learning. My research is inherently multidisciplinary, combining theory with practical implementation to solve emerging challenges in latest AI trends. Before joining UTSA in the Spring of 2025, I served as a Senior Software Engineer in Bangladesh for about 3 years.

I completed my B.Sc. in Computer Science and Engineering from Khulna University of Engineering & Technology (KUET). My undergraduate thesis focused on brain hemorrhage classification and segmentation from head CT scan images, where I employed deep learning and image processing techniques under the guidance of Prof. Dr. Sk. Mohammad Masudul Ahsan.

Previously, I interned at Intelligent Machines Limited, working on computer vision projects. Other than that I gained experience in various projects involving machine learning, deep learning, and data visualization. Beyond my professional pursuits, I am an avid traveler. I have summited the highest peak of Bangladesh, witnessed the highest waterfall of Bangladesh, and trekked the mesmerizing Goecha La in Yuksam, India, with plans for more adventures in the future.

Check out my GitHub page.

The following are broadly my current research interests. See also my list of publications.

• Explainable AI
• Trustworthy Machine Learning
• Deep Learning
• Computer Vision
• Medical Image Analysis

The best way to contact me is by email: abunomanmd.sakib@gmail.com.

1. Segmentation of Hemorrhagic Areas in Human Brain from CT Scan Images

   In 4th International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, 2023

   Brain hemorrhage is potentially a fatal condition that results from internal bleeding in the human brain. In this study, Computed Tomography (CT) scan images have been used for segmentation tasks to pinpoint the area of hemorrhage. Unique data augmentation techniques using non-linear transformations like, Twirl and Spherical have been used along with traditional data augmentation techniques to increase variation in the dataset. The hemorrhagic portion of the brain in images that are easy to distinguish have been annotated to perform the segmentation task. The segmentation task was applied using U-Net and U-Net++ architecture. U-Net architecture has shown 84.33% Intersection over Union (IoU) and 91.34% dice coefficient score whereas U-Net++ has achieved 17.06% IoU and 28.27% dice coefficient score after applying some non-linear transformations on the dataset.

2. Classification of Brain Hemorrhage Using Deep Learning from CT Scan Images

   Nipa Anjum, Abu Noman Md. Sakib, and Sk. Md. Masudul Ahsan

   In Proceedings of International Conference on Information and Communication Technology for Development, Singapore, 2023

   Brain hemorrhage is a life-threatening problem that happens by bleeding inside human head. In this study, computed tomography (CT) scan images have been used to classify whether the case is hemorrhage or non-hemorrhage. Different convolutional neural network (CNN) models have been observed along with some pre-trained deep learning models such as VGG16, VGG19, ResNet150, ResNet152 and InceptionV3. Pre-trained models have performed well on the dataset but all of them are heavyweight architectures in terms of number of total parameters. But the proposed model is a lightweight architecture as well as a well performing one. After evaluating the model performance, it has been observed that the proposed model gave 96.67% accuracy, 97.08% sensitivity and 96.25% specificity which is the best among other custom CNN models.