Kishore Arul

I’m Kishore Arul, a Senior Data Engineer with extensive experience working across healthcare, retail, and manufacturing domains. I specialize in designing scalable data pipelines, integrating complex datasets—such as EHR and clinical trial data—and ensuring compliance with data privacy regulations like HIPAA. My work emphasizes building cloud-native, secure, and efficient data architectures that support large-scale analytics and machine learning applications.

Beyond my industry work, I am passionate about applied research. I’ve authored and presented multiple papers focused on AI applications in healthcare and data engineering, including:
• Advancing Diabetes Risk Prediction Using ANFIS: A Fuzzy-Logic Approach for Improved Healthcare Decision Support – AIMLA 2025 (DOI: 10.1109/aimla63829.2025.11040564)
• Prognostic Modeling of Lung Disease Hospitalizations Using LSTM Networks to Measure the Impact of Air Degradation – AIMLA 2025 (DOI: 10.1109/aimla63829.2025.11040210)
• Energy-efficient Data Engineering Practices for Big Data Workloads in Cloud Infrastructure – Journal of Current Science Research and Review (2023)
• Data Engineering Challenges in Multi-cloud Environments – IJSRM (DOI: 10.18535/ijsrm/v10i6.ec08)
• Optimizing Data Pipelines in Cloud-based Big Data Ecosystems – IJECS, 2021

Over the years, I’ve led teams of 5–10 members, driven data platform modernization, and collaborated on AI/ML initiatives that support better decision-making and operational efficiency. I’m always eager to contribute to the broader community by sharing insights and learning from others in the field.