Experience

ML Engineer — Reinforcement Learning

• Develop and evaluate RL locomotion controllers for quadrupeds deployed in industrial inspection.
• Run end-to-end experiments across GPU simulation, policy training, robustness evaluation, and sim-to-real iteration.
• Design curricula and scenario suites targeting rare locomotion failures.
• Led and secured a EuroHPC grant enabling up to 50,000 H100 GPU-hours for large-scale RL robustness and scaling experiments.

Research Assistant

• Developed inverse RL and imitation-learning methods for skill assessment and robust reward learning.
• Built reinforcement-learning benchmarks and assistance policies in surgical digital twins.
• Studied causal invariance and Wasserstein objectives under environment shift.
• Supported teaching in advanced machine learning, learning theory, and game theory.

Research Assistant

• Extended sequence-to-sequence models with structured variational inference.
• Investigated hyperbolic Wasserstein autoencoders for hierarchical representations.

Electrical Engineer

• Developed energy-harvesting wireless sensor networks for monitoring electrical machines.

PhD Computer Science (ML/AI)

Reinforcement learning from demonstrations in surgical digital twins.

MSc Electrical Engineering (Intelligent Systems)

Machine learning, statistical learning theory, and probabilistic AI.

BSc Electrical Engineering

Control theory, embedded systems, and electrical engineering.