An applied AI researcher and engineer specializing in edge intelligence, power electronics, and robotics systems. Working alongside Edison Medal recipient Frede Blaabjerg, where our research focuses on physics-informed machine learning, thermal modeling of power semiconductor devices, and intelligent control systems for next-generation energy and automation technologies.

Over the past several years, we have developed and deployed custom AI algorithms across a wide range of domains, including:

–power electronics & predictive thermal monitoring

–robotics perception and control

–drones & autonomous platforms

–embedded and edge-AI systems

–computer vision and real-time tracking architectures

Our work bridges the gap between research and implementation, integrating AI models directly into hardware-constrained, mission-critical, and industrial environments. Below is a list of pertinent publications:

• D. Minott, M. Davari, I. Otchere and F. Blaabjerg, “Replacing Classical Algorithms to Determine the Reliability of Power Electronic Converters: An AI Method Based on the Nonlinear Autoregressive with Exogeneous Inputs Artificial Neural Network,” SoutheastCon 2025, Concord, NC, USA, 2025, pp. 707-712, doi: 10.1109/SoutheastCon56624.2025.10971482.
• D. Minott, S. Siddiqui and R. J. Haddad, “Benchmarking Edge AI Platforms: Performance Analysis of NVIDIA Jetson and Raspberry Pi 5 with Coral TPU,” SoutheastCon 2025, Concord, NC, USA, 2025, pp. 1384-1389, doi: 10.1109/SoutheastCon56624.2025.10971592.