Aida Farahani, PhD | AI Solutions Engineer

About

I am an AI Solutions Engineer with a PhD in Artificial Intelligence, specializing in turning real-world, cross-disciplinary challenges into practical and reliable AI systems. My journey into AI began in my twenties, before frameworks such as TensorFlow and PyTorch were available, when I designed and trained a neural network for my BSc thesis in Software Engineering. Since then, I have worked across industrial computer vision, data-centric AI, time-series analysis, 3D deep learning, machine learning for FEM simulations, LLMs, VLMs, RAG, AI agents, and end-to-end AI development—primarily with complex, real-world data rather than standard benchmarks. Having witnessed AI evolve from low-level implementations to today’s AI-assisted development, I remain deeply fascinated by its progress and possibilities.

                        PhD in Artificial Intelligence
                        Technical University of Chemnitz
                    
                        Applied AI Focus
                        Industrial computer vision, data-centric AI, and production-oriented workflows
                    
                        Applied & Scientific Foundation
                        Practical AI for real-world systems, grounded in scientific machine learning and research methods

Work Experience

AI Solutions Engineer - TKI mbH, Germany

2024 - Present

Develop AI solutions for industrial inspection, infrastructure analysis, document understanding, and semantic retrieval.
Design computer vision systems for object detection, duct segmentation, color detection, and noisy real-world field imagery.
Prepare deployment-oriented prototypes, including CoreML export and iOS visualization workflows.
Improve model performance through data-centric AI: dataset refinement, annotation strategy, class balancing, and failure-case analysis.

Industrial AI Computer Vision CoreML Data-Centric AI

Research Scientist - Technische Universitaet Chemnitz, Germany

2018 - 2024

Research career focused on machine learning for engineering simulation, 3D deformation modeling, automotive body production, and environmental sensor analysis across BMBF-funded applied research projects.

ML@Karoprod: Machine Learning for Automotive Body Production

BMBF project with Fraunhofer IWU, SCALE GmbH, and TU Chemnitz.

Created large-scale FEM training datasets for sheet metal forming where no suitable dataset existed.
Used implicit neural representations, including signed distance functions, for dense deformable geometries.
Developed surrogate models for geometric deviation, thickness distribution, and thinning on high-resolution shell meshes, including the ML-Karoprod MeshPredictor.
Built reinforcement learning approaches for inverse process design and sequential deformation modeling.

Neural-network prediction demo: deformation results update within seconds from slider parameters, avoiding a fresh FEM setup and full simulation run for each variation.

Smart Airsense: AI-Based Health Assistant

January 2022 - 2024

BMBF project with Corant GmbH / air-Q and TU Chemnitz.

Worked with air-quality sensor measurements logged over time by Corant / air-Q devices.
Added machine learning methods for predicting environmental events from noisy real-world time-series data.
Developed and validated predictive models in a human-in-the-loop machine learning setting.