Pose Estimation Experts in Germany

• Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
• Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
• Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
• Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.

Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.

• Leading AI development for aquaculture startup, optimising shellfish visual assessments with machine learning and computer vision.
• Development and systematic evaluation of ML/CV algorithms for shellfish condition and morphometrics, using Python, Pytorch and MLFlow.
• Analysis of model performance, including identification of failure modes and edge cases in production deployments.
• Design of annotation strategies and refinement of labelled datasets for computer vision tasks.
• Detailed analysis of system performance and communication of findings through publication-quality technical reports to investors and fellow R&D staff.
• Responsible for delivery of technical roadmap.

• Automated driver assessment by programming temporal rule engines to evaluate lane-change execution safety, head-pose mirror checks, indicator usage cycles, and compliance with traffic lights and road signs
• Synchronized real-time traffic sign recognition and multi-state traffic light classification models with time-series CAN-bus telemetry and HD-map spatial priors to grade traffic rule adherence
• Trained and deployed distinct deep learning models optimized for interior cabin monitoring and exterior surrounding-area perception
• Combined perception outputs with camera intrinsics and horizon stability checks to execute 3D ground-plane object distance estimation assuming flat-ground geometry
• Deployed a split-compute edge network across a 10-vehicle fleet via VPN, implementing a zero-allocation host memory pipeline to eliminate frame accumulation latency (6×21 FPS per vehicle)

• Built a Trend-Scouting AI system to automate technology intelligence in power electronics and semiconductors, combining Azure OpenAI with LangChain, Scrapy-based web crawling for structured, noise-free data acquisition, and automated PDF reporting for internal R&D use. Developed a FastAPI-based (Uvicorn) web application to validate LLM outputs, test prompt strategies, and enable interactive system evaluation.
• Developed a real-time STM32 binary telemetry debugger with a PyQt-based GUI, featuring header-based frame synchronization, anomaly detection, template-driven payload decoding, time-aligned buffering, and live signal visualization.
• Developed an AI-driven power inductor designer using surrogate regression models for accurate electromagnetic and thermal prediction. Integrated multi-objective NSGA-II optimization to generate efficient, manufacturable designs.

• Developing methods for monocular 3D facial reconstruction and personalized geometric modelling from mobile imagery
• Building learning-based approaches for facial shape estimation, video-based facial analysis, and privacy-preserving visual learning

• Developed a deep learning system with multi-view Basler cameras for markerless 3D pose estimation

• Showcasing the world's first fully immersive AR glasses experience in a moving car at CES 2024.
• Speaker about augmented reality at international conferences (e.g. the AR Ride Concept @ Unite 2024).
• Lead a 12-person interdisciplinary software team developing Android head-unit integrations, navigation & ADAS UI, and embedded software.
• Define technical direction, drive cross-domain architecture and integration, and mentor engineers across Android, UI/UX and embedded stacks.
• Oversee a small fleet of test vehicles for validation, tests, and data collection.

• Designed and implemented multi-modal entertainment toolchains that combine passenger input, vehicle context, large-language models (text-to-text and speech-to-speech) and image generation models to deliver more interactive and immersive in-car experiences.
• Built and orchestrated tools for LLM-based agents, covering session management, background task execution, dynamic user interactions and persistent application state.
• Investigated multi-agent orchestration frameworks for in-car environments, evaluating communication protocols and architectural strategies for coordinated and reliable agent behavior.

• Developed a Python-based synthetic data generation pipeline in Blender to simulate complex human-forklift interactions for robotic perception and AI model training.
• Designed and modeled 3D industrial digital twins to support depth estimation, stereo vision, and safety analysis workflows.
• Collected and processed LiDAR, laser, and photogrammetry point clouds to generate accurate 3D maps for environment reconstruction and ground-truth data creation.
• Developed and deployed YOLOv8-based pose estimation and depth perception algorithms using PyTorch and OpenCV, optimized for GPU clusters and NVIDIA Jetson platforms.
• Integrated and validated AI modules in ROS-based robotic environments, ensuring real-time performance and interoperability.

• Developed point cloud classification and segmentation models for dental applications.
• Designed domain adaptation techniques that improved F1 score by 0.1 on a new clinical domain.
• Worked with 3D geometric data and production-scale ML pipelines.

• Applied pose estimation frameworks on thermal images using infrared cameras to enhance temperature analysis and thermal comfort evaluation.
• Performed CFD simulations to analyze and visualize airflow and temperature distribution in enclosed spaces, providing data-driven insights for improving HVAC system efficiency.
• Used transfer learning to adapt pre-trained deep learning models for different tasks and datasets, improving accuracy and reducing training time.
• Handled large datasets and applied visualization techniques such as boxplots, scatter plots, and other graphical tools to identify trends, detect anomalies, and validate data accuracy.
• Built machine learning predictive models such as linear regression, logistic regression, and classification models.
• Containerized ML models and data pipelines with Docker and orchestrated scalable training and inference workflows using Kubernetes.

• Design and implement an end-to-end Siemens NX based pipeline to convert OBJ CAD models into graph representations by applying AI-driven clustering of mesh faces into nodes and face adjacency for edges, streamlining GNN integration
• Generate a large-scale synthetic 3D CAD dataset, annotating parts with few MFCAD-style features to ensure balanced, diverse training data for GNN workflows
• Support the design, training, and evaluation of graph neural network architectures for AI-driven detection and classification of geometric features in 3D CAD shapes, accelerating feature-recognition workflows