🔬 Research: Self-Supervised Learning, Graph & Geometric Neural Networks | ⚡ Skills: JAX, PyTorch, TensorFlow, Julia, Python
🔭 Currently working on: Home Energy Simulation (JAX, MPC, RL) | Action-adaptive Continual Learning for RL Agents
🚀 Recent research: 3D Object Detection (Self-Supervised) | Molecule Modeling (GNNs) | Non-Convex Consensus Optimization
| Project / Paper | Description | Link |
|---|---|---|
| Home Energy Simulation | High-performance high-fidelity simulation in JAX with differentiable MPC, continuous-time deep RL, neural ODEs and system identification. Running 200 million steps per second across parallel environments on a laptop GPU. | GitHub |
| Action-adaptive Continual Learning | Continual learning framework for reinforcement learning agents, adaptive to changing tasks. | Coming Soon |
| 3D Object Detection (Self-Supervised) | Efficient self-supervised methods for autonomous driving 3D object detection. | |
| Molecule Modeling with GNNs | Graph neural networks for molecular property prediction. | GitHub |
| Consensus-Based Optimization | Experimental framework and methods for non-convex consensus optimization. | GitHub |
| Robust Quantum Variational Algorithms | Influence of Noise on Quantum Variational Algorithms. | |
| Quantum-Classical MILP Optimization | Evaluation of quantum-classical hybrid solution methods for 3SAT problems. (German) |