CHEME AI Lab
Chemical Engineering & Medical Imaging AI Lab
Integrating fundamental applied science understanding into technology has been a continuous process throughout history, and the CHEME AI Lab works to make today’s data-driven artificial intelligence (AI) stronger. We investigate knowledge-driven AI and demonstrate its potential within two applied science domains in particular – chemical engineering and imaging physics. Our research also applies to a wider range of applications. It explores fundamental ways to instil knowledge into all key components of AI: data acquisition, algorithm design, user interaction and deployment. Such knowledge-driven AI aims to be more interpretable and reliable than purely data-driven AI, and it will further drive future scientific development in chemical engineering and imaging physics.
The CHEME AI Lab is part of the TU Delft AI Labs programme.
Digitisation of engineering diagrams is a key enabler for machine learning and digital twins.
AI and medical imaging to noninvasively examine heart tissue health.
Following brain biomarkers longitudinally.
The team
Directors
Artur Schweidtmann
Faculty of AS
Lab director
Qian Tao
Faculty of AS
Lab director
PhD's
Lukas Schulze Balhorn
PhD candidate
Qinghe Gao
PhD candidate
Dominik Goldstein
PhD candidate
Giacomo Lastrucci
PhD candidate
Tanuj Karia
PhD candidate
Max Theisen
PhD candidate
Yi Zhang
PhD candidate
Yidong Zhao
PhD candidate
Education
Master Projects
Openings
- M.Sc. Thesis for Artificial Intelligence in Chemical Engineering Research
This thesis is about the application and development of machine-learning and artificial intelligence algorithms within chemical engineering. We offer a variety of master projects related to deep learning, computer vision, data mining, process modelling. We apply our methods for the optimal design and operation of more sustainable processes. For more information contact: a.schweidtmann@tudelft.nl
- M.Sc. Thesis for Artificial Intelligence in Chemical Engineering Industry
This thesis is about the industrial application and development of machine-learning and artificial intelligence algorithms within chemical engineering. We offer a variety of master projects in close collaboration with industry. The topics are related to deep learning, computer vision, data mining, process modelling. We apply our methods for the optimal design and operation of more sustainable processes. For more information contact: a.schweidtmann@tudelft.nl
- M.Sc. Thesis for Artificial Intelligence in Medical Image Analysis
We offer a variety of interesting master projects on AI for medical image analysis, covering medical image segmentation, registration, and interpretation, towards high-performance, generalisable, and interpretable AI for medical imaging. For more information contact: q.tao@tudelft.nl
News
NWO Open Science Fund
Artur Schweidtmann has won a NWO Open Science Fund with his proposal for an open-source knowledge graph database for chemical engineering. His project is one of the 26 projects that were honoured by this call for the first time this year (and the only one from TU Delft). The ‘ChemEng KG’ aims to accelerate process development in academia and industry.
Flowsheet simulations are crucial for (bio-)chemical process development. However, no public database for flowsheet simulation files exists, which is a major hurdle as knowledge from earlier simulations is not easily findable, accessible, interoperable, and reusable.
“We envision establishing an open-source knowledge graph database for flowsheet simulation data that is FAIR and linked to leading initiatives in the open science community. The ‘ChemEng KG’ will accelerate process development in academia and industry. In addition, it will pave the way for automated process design through optimisation and machine learning."
