Biomedical Intervention Optimisation lab
Modern machine learning algorithms have achieved unprecedented accuracy in image and video understanding tasks, via pure learning from data. These powerful abilities come at the price of enormous amounts of training data, memory, and computational requirements. Such resources are rarely available to real-time feedback systems in medical intervention and biomedical research.
Experts will join forces in the BIOLab across many fields including computer vision, reinforcement learning, neural architecture, deep learning and computational physics, and biomedical imaging. We will create high-efficiency, real-time, AI-driven feedback and control in biomedical applications. The focus is on improving the efficiency of machine learning algorithms by designing novel artificial neural network architectures, developing new reinforcement learning and generative algorithms, and incorporating biologically inspired neural network models. These newly developed concepts and algorithms will be applied to a wide range of problems in biomedical applications. Examples include optimizing tumour irradiation protocols with missing information, and limiting irradiation damage to delicate living samples in smart microscopy.
The BIOLab is part of the TU Delft AI Labs programme.
Course Deep Reinforcement Learning (CS4400) for MSc Computer Science.
Project supervision in the Deep Learning (CS4240) course for MSc Computer Science.
Project supervision in the Research Project (CSE3000) course for BSc Computer Science.
Guest lecture on "Event-Based Vision" in the Computer Vision by Deep Learning (CS4245) course for MSc Computer Science.
Guest lecture on "Neuromorphic Computing" in the Machine Learning 2 (CS4230) course for MSc Computer Science.
NB2121 Image Analysis for Bachelor Nanobiology: including a lecture on deep learning in the context of image analysis
Supervision of MSc Graduation Projects for the Computer Science program (IN5000).
AI minor and AI Master Block programs
Project supervision for the Capstone Applied AI Project (TI3150TU), Fundamentals of AI (IFEEMCS520100) and Interdisciplinary Advanced AI Project (IFEEMCS520200) courses.