Usama's Lab — Bridging Academia & Industry
Forging AI-powered
AI & Full Stack Engineer and researcher translating applied AI into production impact at Lean Automation. Focused on RAG systems, LLM fine-tuning, predictive maintenance, and medical imaging research.
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L A B · A I / M L
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L A B · A I / M L Timeline
Professional Journey
Experience across AI engineering, full-stack product development, and applied medical AI research.
AI & Full Stack Engineer
Feb 2024 – PresentArchitecting industrial AI systems spanning RAG, LLM fine-tuning, predictive maintenance, and event-driven microservices at production scale.
- ›Built RAG systems with FAISS + LLMs for complex industrial log intelligence
- ›Fine-tuned Llama 3 and Grok for domain-specific industrial reasoning
- ›Designed Kafka pipelines processing 10,000+ sensor events/second with sub-50ms latency
- ›Decomposed monolith into 12+ microservices handling millions of daily events
Full Stack Engineer
Jan 2023 – Jan 2024Built and shipped production web platforms and automation pipelines across analytics, APIs, CI/CD, and deployment workflows.
- ›Delivered 5+ production full-stack apps with 99.5% availability SLAs
- ›Implemented GitHub Actions + Docker CI/CD for zero-downtime deployments
- ›Automated reporting via SQL procedures and APIs, reducing manual effort by 80%
- ›Built real-time dashboards for KPI and anomaly monitoring
Research Assistant
Mar 2024 – Feb 2025Led thesis research on medical AI for brain tumor analysis, focusing on model design, dataset engineering, and publication-ready outputs.
- ›Developed a Dual-Head Neural Network (DHNN) for tumor type + grade prediction
- ›Built end-to-end MRI preprocessing pipelines with OpenCV and Pandas
- ›Achieved 93.2% classification accuracy with EfficientNetB0 backbone
- ›Co-authored and submitted work to Elsevier Results in Engineering
Bachelor of Science in Computer Science (BSCS)
Mar 2021 – Jan 2025CGPA: 3.56 / 4.0. Focused on AI/ML systems, software engineering, and data-driven problem solving.
- ›Thesis: Multi-Class Brain Tumor Classification and Grade Estimation using DHNN
- ›Coursework: AI, ML, DSA, DBMS, Operating Systems, Distributed Systems
- ›Built research and production projects spanning healthcare and industrial AI
Portfolio
Featured Projects
Research-backed production systems delivering measurable impact across healthcare and industrial sectors.
Multi Class Brain Tumor Classification and Grade Estimation using Dual Head Neural Network
Dual-Head Neural Network leveraging EfficientNetB0 for multi-class tumor classification achieving 93.2% accuracy with extreme computational efficiency (4.0M parameters, 15ms inference).
A VAE LSTM Architecture for Early Fault Signatures, Time to Failure Projection, and Root Cause Pathway Analysis
VAE-LSTM architecture for industrial predictive maintenance with 48-hour advance fault detection. Achieved $2.3M annual savings with 20% downtime reduction.
HarmFilter App
Multi-model BiLSTM hate speech detection and prevention tool addressing harmful online content in South Asian contexts. Actively educates users to promote respectful online communities.
FarmGuardian
AI-based decision support system for Punjab farmers. Provides disease detection and yield forecasting through Urdu-based interface, helping minimize crop losses and optimize resources.
Eventure
Innovative tourist platform revolutionizing travel in Pakistan with personalized journeys, real-time security alerts, and seamless vendor integration for a dynamic travel experience.
Research Insights
Lab Notes
Technical deep-dives into AI/ML systems, research findings, and production engineering insights.
Building AI-Powered Predictive Maintenance Systems for Industrial IoT
Learn how we reduced unplanned downtime by 20% using VAE-LSTM pipelines. Deep dive into the architecture, challenges, and real-world deployment of industrial ML systems.
Deep Learning for Medical Imaging: Brain Tumor Classification with CNNs
How we achieved 96% accuracy in brain tumor classification using EfficientNetB0 and dual-head neural networks. Complete methodology and clinical validation results.
Causal AI for Industrial Anomaly Detection: Beyond Correlation
Discover how we implemented causal inference to identify root causes and reduced false positives by 60%. A paradigm shift from reactive alerts to proactive diagnosis.
Work in Progress
Workbench
Active experiments and prototypes. Things that are being built, broken, and rebuilt.
industrial-rag-core
Retrieval-Augmented Generation stack for industrial logs using FAISS + LLM pipelines for failure triage and root-cause assistance.
predictive-maintenance-suite
VAE-LSTM and ANN/RNN pipelines for oil & gas fault forecasting with production-grade monitoring.
iot-event-mesh
Kafka-based sensor ingestion mesh sustaining 10,000+ events/second with sub-50ms latency and zero data loss objectives.
dhnn-brain-tumor-study
Dual-Head Neural Network research workflow for simultaneous tumor type and grade estimation from MRI scans.