Engineering / Selected projects
Model optimization, perception, robotics, and applied ML across research labs and industry.
Research foundations
Carl Zeiss · 2025 — Present
Optimizing vision foundation models and language models for real-time inference on edge accelerators. Quantization, distillation, and architecture-aware compression connect model quality with latency, memory, and energy budgets.
Related research: Quant-Trim ↗Intel Data Innovation Lab, Munich — 2022
Researched and implemented combined sparsification + quantization pipelines for edge device deployment. Benchmarked across SparseZoo models targeting INT8/pruned inference on CPU and accelerator backends.
Technical ReportBMW, Munich — 2023–2024
Designed an end-to-end optimization framework for deploying neural networks on automotive Edge Control Units. Pipeline encompasses quantization, graph optimization (ONNX), and backend-specific compilation targeting resource-constrained ECU hardware.
Huawei Munich Research Center — 2023
Optimized multi-robot task assignment and monitoring using reinforcement learning and LLM-based planners. Compressed control networks for on-device inference on Jetson platforms in SLAM-based environments.
Infineon Technologies, Munich — 2020–2023
Built a production AI pipeline for semiconductor leakage detection using efficient autoencoders. Also designed an NFC encoder/decoder in VHDL to replace RAM in embedded NFC systems.
CARIAD (Volkswagen Group), Munich — 2024
Developed visual perception techniques for unknown/novel objects using instance segmentation, language-to-vision alignment, and knowledge distillation. Evaluated on public perception benchmarks.
EPFL, Lausanne — 2022–2023
Developed novel augmentation strategies for visual saliency prediction models, achieving competitive performance on the MIT300 Validation benchmark.
TUM, Munich
End-to-end pipeline combining OCR with Kalman filtering for license plate recognition and multi-object pedestrian tracking in real-time video streams.
TUM, Munich — 2022 · Published on arXiv
Automated extraction of key points from legal document premises using BERT, IBM Debater, PageRank, and clustering. Paper published on arXiv.
arXiv PaperEPFL Applied Data Analysis — 2022
Large-scale NLP analysis of world cinema trends across decades and geographies using sentence embeddings, clustering, and statistical methods on movie plot corpora.
GitHubUnternehmerTUM, Munich
Designed a field robot for real-time raspberry plant gender classification and precision spraying. On-device inference via TensorRT on Jetson with custom CUDA pipeline.
Demo VideoEPFL, Lausanne
Autonomous robot with visual navigation using SLAM-based localization and path planning. Fused vision and LIDAR data for obstacle avoidance in dynamic environments.
TUM, Chair of Electronic Design Automation — 2020–2021
Contributed to the ETISS Instruction Set Simulator. Implemented a full ELF loader and memory layout manager for RISC-V architecture simulation.
Lauzhack Hackathon, Lausanne
Transformer-based model for 3D MRI breast cancer detection and segmentation. Built during a 24-hour hackathon with Docker-based inference pipeline.
TUM.AI, Munich
Anomaly detection on physiological time-series from wearable devices for early disease detection before symptom onset. Used neural ODE-inspired architectures.
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