Welcome to my GitHub profile! I’m a PhD student in the Process Intelligence Research group at the Delft University of Technology, supervised by Artur Schweidtmann. My work lies at the intersection of artificial intelligence and chemical engineering, focusing on:

• 🕹 Reinforcement Learning
• 🔗 Graph Neural Networks
• 🧬 Generative Modeling
• 🧪 AI for Science

Beyond research, I’m an avid boulderer and climber 🧗‍♂️, always seeking new challenges both on the wall and in my projects!

• Deep reinforcement learning for process design: Review and perspective
  Current Opinion in Chemical Engineering (2024)
  A detailed review covering the application of reinforcement learning (RL) in chemical process design, exploring both theoretical foundations and practical case studies.

• Flowsheet generation through hierarchical reinforcement learning and graph neural networks
  AIChE Journal (2023)
  Introduced a novel framework combining hierarchical RL and graph neural networks (GNNs) to automate flowsheet design. This paper highlights advanced actor-critic logic.

• Transfer learning for process design with reinforcement learning
  Computer-Aided Chemical Engineering (2023)
  Demonstrates how transfer learning accelerates process optimization tasks in RL by leveraging knowledge from similar tasks.

• Graph neural networks for the prediction of molecular structure-property relationships
  Royal Society of Chemistry, Book Chapter (2023)
  An overview of GNNs for molecular property prediction, with practical examples demonstrating their power in chemistry applications.

• Self-supervised graph neural networks for polymer property prediction
  Molecular Systems Design & Engineering (2024)
  Applied self-supervised learning techniques to enhance GNN performance in predicting polymer properties.

• Modeling category-selective cortical regions with topographic variational autoencoders
  NeurIPS Workshop SVRHM (2021) - Best Paper Award
  Introduced the Topographic Variational Autoencoder (TVAE) to model localized category-selectivity in the brain, blending neuroscience and unsupervised learning.

• 🥇 Best Paper Award: NeurIPS 2021 Workshop SVRHM
• 🎖 Featured in AIChE Journal and Current Opinion in Chemical Engineering
• 📈 10+ highly cited publications on AI in chemical engineering

• 🌐 Personal Website
• 💼 LinkedIn
• 📧 Email

Feel free to explore my repositories and reach out for collaborations or discussions about AI, chemical engineering, and beyond! 🚀