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Suyi Zhang

Post-doctoral Researcher

University of Oxford

About

I am a post-doctoral researcher at the Nuffield Department of Neurosciences (NDCN), at University of Oxford. My current research focuses on understanding how humans learn and adapt to pain and relief by modelling brain imaging (fMRI/EEG) and physiological data with learning algorithms, supervised by Dr Ben Seymour. I am also interested in developing brain-computer interface and decoded neurofeedback systems for chronic pain treatment.

Interests

  • Pain systems neuroscience
  • Human brain imaging / modelling
  • Biomedical engineering
  • Machine learning

Education

  • PhD in Computational Neuroscience, 2018

    University of Cambridge

  • MSc in Biomedical Engineering, 2012

    University of Oxford

  • BEng in Biomedical Engineering, 2011

    University of Sheffield

Recent Publications

Encoding and Decoding of Pain Relief in the Human Brain
Suyi Zhang
PDF DOI
Endogenous controllability of closed-loop brain-machine interfaces for pain.
Suyi Zhang, Wako Yoshida, Hiroaki Mano, Takufumi Yanagisawa, Kazuhisa Shibata, Mitsuo Kawato, Ben Seymour
PDF Code Dataset DOI
Interlacing Personal and Reference Genomes for Machine Learning Disease-Variant Detection
Luke R. Harries, Suyi Zhang, Geoffroy Dubourg-Felonneau, James HR Farmery, Jonathan Sinai, Belle Taylor, Nirmesh Patel, John W. Cassidy, John Shawe-Taylor, Harry W. Clifford
PDF
The control of tonic pain by active relief learning
Suyi Zhang, Hiroaki Mano, Michael Lee, Wako Yoshida, Mitsuo Kawato, Trevor W. Robbins, Ben Seymour
PDF Dataset DOI
Thermosensory perceptual learning is associated with structural brain changes in Parietal–Opercular (SII) Cortex
Hiroaki Mano, Wako Yoshida, Kazuhisa Shibata, Suyi Zhang, Martin Koltzenburg, Mitsuo Kawato, Ben Seymour
DOI
See all publications

Recent Talks

Brain-machine interfaces for pain enhances endogenous modulation of experienced pain.

International Symposium on Translational Research in Brain Stimulation Nov-09-19
Invited talk as International Travel Award recipient.

The Reinforcement Learning Model of Pain.

The 11th Congress of the European Pain Federation (EFIC) Sep-05-19
Invited talk for the symposium ‘Avoidance Behaviour in Pain: Insights from Associative Learning, Computational Modelling and Brain Imaging’.

Projects

EEG-based neurofeedback system

Building an EEG-based brain-machine interface system as a prototype device for pain treatment.

Modelling aversive learning with fMRI

Using reinforcement learning algorithms to model human brain imaging and behavioural data during the learning of pain and relief.

Deep learning for variant calling

Generating pileup images from DNA sequencing data and using convolutional neural nets for variant calling.

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