I build data pipelines, backend systems and efficient AI, and I measure all of it.

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Open to collaborating on open-source, data engineering and AI systems projects.

🔭 Building now DataForge: data pipeline orchestration AssetSense: predictive maintenance AdaptiLLM: adaptive on-device inference

🌱 Exploring Spark-based ETL LLM quantization and edge inference Model deployment and MLOps Distributed systems

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⚡ Live from GitHub

Refreshed every 6 hours by a GitHub Action in this repo. No third-party servers.

Latest activity

• ⬆️ Pushed to DailyForge (main) · _{18 Oct 2026}
• ⬆️ Pushed to geeky-rish (main) · _{18 Oct 2026}
• ⬆️ Pushed to Labs (yolo-reviewer-test-2) · _{18 Oct 2026}
• 🔀 Merged PR #6 in Labs · _{18 Oct 2026}
• 🔀 Opened PR #6 in Labs · _{18 Oct 2026}

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🚀 Featured projects

⚙️ DataForge Data pipeline orchestration platform: dataset ingestion, Spark transformations, validation and async job execution. FastAPI Apache Spark MinIO PostgreSQL Docker Reusable ETL workflows Schema enforcement and validation Object storage with metadata tracking Containerized service orchestration	📡 AssetSense Industrial monitoring that turns real-time sensor telemetry into anomaly alerts and equipment health scores. ESP32 C++ MQTT Node.js MongoDB React Vibration, temperature and current sensing Anomaly detection and health scoring Remaining Useful Life (RUL) prediction Live telemetry dashboard
🧠 AdaptiLLM On-device LLM inference that adapts its execution settings to query complexity and device constraints. Android Kotlin C++ JNI TFLite Qwen2.5 Adaptive CPU threads and context length Query classification for workload profiling Latency, energy and quality-aware policies Evaluated across 720 inference runs	🔬 Research Practical machine learning, reinforcement learning and resource-aware AI. Reinforcement Learning Time-Series LLM Optimization Dynamic pricing with reinforcement learning Efficient on-device model execution Data-driven system performance evaluation

How AdaptiLLM adapts (diagram)

flowchart LR
    Q([User query]) --> C[Query complexity<br/>classifier]
    C --> P{Adaptive policy}
    P -->|CPU threads| R[On-device Qwen2.5<br/>Android · JNI · C++]
    P -->|context length| R
    R --> M[Latency · energy · quality]
    M -.feedback.-> P

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🏆 Recognition

• 🥇 Best Paper Award, ComSIA 2026 (Springer): first author of Reinforcement Learning for Dynamic Pricing
• 🌍 Global Public Rank 15: WiDS Datathon Challenge 2024

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🛠️ Stack

Languages
AI / ML / Data
Backend / DB
Cloud / DevOps
Edge / Embedded

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👥 Community

☁️ AWS Student Builder Groups: Chapter Lead Hands-on workshops on EC2, S3, Lambda and serverless Mentored 50+ students through real AWS deployments

🌐 Google Student Ambassador Represented Google developer programs on campus Organized technical learning activities

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📊 Stats

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Build it. Measure it. Improve it.