Albrecht Group: "Advanced data analysis and Deep Learning in single-molecule science"
We are using and developing tools for advanced single-molecule data analysis and unsupervised classification, including dimensionality reduction techniques, Convolutional Neural Networks and Transfer Learning approaches for applications in sensing and single-molecule electronics.
Categories: unsupervised classification; Transfer Learning; Deep Learning
Chakrabarti Group: "Machine learning soft materials"
We are interested in machine learning techniques for classifying colloidal crystal structures as well as recognising patterns in seemingly disordered soft materials susceptible to thermal fluctuations.
Categories: pattern recognition; classification; materials;
Gupta Group: "Optical sensing"
We are developing label-free optical methods and technologies for sensing of chemical and biological analytes. Hydrogels and biconjugation approaches are key to the development of these methods/ technologies. The raw output of these sensors is in the form of images, which are processed in real-time for continuous monitoring of the concentration of analytes. We are interested in exploring the use of AI to analyse the output of our sensors.
Categories: platforms; materials, data
Herten Group: "Fluorescent probes and single-molecule spectroscopy"
School of Chemistry
Chair in Cell Biology of Membrane Proteins
Institute of Cardiovascular Sciences
We are developing spectroscopy tools for quantitative and super-resolution fluorescence microscopy. Aiming at protein counting and 3D super-resolution representation of protein networks, we are highly interested to further improve interpretation of spectra and images and to support the design of new fluorescent probes by use of machine-learning approaches.
Categories: probes; platforms
Neely Group: " New tools for understanding genomes"
We develop novel chemistry that allow us to tag specific genomic sequences. We are using these chemistries in imaging DNA sequence, in nanopore sequencing experiments and as a way to better understand the epigenome. Our work creates large, complex but information-rich datasets and we employ AI to help us better understand this data.
Categories: probes; data.
Styles Group: "Computational Chemical Imaging"
We work on understanding complex data from imaging experiments that directly probe sample chemistry, including labelled techniques such as fluorescence microscopy, and unlabelled techniques such as Raman and Mass Spectrometry Imaging.
Categories: imaging; probes; analytes