Kaysarul Anas Apurba
CV
M.Sc. Computational Sciences · Laurentian University
I am an independent ML researcher and PhD applicant with an M.Sc. in Computational Sciences from Laurentian University, Canada (CGPA 9.10/10). My work spans medical image analysis, NLP, and network security — with a focus on building systems that are both accurate and deployable in low-resource settings.
My research interests include Information Retrieval, Large Language Models, Adversarial Robustness, and Medical Image Analysis. I am currently working on RAG systems hardened against retrieval poisoning and prompt injection, and automated malaria cell segmentation from blood smear images.
I am actively engaged in several ongoing collaborative projects. For a closer look at my recent and current work, please visit my Research page.
A Non-Invasive Cloud-Based Migration Strategy for Post-Quantum Cybersecurity in Smart HVAC Systems
IEEE Symposium on Security and Privacy (S&P) 2027 Submitted Under Review 2027
Legacy smart HVAC systems suffer from critical quantum vulnerabilities due to their reliance on classical ECDH. We propose a non-invasive, cloud-based proxy architecture that integrates NIST-standard post-quantum cryptography (ML-KEM and ML-DSA) into the ecosystem without modifying legacy hardware or firmware, providing a deployable migration pathway to quantum-safe security.
MalariAI: Automated Malaria Cell Segmentation and Classification from Blood Smear Images
Computerized Medical Imaging and Graphics (CMIG) Manuscript 2026
We propose MalariAI, a two-stage pipeline combining watershed-based cell segmentation with EfficientNet-B0 classification for automated malaria detection from blood smear images. Evaluated on NIH BBBC041 and MP-IDB datasets, achieving 75.95% cell recovery rate and 98.36% classification accuracy. Includes Grad-CAM++ visualizations and cross-dataset validation.
Accurate Prediction of Pulmonary Fibrosis Progression Using EfficientNet and Quantile Regression: A High Performing Approach
IEEE Region 10 Symposium (TENSYMP 2023) Conference 2023
Accurate prediction of pulmonary fibrosis progression is crucial for effective patient management. This study proposes an efficient deep learning framework for predicting the progression of pulmonary fibrosis using high-resolution computed tomography (HRCT) images. We leverage the EfficientNet architecture, known for its high accuracy and computational efficiency, to extract discriminative features from CT scans. To capture the uncertainty inherent in disease progression, we employ quantile regression instead of standard mean-based regression. This approach allows us to model the conditional distribution of future lung function, providing not only a point prediction but also prediction intervals that quantify the uncertainty associated with the prognosis. Our experiments on a benchmark dataset demonstrate that the proposed EfficientNet-based quantile regression model achieves state-of-the-art performance, outperforming existing methods in predicting pulmonary fibrosis progression while providing reliable uncertainty estimates.