Full-Stack Engineer · GKS Scholar · Edge AI & Applied ML Researcher
Six years shipping production web systems, now spending my research time on the harder half of the problem: getting machine learning off the notebook and onto real sensors, real devices, and real dashboards people actually use.
Master's researcher in Electronics Engineering at Jeju National University, based in Jeju, South Korea on the Global Korea Scholarship. Before the lab, six years of full-stack engineering — APIs, dashboards, automation, and the unglamorous data plumbing that keeps them fed.
That combination is the point. Most edge-ML work fails somewhere between the sensor and the screen, and I have spent enough time on both ends to build the whole path rather than a slice of it.
Currently going deep on Edge AI and TinyML, computer vision, and time-series anomaly detection, applied to smart agriculture, environmental monitoring, energy management, and industrial safety.
| Systems | Scalable full-stack applications, REST APIs, and real-time dashboards |
| Data | High-volume synchronization pipelines and stream/batch processing |
| Ticketing | Broker management platforms — inventory sync, pricing automation, margin reporting |
| Automation | Web automation and resilient data-collection infrastructure |
| Vision | YOLO-based detection for monitoring and safety use cases |
| Sensing | Sensor-driven ML prototypes, from acquisition to inference |
| Delivery | Edge and cloud deployment pipelines that survive contact with production |
A large part of my commercial work is custom software for ticket brokers — the operators reselling live event, sports, and concert inventory, where margin is won or lost on how fast and how accurately listings move.
Off-the-shelf tools handle the average broker. They do not handle yours. What I build instead is fitted to the operation:
- Inventory management — one source of truth across every marketplace, with high-volume synchronization that keeps listings, splits, and holds accurate
- Automated pricing — rules and data-driven repricing that react to demand and competitor movement instead of waiting on manual sweeps
- Marketplace integrations — bulk listing, delisting, and order ingestion, so the same inventory works everywhere without duplicate effort
- Operations dashboards — real-time visibility into sales, exposure, cost basis, and per-event margin
- Automation — fulfillment, reconciliation, and reporting workflows that remove the repetitive manual work errors hide inside
The measure of success here is commercial, not technical: fewer oversells, faster turnaround, tighter pricing, and more profit per event.
Sensors → Real-Time Data → Machine Learning → Edge/Cloud Deployment → Decision Dashboard
Every stage in that chain is where projects break, so I work across all of them:
- Edge AI and TinyML — inference under real memory and power budgets
- Anomaly detection on noisy, drifting sensor streams
- Computer vision for monitoring, inspection, and safety
- Predictive irrigation and soil-moisture analysis
- Agriculture and climate resilience
- Energy monitoring and optimization
The goal is applied research that publishes and deploys — systems that keep working once they leave the laboratory.
AI & Data
Python PyTorch scikit-learn OpenCV YOLO Pandas NumPy
Full-Stack
TypeScript JavaScript React Next.js Node.js Express Tailwind CSS shadcn/ui
Data & Infrastructure
MongoDB PostgreSQL Redis Prisma Docker Linux Nginx PM2
Research collaborations, open-source work, internships, and industry–academia partnerships — particularly around edge AI and embedded ML, computer vision and sensor fusion, smart agriculture and climate tech, real-time data engineering, and AI-powered full-stack products.
📫 abdulsamad.laghari1@gmail.com · 💼 Upwork
Building reliable software today while researching intelligent systems for tomorrow.