Research & Publications
Overview
Cloud architecture researcher with published peer-reviewed papers in algorithmic trading, distributed systems, and cloud-native messaging architectures. Active research focus on AI/ML integration, high-throughput systems, and next-generation cloud platforms.
Research Philosophy: Bridging the gap between theoretical models and practical implementation by conducting research in production environments and sharing actionable insights for enterprise architects and engineers.
Published Scholarly Articles
AI-Driven Algorithmic Trading: Integrating Machine Learning, Hybrid Technical Indicators, and Risk Management for Momentum Strategies
Journal: International Journal of Engineering and Computer Science (IJECS)
Volume: Vol. 14 No. 11 (2025) | Published: November 24, 2025
DOI: https://doi.org/10.18535/ijecs.v14i11.5335
Research Contribution:
Novel integration of supervised machine learning with hybrid technical indicators (VWAP, MACD, RSI, Bollinger Bands) and dynamic risk management for momentum trading. Demonstrates 14% higher returns and 7% lower drawdown compared to conventional rule-based approaches, with Sharpe ratio improvement from 0.8 to 1.7.
Key Innovations: - Sub-second latency trading system with 67% accuracy (vs. 54% baseline) - Momentum-specific signal fusion with adaptive weighting - Integrated real-time risk management with momentum-aware controls - Comprehensive data validation across multiple market data providers
Keywords: Algorithmic trading, machine learning, momentum trading, technical indicators, risk management, hybrid signals, real-time systems, VWAP, artificial intelligence, quantitative finance
📖 Read Full Paper | 📥 Download PDF | 🔗 View DOI
High-Throughput Cloud-Native Messaging Architectures: Design and Performance Analysis of Pub/Sub Microservices with Kubernetes and Azure Event Hub
Journal: International Journal of Advance Research in Computer Science and Management Studies (IJARCSMS)
Volume: Vol. 13 Issue 11 | Published: 2025
DOI: https://doi.org/10.61161/ijarcsms.v13i11.1
Research Contribution:
Comprehensive empirical performance analysis of high-throughput cloud-native messaging architectures using Azure Kubernetes Service (AKS) and Azure Event Hub within a publish-subscribe model. Demonstrates how cloud-native designs effectively handle large-scale data ingestion and real-time event streaming with minimal latency while maintaining 99.9%+ reliability.
Key Findings: - Systematic evaluation of scalability, throughput, and latency under varying load conditions - Operational efficiency through Kubernetes auto-scaling and partition-based distribution - Best practices for integrating observability, security, and automation - Reference architecture for mission-critical enterprise applications
Keywords: Cloud-Native Architecture, Microservices, Kubernetes, Azure Event Hub, Messaging Architecture, Pub/Sub, Event-Driven Systems, Distributed Systems, High-Throughput Processing
📖 Read Full Paper | 📥 Download PDF | 🔗 View DOI
Research Interests
Primary Research Areas
- Cloud-native architecture patterns and distributed systems design
- Algorithmic trading systems with AI/ML integration
- Microservices design and event-driven architectures
- High-throughput messaging and streaming platforms
- Real-time data processing at enterprise scale
Emerging Research Focus
- AI agents for cloud operations and automation
- Performance optimization of Kubernetes-based systems
- Integration patterns for Azure OpenAI Service
- Next-generation platform capabilities (WebAssembly, serverless architectures)
Active Research Projects
Current Investigations
- AI-Powered Cloud Infrastructure Automation: Exploring intelligent agents for infrastructure management and optimization
- High-Performance Event Streaming: Advanced patterns for real-time data processing in financial services
- Cloud-Native Security: Zero-trust architecture implementations in Kubernetes environments
Research Impact
Academic Contributions
- Published in peer-reviewed international journals
- DOI-indexed publications for citation and verification
- Bridging gap between theoretical models and practical implementation
- Providing actionable insights for cloud architects and engineers
Industry Applications
- Real-world performance analysis from production environments
- Best practices for enterprise-scale deployments
- Reference architectures for mission-critical systems
- Compliance-aware designs for regulated industries (financial services)
Future Research Directions
- Advanced AI/ML Integration: Next-generation machine learning models for cloud optimization
- Edge Computing Patterns: Hybrid cloud-edge architectures for real-time processing
- Sustainable Cloud Computing: Energy-efficient architecture patterns for large-scale systems
- Financial Technology Innovation: Advanced trading system architectures with regulatory compliance
Collaboration Opportunities
Interested in collaborating on research in cloud architecture, distributed systems, or algorithmic trading?