Peter S.-Senior AI, Data & Computer Vision Expert

Project: Defect Generation on Test-Bench Images of Metal Surfaces Environment: Automated Visual Inspection (AVI), Metallurgy & Manufacturing

• Objective & Implementation: Designed, architected, and trained Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g., cracks, inclusions, scale) on rough metal surfaces under real test-bench lighting conditions for privacy-compliant and efficient dataset expansion (data augmentation).
• Technical Design: Implemented robust Generative AI and computer vision pipelines in Python and PyTorch. Used semantic segmentation approaches for mask-controlled defect synthesis and subsequent evaluation with EfficientDet object detection models.
• Business Impact: Massive dataset upscaling (10x) without time-consuming and costly physical test-bench runs, while significantly improving the detection performance of automated inspection systems.

Technologies & Skills Used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision

Project: Synthetic Data for Surface Defects Environment: Industrial Vision, Surface Quality Inspection

• Objective & Implementation: Developed a conditioned generation pipeline using ControlNet and latent-based inpainting methods to selectively synthesize variable surface defects (e.g., scratches, pores, coating defects) on flawless product photos, with precise mathematical control over position, extent, and defect class.
• Technical Design: Designed and fine-tuned generative AI pipeline architectures (Stable Diffusion Inpainting, ControlNet) in PyTorch and Python. Processed images with OpenCV, generated masks, and evaluated results with Mask R-CNN / semantic segmentation. Developed APIs and tooling GUIs with Qt and scaled the solution on Amazon Web Services (AWS).
• Business Impact: Estimated 80% reduction in physical scrap accumulation for data collection; significantly improved recall for rare, critical defect classes in industrial quality assurance (quality control engineering).

Technologies & Skills Used: Python | PyTorch | Stable Diffusion Inpainting | ControlNet | Mask R-CNN | OpenCV | Semantic Segmentation | Machine Learning | Amazon Web Services (AWS) | Qt (Software) | API Development | Quality Control Engineer

Project: Generation of Synthetic Defects on Weld Seams Environment: Non-Destructive Testing (NDT), Heavy Industry

• Objective & Implementation: Developed a physics- and edge-guided generation pipeline to synthesize realistic weld seam defects (e.g., lack of fusion, pores, crater cracks) in optical images. Automatically balanced and selectively expanded the training dataset for downstream segmentation and detection models.
• Technical Design: Trained and orchestrated generative deep learning architectures (GANs, latent diffusion models, transformers) with PyTorch and Python. Processed images and extracted features using OpenCV, and developed the UI in Qt.
• Business Impact: Fully balanced the previously heavily distorted dataset (data imbalance solved) and significantly improved the detection accuracy of downstream AI models in industrial quality inspection.

Technologies & Skills Used: Python | PyTorch | GANs | Latent Diffusion Models | Transformers | Computer Vision | OpenCV | Data Science | Qt (Software)

Project: Web-Based Training Platform for Generative AI Environment: Cloud & SaaS

• Goal & Implementation: Design and development of a scalable web framework for the fully automated management of complex datasets and execution of distributed fine-tuning jobs in the cloud.
• Technical Design: Backend and fullstack architecture based on Python and Django. Integration of PyTorch for model training and orchestration of high-performance cloud infrastructures on Amazon Web Services (AWS SageMaker, Elastic Beanstalk, EC2, S3).
• Business Impact: Significant reduction of setup and training times when adapting generative models through a modular user and project management system.

Technologies & Skills Used: Python | PyTorch | Django | AWS (SageMaker, Elastic Beanstalk, EC2, S3) | SaaS Architecture | Data Engineering

Project: Simulation of a Vision Solution for Robotic Sewing & Fabric Processing Environment: Industrial Automation & Robotics

• Goal & Implementation: Development of a physically accurate fabric simulation for highly complex deformable materials using mathematical optimization and systems simulation. Implementation of a parallel on-demand cloud rendering pipeline on AWS for scaled synthetic data generation.
• Technical Design: Automated control and scripting of simulations using Python and the Blender API within a flexible system architecture.
• Business Impact: Enabled the efficient training of robust vision models for robots without complex manual test setups and costly physical test series.

