Making sense of the future of AI.
Thoughts on artificial intelligence, emerging technology, and the ideas shaping our world. I explore important AI developments, share my perspective on the news, and document my research journey.
My Perspectives
Independent commentary and accessible explanations of AI, deep learning, agents, and technology's wider impact.
Explore essays →AI News & My Take
Noteworthy reporting from established outlets, paired with my analysis and links to the original journalism.
Explore commentary →Research & Publications
My research interests, publications, selected projects, and work in machine learning and healthcare AI.
View research →A personal publication by Md. Nazmussakib. Opinions are my own; linked reporting remains the work of its original publisher.
About
Education
Doctorate in Computer Science
2025-11-01
University of the Potomac, Washington, DC
MS Information Technology (Software Design and Management)
2022-09-01
2024-10-31
Washington University of Science and Technology
BA Public Administration
2017-01-01
2021-03-31
Bangladesh University of Professionals (BUP)
Research Interests
Academic Resume
Explore my education, research publications, applied projects, and technical skills in my updated academic resume.
↓ View or Download Resume (PDF) →I study applications of artificial intelligence and deep learning in healthcare, especially medical image classification and AI-assisted cancer detection. My proposed research explores combining local and global visual features, improving generalization across diverse datasets, and making model decisions more interpretable.
Areas of interest: computer vision, computational pathology, medical imaging, multi-scale representation learning, attention-based fusion, explainable AI, and robust machine learning.
This research agenda is under development; no experimental findings are claimed.
My academic work includes 5 journal publications and 5 IEEE conference papers spanning healthcare AI, medical image analysis, predictive analytics, and robust machine learning.
- Medical Image Classification Research Prototype — ResNet50 feature extraction, PCA, and classical/variational quantum classifiers; training and validation pending.
- Chicken Disease Image Classification — Image classification pipeline and experiment workflow.
- Data Analytics Portfolio — Python, SQL, exploratory analysis, and predictive-modeling notebooks.
- End-to-End Machine Learning Pipeline — Reproducible ML project structure and notebooks.
Business Data Analyst Trainee, Global Systems LLC (Dec 2024–Aug 2025): dashboard reporting, KPI analysis, market and competitor research, and data quality validation.
Data Analyst, Beatnik Technology Ltd. (Mar–Dec 2021): data cleaning and analysis using SQL, Python/R, and spreadsheets; Tableau and Power BI reporting.
Technical skills: Python, R, SQL, NumPy, Pandas, Matplotlib, data cleaning, feature engineering, statistical analysis, model evaluation, Jupyter Notebook, Kaggle, GitHub, Power BI, Tableau, and Excel. Computer vision, CNNs, attention, explainability, and robustness are areas of active study.