Smart Tek Technologies is advancing Industrial Edge AI for predictive maintenance, intelligent sensing and reliable decision-making close to the machines and systems that matter.
Smart Tek Technologies brings together software architecture, cloud and data systems, machine learning and edge computing. Our current R&D focus is adaptive, hardware-aware AI for industrial and energy infrastructure, where latency, connectivity, reliability and compute constraints make local intelligence increasingly important.
Low-latency inference near equipment and sensors, designed around real-world compute, memory and connectivity constraints.
Time-series intelligence for degradation modeling, remaining useful life estimation and failure-risk awareness.
A continuum in which advanced models can be developed and refined with larger compute resources, then optimized for efficient edge deployment.
Our technology direction combines time-series AI, signal processing, model optimization and intelligent sensing with robust deployment practices.
How can predictive AI models be selected, compressed, adapted and deployed across heterogeneous edge hardware while maintaining accuracy, reliability and energy efficiency under changing industrial operating conditions?
Our current research foundation uses the NASA C-MAPSS FD001 dataset to study multivariate time-series prediction of remaining useful life (RUL). The prototype uses a PyTorch LSTM model, ONNX deployment, containerized services and streaming inference as a basis for broader edge-AI research.
*Measured in the current benchmark environment; this is not a claim of latency on a physical edge device. Hardware benchmarking and optimization are part of the research roadmap.
Evaluate LSTM, GRU, CNN/TCN and compact transformer approaches across predictive quality and edge efficiency.
Study quantization, compression and hardware-aware inference across increasingly constrained edge platforms.
Investigate domain shift, model drift, uncertainty, sensor fusion and escalation across edge-to-cloud/HPC systems.
Emad Ghosheh, PhD is a technology leader, computer scientist, researcher and university educator with more than two decades of experience spanning software architecture, telecommunications, cloud and data systems, fintech and enterprise technology.
His current focus is Industrial Edge AI, machine-learning systems and predictive maintenance for industrial and critical-infrastructure applications.
PhD, Computer Science
MS, Electrical & Computer Engineering
Executive MBA
Adjunct Computer Science Faculty
Technology Lead, Smart Tek Technologies
For research collaboration, technology partnerships and Industrial Edge AI discussions, contact Smart Tek Technologies.