Convolutional Neural Network Experts in Germany

• Engineered and validated a full-vehicle thermal management system in MiL simulation, achieving 95% correlation accuracy against real-world vehicle measurements, directly supporting virtual calibration and reducing dependency on physical test benches.
• Led end-to-end Model-in-the-Loop (MiL) simulation development using AVL CruiseM and MATLAB/Simulink, covering system architecture, parameterization, and validation.
• Acquired and analyzed vehicle sensor measurements (temperature, volumetric flow rate) using dSpace MicroAutoBox (HiL) and IPEmotion, translating raw data into actionable calibration insights.
• Calibrated and optimized critical actuators and thermal components - pumps, valves, electric heaters, heat exchangers, and refrigerant circuits and identified/integrated previously missing physical behaviors to close the gap between simulated and real vehicle performance.
• Designed and tuned an integrated actuator controller with precisely calibrated parameters, producing a high-accuracy virtual model adopted for downstream development use.

• Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
• Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
• Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
• Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
• Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.

• Topic: Object Detection and Semantic Segmentation for (AUV) Systems using Transformer-Based Vision Models and Sensor Fusion.
• Designed and implemented an end-to-end multi-sensor fusion perception pipeline (Camera, LiDAR, IMU) in ROS
• Developed CNN-based Machine Learning model (YOLOv8) and Transformer-based vision models for real-time object detection
• Processed and clustered 3D LiDAR point clouds using DBSCAN, RANSAC, and voxel grid filtering to enable robust object localisation in noisy environments.
• Designed Bayesian Network models (GeNle) for probabilistic reasoning and sensor-level decision fusion under uncertainty.
• Applied Kalman filtering for sensor state estimation, temporal alignment, and smooth object tracking, reducing false positives in safety-critical scenarios.
• Evaluated system performance under realistic driving dynamics, improving tracking stability and overall perception robustness.
• Built deep learning pipelines for training, validation, and performance evaluation of perception models using sensor data.

Program Manager for a radical architecture and IT transformation program (RAITT) reshaping the applications landscape (i.e. cloud transformation) and operating model into agile organisation.

• E2E readiness towards mass-market business division covering demand, delivery, test and roll-out phases
• Driving Telefonica internal teams and external system integrators to ensure delivery on time and in quality in adherence to defined processes
• Management of risks, issues and dependencies on program level

• Developed end-to-end reproducible ML pipelines (PyTorch) with data versioning (DVC), experiment tracking (MLflow), automated testing (PyTest), and CI/CD across all training workflows.
• Scaled Vision Transformer and CNN training across NVIDIA A100 GPU clusters (CUDA, DDP, SLURM); applied hyperparameter optimization (W&B Sweeps) to reduce training overhead and identify optimal configurations.
• Developed a real-time 3D human motion generation system (ViT, VQ-VAE, SMPL-X/PIXIE) for personality-conditioned avatar synthesis; achieved state-of-the-art FID = 6.15 and P-FID = 10.31 on the UDIVA benchmark.
• Validated model expressiveness through structured user studies, achieving 86% accuracy in distinguishing extroverted vs. introverted avatar behaviors.
• Optimized inference pipelines by deploying PyTorch models via TensorRT and ONNX Runtime into native C++ code; benchmarked performance.

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

• Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
• Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
• Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.

• Migration of the software development process of a medical technology software to C/C++ package manager Conan and development of macOS-specific system components

• Developed a CNN-based pedestrian detection system using LiDAR data
• Created Python scripts for bounding boxes, dataset labeling, and data conversion
• Evaluated model performance on datasets with point clouds

• 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

• Analysis
• Consulting
• Software design
• Development
• Automation
• Testing
• Deployment
• Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
• Developed best practices for working with agentic systems and AI in practice
• Created code templates

• Technical and professional management of software and ML development; largely independent implementation of programming and guidance of the team and external project partners
• Design, creation, and preparation of training and test data sets from experimental image data and simulations
• Selection, implementation, training, validation, and testing of neural networks for image-based reconstruction and transformation
• Systematic evaluation, comparison, and optimization of various model architectures (convolutional neural networks, generative adversarial networks, autoencoders, transformers)
• Design and implementation of explainable AI analyses for model interpretability and robustness assessment (analysis of feature maps, augmentation studies, guided backpropagation)
• Presentation of the developed methods and results in project meetings and at international conferences

• Spearheading investor outreach and partnership development as founding MD, building the business case and technical narrative needed to attract initial funding and strategic collaborators in the digital health space
• Designing multi-agent AI systems for autonomous biosignal analysis, orchestrating LLM-based reasoning pipelines with domain-specific medical context to enable intelligent, clinical decision support

• Developed the iOS/Android Delegate App and the Conference Operator
• Updated and developed a user-friendly conference environment and real-time video streaming
• Optimized the overall conference experience by implementing customizable features for flexible setup
• Enhanced the efficiency and usability of conference technology, enabling a seamless workflow and improved participant interaction experience