Dr Samaneh Kouchaki
Academic and research departmentsCentre for Vision, Speech and Signal Processing (CVSSP), Department of Electrical and Electronic Engineering.
I joined CVSSP in July 2020 to lead research and teaching in machine learning for health and dementia care in collaboration with the UK Dementia Research Institute (UK DRI) Care Research & Technology Centre (a joint initiative between CVSSP, the Surrey Sleep Research Centre and Department of Mathematics at Surrey, and Imperial College London).
I previously spent three years as a postdoctoral researcher within the Institute of Biomedical Engineering at the University of Oxford. I was the senior machine learning researcher for the ‘100,000 Genomes Project for Tuberculosis’, an international consortium involving the Centres for Disease Control of most major nations (including the USA, UK and China), jointly funded by the Gates Foundation and the Wellcome Trust. My research focus was on the prediction of antibiotic resistance in pathogens such as those that cause tuberculosis.
Prior to this, I was at the University of Manchester within the Division of Evolutionary and Genomic Sciences where I was funded by the EU Horizon 2020 Virogenesis project, working on next-generation DNA sequencing using signal and image processing techniques coupled with unsupervised machine learning.
I obtained my PhD in Computer Science at Surrey in 2015. My PhD focused on developing novel multi-way techniques for source separation with application to biomedical signals.
Areas of specialism
Biomedical signal processing; deep supervised/semi-supervised learning for healthcare data; graph learning and embedding for omics data; time-series data processing and pattern analysis.
Dr Kouchaki’s research is aimed at improving patient care by providing decision support. Her objective is to develop intelligent tools, based on hybrid architectures of advanced probabilistic and deep learning techniques, that facilitate improved patient outcomes.
PhD research positions
Dr Kouchaki is currently looking to supervise PhD students interested in exploring the following topics:
Deep interpretable learning for healthcare data:
Interpretability is an important factor in healthcare as it helps clinicians understand how the model is working and also discover the important clinical variables. Adding interpretability to deep learning techniques would allow their deployment in clinical use.
Machine learning and signal processing for genetic data:
Omics-based research includes the study of multiple genetic resources (proteomics, genomics, metabolomics) and provides biological insights for many healthcare applications. Traditionally, machine learning techniques have been employed successfully in analysing individual genetic resources but not multi-omics. Multi-omics research is vital for understanding complex biological systems.
Heterogeneous graph embedding and graph convolutional networks for multi-sensor data analysis:
The aim is to develop a sophisticated and robust framework for embedding multiple features for a joint dense representation of the data to better predict the patient’s outcome by applying deep learning algorithms.
Postgraduate research supervision
LABORATORIES DESIGN & PROFESSIONAL STUDIES III and IV (EEE2036 and EEE2037)