Leadership
Lead AI Researcher at Manukai AG, Zürich
Led AI research at an AI for Manufacturing company (3D CAD models to CAM instructions).
Expert in Computer Vision & Signal Processing
Specialized in signal denoising and video understanding, enabling the processing of high-throughput sensor data for defect detection.
Specialist in Graph & Recurrent Neural Networks
Engineered advanced architectures utilizing Graph Neural Networks and Recurrent Neural Networks to capture spatial and temporal dependencies, directly applicable to disentangling complex process orders in manufacturing root cause analysis.
Advanced Knowledge in Bayesian ML
Proven track record of utilizing Bayesian ML approaches, critical for manufacturing root cause analysis.
Architect of Real-Time ML Pipelines
Designed and deployed production-grade real-time detection systems and CI/CD infrastructures on cloud, enabling the robust implementation of predictive maintenance models required to minimize equipment downtime.
Senior Researcher at Harvard & ETH Zürich
10+ years in Computational Science, specializing in high-fidelity algorithms for complex physical systems.
Expert in AI for Physical Problems
Creator of "ODIL", a machine learning framework that solves inverse physical problems 100K times faster, relevant to reconstructing sub-micron defect shapes from optical sensor data.
High-Performance Computing Architect
Engineered massive-scale parallel solvers for supercomputers, capable of handling the real-time, high-throughput data streams, relevant to production yield optimization.
Complex Systems Simulation Lead
Lead developer of multi-physics engines (Aphros, uDeviceX), with deep expertise in modeling temporal dependencies and fluid dynamics relevant to production yield analysis.
Software Architect
Extensive proficiency in C++, CUDA, and parallel computing, relevant to the deployment of optimized DL models for predictive maintenance and equipment fault detection.