Applied R&D: Generative AI for Domain-Specific Data
Completed:
This project was a strategic deep-dive to unlock the commercial potential of next-generation generative AI. The vision was to create a novel methodology for generating high-fidelity synthetic data, addressing a critical bottleneck in high-value engineering and industrial AI.
The Opportunity
In fields from aerospace to industrial automation, the scarcity of diverse, high-stakes training data is a major barrier to innovation. Physical testing is expensive and slow. The opportunity was to engineer an AI that could generate realistic, physics-informed “digital twin” data on demand, radically accelerating development cycles.
The Breakthrough
First, the core generative architecture was engineered and validated on complex visual datasets, achieving state-of-the-art generation quality. With the technology proven, I then pioneered its application in a far more challenging domain: modeling the transfer functions of intricate sensor systems.
The breakthrough was successfully teaching the model to generate novel system data that was not only statistically realistic but also adhered to the underlying laws of physics.
Tech & Skills
- Core Competencies: Generative AI, Diffusion Models, Applied R&D