Machine Learning

In addition to the projects highlighted below, we are part of the project Maths4DL - Mathematics for Deep Learning. It aims to put deep learning onto a firm mathematical grounding, and will combine theory, modelling, data and computation to help unlock the next generation of deep learning. This project is in collaboration with the University of Bath and University College London.

Image Classification Under Minimal Supervision: Graph-Based Semi-Supervised Learning for Real-World Large-Scale Problems.

Reseacher: Angelica Aviles-Rivero, and Carola-Bibiane Schönlieb

Learning Sampling Patterns for MRI

Researcher: Ferdia Sherry, Erlend Riis, Luca Calatroni, and Carola-Bibiane Schönlieb

Machine learning methods for segmentation of food microscopy images

Researcher: Rihuan Ke, Carola-Bibiane Schönlieb and Peter Schuetz

Model-Based and Deep Learning-Based Approaches for Image Restoration for Real-World Problems: Image Reflection Removal and Image Deraining.

Researcher: Angelica Aviles-Rivero, and Carola-Bibiane Schönlieb

Semi-Supervised Hyperspectral Image Classification

Researcher: Angelica Aviles Rivero, Philip Sellars, David Coomes, Nicolas Papadakis, Carola-Bibiane Schönlieb

Unsupervised Learning for Image Analysis

Researcher: Angelica Aviles-Rivero, Hankui Peng, Lihao Liu, and Carola-Bibiane Schönlieb

Conditional Image Generation with Score-Based Diffusion Models

Researcher: Georgios Batzolis, Jan Stanczuk, Carola-Bibiane Schonlieb and Christian Etmann.

Dynamical Systems-based Approaches to Designing Robust Neural Networks

Researcher: Ferdia Sherry

Flow of Microtubules in the Drosophila Oocyte

Researcher: Lukas F. Lang, Maik Drechsler, Hendrik Dirks, Martin Burger, Carola-Bibiane Schönlieb, Isabel M. Palacios

Multi-Tasking Models for Image Analysis

Researcher: Angelica Aviles Rivero, Veronica Corona, Noémie Debroux, Carola-Bibiane Schönlieb

Multi-sensor Remote Sensing for the Detection of Individual Trees

Researchers: Sören Dittmer, Jonathan Williams, Carola-Bibiane Schönlieb, Tom Swinfield, David A. Coomes, Juheon Lee, Xiaohao Cai

Semi-Supervised Hyperspectral Image Classification

Researcher: Angelica Aviles Rivero, Philip Sellars, David Coomes, Nicolas Papadakis, Carola-Bibiane Schönlieb

INTEGRAL: Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India

Researchers: Madeleine Kotzagiannidis, Angelica Aviles-Rivero, Rihuan Ke, Carola-Bibiane Schönlieb, David Coomes, James Woodcock, and Rahul Goel

Machine learning methods for segmentation of food microscopy images

Researcher: Rihuan Ke, Carola-Bibiane Schönlieb and Peter Schuetz

Multi-sensor Remote Sensing for the Detection of Individual Trees

Researchers: Sören Dittmer, Jonathan Williams, Carola-Bibiane Schönlieb, Tom Swinfield, David A. Coomes, Juheon Lee, Xiaohao Cai

Cambridge Image Analysis

Image Classification Under Minimal Supervision: Graph-Based Semi-Supervised Learning for Real-World Large-Scale Problems.

Learning Sampling Patterns for MRI

Machine learning methods for segmentation of food microscopy images

Model-Based and Deep Learning-Based Approaches for Image Restoration for Real-World Problems: Image Reflection Removal and Image Deraining.

Semi-Supervised Hyperspectral Image Classification

Unsupervised Learning for Image Analysis

Conditional Image Generation with Score-Based Diffusion Models

Dynamical Systems-based Approaches to Designing Robust Neural Networks

Flow of Microtubules in the Drosophila Oocyte

Multi-Tasking Models for Image Analysis

Multi-sensor Remote Sensing for the Detection of Individual Trees

Semi-Supervised Hyperspectral Image Classification

INTEGRAL: Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India

Machine learning methods for segmentation of food microscopy images

Multi-sensor Remote Sensing for the Detection of Individual Trees

Study at Cambridge

About the University

Research at Cambridge