Technologies & Skills Used: Python | Blender (API) | Amazon Web Services (AWS) | Systems Simulation | Mathematical Optimization | Software Development (General)

Technical Leadership & Strategy

• Technical Lead: Technical leadership of an engineering company with a strategic focus on innovative generative AI solutions and synthetic data generation.
• Customer Projects: End-to-end design, development, and scalable deployment of industrial computer vision and AI systems for enterprise customers.

Generative AI & Data Architecture

• Generative AI Tools: Architecture and scaling of proprietary generative AI tools for the automated generation of synthetic training data.
• Efficiency & Impact: Significant reduction of data annotation costs and acceleration of model training cycles through the targeted use of synthetic datasets.

Machine Learning, Deep Learning & Innovation

• Deep Learning Innovations: Development of novel deep learning methods (CNNs, Transformers) for precise information extraction from highly complex laser images, as well as trinamiX material recognition and skin detection using PyTorch and TensorFlow.
• Architecture Optimization: Continuous evaluation and performance improvement of neural network architectures for demanding computer vision and classification tasks.
• Technical Leadership: Technical contact and mentor for all machine learning and deep learning topics across the company.

Data Engineering, Pipelines & Spectroscopy

• End-to-End Data Pipelines: Design and implementation of complete data preparation and modeling pipelines, including hardware specification for data collection, automated data processing, metric evaluation, and systematic testing.
• Spectroscopy & Chemometrics: Analysis of NIR spectra and development of chemometric algorithms for accurate material and spectral data analysis.

Embedded Systems, Prototyping & Cloud

• Embedded & Edge Deployment: Responsibility for the software development of the first spectrometer prototype and implementation of resource-efficient algorithms (C++, Python) for use on embedded systems and mobile devices (e.g., smartphones).
• Cloud Integration: Integration, scaling, and management of data science pipelines in the AWS cloud.

Project: Design & Implementation of a Method for Laser-Spot Reflectometry

Environment: Optical Measurement & Industrial IoT

• Goal & Implementation: Development of an AI-based measurement method for the automated extraction of information from laser images, including automated robot control and a robust backend infrastructure.
• Technical Design: Design and training of deep learning models with TensorFlow in Python. Integration of interfaces for precise hardware and robot control, as well as connection to a relational database backend for reliable data storage and analysis.
• Business Impact: Full automation of the entire measurement and data collection process for complete and continuous quality assurance in industrial production.

Technologies & Skills Used: Python | TensorFlow | Robot Control | Database Backend | Reflectometry | Optical Measurement | Industrial IoT

Project: NIR Spectrometer Development (PbS Cells) & Mobile Integration Environment: Spectroscopy & Mobile Devices

• Goal & Implementation: Hardware-oriented control and signal processing for infrared sensors (PbS cells). Development of robust chemometric analysis algorithms for NIR spectra, along with seamless integration into a mobile Android app and a scalable database backend.
• Technical Design: High-performance signal processing and data transformations in C++ and Python. Architecture of interfaces and the backend for efficient transmission and storage of spectral data on mobile devices.
• Business Impact: Successful commercialization of a mobile, compact spectrometer for material identification within seconds directly on site.

Technologies & Skills Used: Python | C++ | Data Transformation | Database Backend | Android | Embedded Systems | Signal Processing

Algorithm Development, Image Processing & Computer Vision

• 3D Reconstruction & Computer Vision: Development of physics-based algorithms and pipelines using C++, Python, and OpenCV for the precise reconstruction of 3D point clouds from projected laser points.
• Machine Learning & AI: Implementation of models for object detection and semantic segmentation using TensorFlow.
• Software Development & GUI: Creation of high-performance software solutions, interfaces (APIs), and user interfaces using C++ and Qt, as well as visualizations in OpenGL.

Simulation, Cloud & Tooling

• Physics Simulation: Modeling and simulation of physical effects, as well as delivery of customized simulation tools.
• Cloud & DevOps: Deployment and integration of solutions in cloud environments (AWS, Microsoft Azure) using modern version control (Git).