Research & Publications
Cloud architecture researcher with peer-reviewed papers in algorithmic trading, distributed systems, and cloud-native messaging. Current focus: AI/ML integration, high-throughput systems, and next-generation cloud platforms.
Research philosophy: bridge theoretical models and practical implementation by researching in production environments and sharing insights enterprise architects and engineers can act on.
| Papers | Published | In progress | Journals |
|---|---|---|---|
| 3 | 2 | 1 | IJECS, IJARCSMS |
Published Papers
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), Vol. 14 No. 11 |
| Published | November 24, 2025 |
| DOI | 10.18535/ijecs.v14i11.5335 |
Integrates supervised machine learning with hybrid technical indicators (VWAP, MACD, RSI, Bollinger Bands) and dynamic risk management for momentum trading. Returns are 14% higher and drawdown 7% lower than conventional rule-based approaches, and the Sharpe ratio improves from 0.8 to 1.7.
- 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
- Data validation across multiple market data providers
Keywords: algorithmic trading, machine learning, momentum trading, technical indicators, risk management, real-time systems, VWAP, quantitative finance
Read on Site | Read Full Paper | Download PDF
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), Vol. 13 Issue 11 |
| Published | 2025 |
| DOI | 10.61161/ijarcsms.v13i11.1 |
Empirical performance analysis of high-throughput messaging on Azure Kubernetes Service (AKS) and Azure Event Hub in a publish-subscribe model. Shows how cloud-native designs handle large-scale ingestion and real-time event streaming with minimal latency and 99.9%+ reliability.
- Scalability, throughput, and latency evaluated under varying load
- Operational efficiency through Kubernetes auto-scaling and partition-based distribution
- Best practices for observability, security, and automation
- Reference architecture for mission-critical enterprise applications
Keywords: cloud-native architecture, microservices, Kubernetes, Azure Event Hub, pub/sub, event-driven systems, distributed systems, high-throughput processing
Read on Site | Read Full Paper | Download PDF
In Progress
Integrating AI Agent Frameworks with Existing Microservices: An Adapter-Based Architecture for Enterprises
| Status | Work in progress, internal review |
An architectural framework for integrating AI agent systems with existing microservices, so enterprises can adopt intelligent automation through evolutionary rather than revolutionary modernization. It addresses authentication propagation, distributed tracing, security compliance, and performance, and supports multiple agent frameworks (LangChain, Semantic Kernel, Agent Framework).
- Hybrid architecture for AI-microservices integration with standardized adapter patterns
- Validated design patterns for service wrapping and agent tool development
- Production implementation in financial services with 40% reduction in API orchestration complexity
- Security evaluation confirming SOC 2 and PCI DSS compliance
Keywords: AI agents, microservices architecture, enterprise integration, large language models, API gateway, Model Context Protocol, LangChain, Semantic Kernel, financial services
Research Areas
| Established | Emerging |
|---|---|
| Cloud-native architecture patterns and distributed systems design | AI agents for cloud operations and automation |
| Algorithmic trading systems with AI/ML integration | Performance optimization of Kubernetes-based systems |
| Microservices design and event-driven architectures | Integration patterns for Azure OpenAI Service |
| High-throughput messaging and streaming platforms | WebAssembly and serverless architectures |
| Real-time data processing at enterprise scale | Zero-trust security in Kubernetes |
Where Research Meets Practice
- Academic: peer-reviewed journals, DOI-indexed for citation and verification
- Industry: performance analysis from production environments, reference architectures for mission-critical systems, and compliance-aware designs for regulated industries such as financial services
Future Directions
- Advanced AI/ML integration: next-generation models for cloud optimization
- Edge computing: hybrid cloud-edge architectures for real-time processing
- Sustainable cloud computing: energy-efficient patterns for large-scale systems
- Financial technology: trading system architectures with regulatory compliance
Collaborate
Interested in collaborating on cloud architecture, distributed systems, or algorithmic trading research?