Experience
Machine Learning Research Scientist
Carl Zeiss, Munich, Germany (01/2025 – Present)
- Implementing robust ML solutions for sensitive use cases (surgical rooms, GPU components, FPGAs) with critical energy efficiency requirements.
- Optimizing and deploying models on Edge devices.
- Reducing computation costs (memory, bandwidth, network size, energy consumption).
- Supervising interns and students.
Research Assistant
CARIAD (Volkswagen Group), Munich, Germany (07/2024 – 12/2024)
- Developed visual perception techniques for unknown objects using instance segmentation, language-to-vision methods, and knowledge distillation.
- Achieved state-of-the-art performance on COCO dataset.
- Submitted findings to CVPR.
Research Assistant
ETH Zurich / Helmholtz AI / TUM, Remote (01/2024 – 06/2024)
- Extended master’s thesis output to language modeling, LLMs, and vision transformers.
- Published at NeurIPS 2024 and AABI.
- Initiated a project on network architecture search.
Machine Learning Engineer (Part-Time)
BMW, Munich, Germany (10/2023 – 03/2024)
- Optimized neural networks for deployment on edge devices.
- Designed a framework for model optimization on resource-limited devices.
Research Machine Learning Engineer (Part-Time)
Huawei Munich Research Center, Munich, Germany (03/2023 – 08/2023)
- Optimized task assignments and monitoring for fleets of robots using reinforcement learning and LLMs.
- Specialized in compressing deep learning networks for edge devices.
- Tools: PyTorch, TFLite, ROS, TensorRT, Jetson, SLAM
Master Thesis
ETH Zurich, Zurich, Switzerland (03/2023 – 12/2023)
- Sparsified and compressed neural networks using Bayesian Occam’s razor.
- Achieved a grade of 1.0 and published at NeurIPS 2024.
Visiting Researcher
EPFL, Lausanne, Switzerland (09/2022 – 02/2023)
- Developed novel data augmentation methods for saliency prediction.
- Achieved competitive performance on the MIT300 Validation set.
Software Engineer (Part-Time)
Infineon Technologies, Munich, Germany (11/2020 – 02/2023)
- Conducted microchip testing and development.
- Built an AI pipeline for leakage detection and developed an efficient autoencoder.
Research Project: Data Innovation Lab
Intel, Munich, Germany (04/2022 – 09/2022)
- Researched and developed sparse and quantized neural networks.
- Improved computational efficiency for edge device deployment.
Software Engineering Intern
Technical University of Munich, Chair of Electronic Design Automation (11/2020 – 01/2021)
- Worked on an Instruction Set Simulator (ETISS).
- Implemented an ELF loader and managed memory layout.
Education
MSc in Robotics, Cognition, Intelligence
Technical University of Munich, Germany (10/2021 – 12/2023)
- Completed with over 150 ECTS out of 120.
BSc in Electrical Engineering and Information Technology
Technical University of Munich, Germany (10/2018 – 09/2021)
- Thesis: Evaluating the viability of the TVM LLVM Backend for TinyML on RISC-V.
Skills
- Programming Languages: Python, C++, C, R, Matlab, Perl, Java, VHDL, SQL
- Frameworks & Tools: PyTorch, TensorFlow, CUDA, Docker, Kubernetes, FastAPI
- Areas of Expertise: Efficient Learning, Computer Vision, NLP, Reinforcement Learning, Bayesian Inference