I'm an engineer working at the seam between physical infrastructure and intelligent software — building IoT and edge systems that turn sensor data into decisions. A decade coordinating large-scale electrical & MEP projects, now an MS in Cyber-Physical Systems from Northeastern, focused on applied research.
My background is deliberately unusual. For over ten years I designed and coordinated electrical and MEP systems on US$1B–$4B infrastructure projects — supervising field teams, integrating BMS, CCTV and access-control systems, and commissioning power and low-current installations across large sites.
Then I went back to school for a Master of Science in Cyber-Physical Systems (IoT) at Northeastern University, graduating with a 3.80 GPA. That's where the two halves of my work met: embedded systems, edge computing, communication protocols and machine learning, applied to the physical equipment I already understood from the field.
I'm most drawn to research and engineering where I design, build, and validate real systems — digital twins, predictive maintenance, and low-cost intelligent sensing — rather than stay purely in simulation.
// Electrical Engineer → CPS Engineer → applied systems research
Systems I've designed end-to-end — from the sensor and the radio link up to the model and the dashboard.
A low-cost solar tracker that uses an ESP32-CAM to visually detect the sun's position and orient the panel for maximum yield — with long-range LoRa telemetry and cloud monitoring, all built from commodity hardware. This was my Master's capstone.
A digital replica of manufacturing equipment that ingests live sensor data, detects anomalies with ML, and predicts maintenance needs — improving overall equipment effectiveness. Built as a three-layer system: edge collection, a twin core, and a live dashboard.
An IoT platform for structural-health and asset monitoring of buildings and industrial plant — distributed sensing, edge processing for real-time analysis, and cloud integration for predictive maintenance. Directly bridges my MEP field experience and CPS training.
A decade delivering physical systems on some of the region's largest projects, then formal training to make them intelligent.
Documenting real systems and framing the work as research contributions.
A low-cost, vision-driven solar tracking approach with long-range telemetry for building energy optimization — quantifying tracking accuracy and yield against fixed panels.
Full capstone documentation and open-source implementation — hardware, tracking algorithm, communication stack and cloud monitoring.
Adding experimental data and a literature review to post a citable arXiv paper.
Framing a research question around motor-drive and infrastructure health using edge AI and live sensor telemetry.
Exploring commodity-hardware CPS that make industrial and building systems observable and self-correcting.