Curriculum Vitae

Professional Summary

  • Generative AI Engineer and Applied Scientist with a PhD in Computer Science, over six years of experience in computer vision and human pose tracking, and two years of hands-on work in diffusion-based generative model development.
  • Deep research expertise in multi-person pose tracking, covering subdomains such as person detection, object tracking, pose estimation, person search, and re-identification.
  • Proven track record in building large-scale datasets (e.g., PoseTrack21) and training deep learning architectures, including convolutional and transformer-based models.
  • Skilled in designing and deploying end-to-end generative AI pipelines, with a focus on high-fidelity digital human synthesis for production environments.
  • Extensive experience with training strategies for resource-constrained environments, including LoRA, mixed precision, quantization, and gradient accumulation.
  • Strong command of modern GPU training environments, MLOps workflows, and cross-functional collaboration in fast-paced teams.

Experience

  • 7. 2023 - Present
    AI Engineer, Technical Lead
    Verce GmbH, Cologne, Germany
    • Led the creation and strategic direction of the in-house AI team.
    • Developed and optimized generative image pipelines for digital fashion models using state-of-the-art diffusion techniques.
    • Built scalable training and evaluation systems for fine-tuning models including SD1.5, SDXL, SD3, and Flux.
    • Introduced efficient training strategies (LoRA, Mixed Precision, Gradient Accumulation) for low-resource GPU environments.
    • Deployed internal AI tools within dockerized Linux environments, supporting the CGI production workflow.
    • Maintained and administered the company GPU server stack (StackIT Cloud, CUDA, driver updates, GPU scheduling).
  • 4. 2018 - 6. 2023
    Research Assistant
    Computer Vision Group, University of Bonn, Germany
    • Conducted PhD research on multi-person pose tracking in videos.
    • Created and annotated the large-scale PoseTrack21 dataset for person search, multi-object tracking, and multi-person pose tracking.
    • Published at top conferences including CVPR, ECCV, BMVC, AAAI, and NeurIPS.
    • Maintained lab GPU infrastructure and supervised multiple Bachelor and Master theses.
    • Led developments in person detection, re-identification, temporal tracking, and spatial consistency.
  • 10. 2015 - 12. 2017
    Student Research Assistant
    Computer Vision Group, University of Bonn, Germany
    • Worked on 2D and 3D human pose estimation tasks using deep learning.
    • Assisted in research and tool development for pose datasets and model evaluation.
  • 2. 2011 - 9. 2013
    Working Student
    OwnSoft GmbH, Cologne, Germany
    • Co-developed management software for a multimedia company in C# and .NET.

Academic Interests

  • Generative AI
    • Training and fine-tuning diffusion models for high-resolution human image generation
    • Improving realism, identity consistency, and fidelity in digital human synthesis
    • Exploring novel architectures and training techniques for generative models
  • Human Pose Estimation and Tracking
    • Multi-person pose estimation and tracking under occlusions and motion
    • Person re-identification and long-term temporal association in videos
    • Person detection, search, and tracking with spatial-temporal attention mechanisms
    • Dataset design and annotation workflows for large-scale pose tracking benchmarks
  • Deep Learning and Computer Vision
    • Designing and benchmarking CNN and Transformer-based architectures
    • Efficient training strategies including quantization, mixed precision, and distributed training
    • Interpretability and robustness in model predictions
  • Infrastructure and Tooling
    • GPU cluster management, job scheduling, and performance debugging
    • Docker-based deployment pipelines for reproducible training and inference

Skills

  • Programming Languages
    • Python (advanced), C++, Bash
  • ML & Deep Learning Frameworks
    • PyTorch, Diffusers, Transformers, Accelerate, PyTorch Lightning
  • Computer Vision & Data
    • OpenCV, TorchVision, NumPy, Matplotlib, numba, TensorBoard
    • Dataset creation, annotation tooling, image/video pre-processing
  • Training Techniques
    • Mixed precision, quantization, gradient accumulation, LoRA
    • Fine-tuning, transfer learning, model compression
  • Infrastructure & MLOps
    • Docker, Make, Git, Linux/Ubuntu, GPU scheduling and driver stack
    • ComfyUI, StackIt Cloud, Microsoft Teams, Jira, Miro
  • Tools & IDEs
    • NeoVim, VSCode, PyCharm

Publications

  • NeurIPS 2024
    Humans in Kitchens A Dataset for Multi-Person Human Motion Forecasting with Scene Context
    • Julian Tanke, Oh-Hun Kwon, Felix B Mueller, Andreas Döring, Juergen Gall
  • AMFG 2023
    A Gated Attention Transformer for Multi-Person Pose Tracking
    • Andreas Döring, Juergen Gall
  • CVPR 2022
    PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose Tracking
    • Andreas Döring, Di Chen, Shanshan Zhang, Bernt Schiele and Juergen Gall
  • AAAI 2022
    Keypoint Message Passing for Video-based Person Re-Identification
    • Di Chen, Andreas Döring, Shanshan Zhang, Jian Yang, Juergen Gall and Bernt Schiele
  • ECCV 2020
    Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos
    • Andreas Döring, Umer Rafi, Bastian Leibe and Juergen Gall
  • BMVC 2018
    Joint Flow: Temporal Flow Fields for Multi-Person Pose Tracking
    • Andreas Döring and Juergen Gall
  • CVUI 2018
    A dual-source approach for 3D human pose estimation from single images
    • Umar Iqbal, Andreas Döring, Hashim Yasin, Björn Krüger, Andreas Weber and Juergen Gall