Shakil Mahmud Shuvo

About Me

As a passionate Machine Learning Researcher, I thrive on pushing the boundaries of AI to solve real-world challenges. My research journey has led me through diverse areas of machine learning, from medical imaging and natural language processing to computer vision, resulting in 6 peer-reviewed publications where I’ve contributed as both lead and collaborative researcher.

My professional experience spans both academia and industry. Currently, I work as an Associate Data Scientist at Cognitus Consulting LLC, where I architect large language model solutions for Fortune 500 clients. This role has deepened my understanding of practical AI deployment while reinforcing my belief that the most impactful research bridges theoretical innovation with real-world application.

Research Philosophy

I believe in the transformative power of machine learning across domains. While my published work focuses on healthcare AI, I'm eager to explore new frontiers in ML - from reinforcement learning and generative models to emerging paradigms in AI. Every new challenge is an opportunity to learn, adapt, and innovate.

My academic foundation was built at Rajshahi University of Engineering & Technology (RUET), where I earned my B.Sc. in Computer Science & Engineering. During my time there, I served as a research assistant at the Young Learners’ Research Lab, where I discovered my passion for pushing the boundaries of what’s possible with AI.

Research Journey & Aspirations

Throughout my research career, I’ve developed expertise in:

Medical AI Systems: Creating innovative architectures for brain tumor classification, retinal disease detection, and glioma segmentation
NLP Applications: Building BERT-based models for mental health applications, including early detection of suicidal ideation
Computer Vision: Implementing attention mechanisms and fusion techniques for enhanced image analysis
Cross-Domain Learning: Applying transfer learning and ensemble methods across different problem spaces

What excites me most is the unexplored potential of machine learning. I’m actively seeking PhD opportunities where I can contribute to cutting-edge research while continuously expanding my horizons. Whether it’s developing novel architectures, exploring theoretical foundations, or applying ML to entirely new domains, I approach each challenge with curiosity and determination.

Looking Forward

I'm particularly interested in emerging areas like federated learning, multimodal AI, explainable AI systems, and the intersection of ML with other fields. My goal is not just to advance the state-of-the-art, but to ensure AI technologies are accessible, interpretable, and beneficial for all.

View My Research

Selected Papers

Early Detection of Suicidal Ideation Using Bidirectional GRU and Language Models

Shakil Mahmud Shuvo, Navia Novely, Md. Farukuzzaman Faruk, Azmain Yakin Srizon, S. M. Mahedy Hasan

Proceedings of the 3rd International Conference on Computing Advancements (ICCA), 2024

First Author
Abstract Paper

Suicide has recently emerged as a leading cause of death worldwide, underlining the importance of effective preventative measures. Online social media posts can provide valuable insights into people who are suicidal and assist in preventing unfortunate outcomes. However, the problem lies in the complexities of such posts, as people may be hesitant to share their distressing thoughts due to different psychological and societal constraints. This study examined the utilization of Bidirectional GRU, a type of recurrent neural network that analyzes data sequences in both forward and backward directions, to improve text classification using language models. This study improves textual analysis by incorporating Bidirectional GRU (Bi-GRU) layers with popular pre-trained language models like BERT, RoBERTa, DistilBERT, DistilRoBERTa, and ELECTRA-Small. The dataset used for this research is sourced from Reddit. The BERT-BiGRU and DistilBERT-BiGRU models demonstrated notable effectiveness, achieving accuracies of 95.8% and 95.2% respectively. These models also exhibited remarkably low false negative rates, with BERT-BiGRU at 4.17% and DistilBERT-BiGRU at 2.80%. These findings show that the combination of Bi-GRU and pre-trained language models considerably improves early detection of suicidal ideation, paving the path for prompt and perhaps lifesaving interventions.
Improving Pre-Trained CNNs with CBAM and Skip Connections for Multi-Class Retinal Diseases Classification using OCT Images

Navia Novely, Shakil Mahmud Shuvo, Md. Farukuzzaman Faruk

Proceedings of the 3rd International Conference on Computing Advancements (ICCA), 2024

Second Author
Abstract Paper

Millions of people suffer from retinal defects over the world. Early discovery and treatment of these anomalies could halt further progression, saving many people from preventable blindness. Disease detection through manual methods is a tedious procedure that is both time-consuming and cannot produce consistent results. Building on the groundwork laid by Deep Convolutional Neural Networks (DCNNs) in computer-aided diagnosis (CAD), there have been initiatives to automate the detection of retinal diseases. This study presented a hybrid framework that utilizes pre-trained models (DenseNet121, ResNet50, VGG16, Xception, and EfficientB1) incorporating the Convolutional Block Attention Module (CBAM) and skip connections for accurate retinal disease classification. The CBAM block improved retinal disease classification by focusing on important features in OCT images. Skip connections were implemented to facilitate the direct transfer of feature information between layers. This study utilized the OCT-C8 dataset, which comprises seven disease classes and one healthy class. The DenseNet-CBAM-Skip and Xception-CBAM-Skip architectures were particularly noteworthy for their outstanding performance, with DenseNet obtaining a high accuracy of 96.28% and Xception 96.11%. They also acquired a significant F1-score of 96.31% and 96.11% respectively, making it promising for real-time application to help ophthalmologists.

Want to explore more of my research?

View All Publications in Research Page