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Yuan Shen

Independent Research Fellow

Computing and Technology

Role

Yuan Shen is a Independent Research Fellow in the School of Science & Technology.

Career overview

Yuan Shen obtained his diploma and PhD degrees in Physics at University of Zurich.

After his PhD study, he moved on to receive research training in the area of Computational Statistics and Machine Learning at University of Warwick, Aston University, and University of Birmingham.

Since 2017, he has been taken Teaching and Research academic positions, first as Lecturer at Xi'an JiaotongLiverpool University and now as Independent Research Fellow at Nottingham Trent University.

Research areas

  1. Machine Learning for Signal Processing (MLSP)
    1. Time-series Data Analysis by Bayesian Inference in Dynamical Systems;
    2. Spatio-temporal Data Analysis by Hierarchical Probabilistic Models;
    3. Classification of Spatial / temporal Data by Learning in Model Space;
    4. Graph Data Analytics by Deep Neural Networks / Higher-order Tensor Factorization
  2. MLSP for Healthcare
    1. Imaging Data Analytics:
      1. Brain imaging data in various conditions (fMRI in cognitive learning and/or dementia; EEG in sleep and/or epilepsy; PET in epilepsy);
      2. Multi-spectral imaging data in various conditions (retinal imaging in Age-related Macular Degeneration)
    2. Omics Data Analytics
      1. Genomics data in various conditions (e.g. gene expression data in breast cancer)
      2. Metabolomics data in various conditions (e.g.  dialysis LC-MS data in Cushing's disease)

Postdoctoral Research Training

  1. Markov Chain Monte Carlo for Models in Stochastic Geometry (an EPSRC project led by Dr Thonnes at The University of Warwick);
  2. Variational Inference in Stochastic Dynamic Models (an EPSRC project led by Dr Cornford at Aston University);
  3. Multispectral Retinal Image Analysis: A new technique for the assessment of Age-related Macular Degeneration (a Dunhill Medical Trust project led by Dr Styles at The University of Birmingham)
  4. Unified probabilistic modelling of adaptive spatial temporal structure in the human brain (a BBSRC project led by Prof Tino at The University of Birmingham);
  5. Personalised medicine through learning in the model space (an EPSRC project led by Prof Tino at The University of Birmingham).

Student Supervision

  1. PhD student projects
    • Nahed Alowadi (completed in 2018) – Population-level spatio-temporal probabilistic modeling of fMRI data (Co-supervision with 25% capacity, Director of study is Prof Peter Tino at School of Computer Science of The University of Birmingham)
    • Hanin Alahmadi (completed in 2019) – Efficient Feature Extraction Methods for High-order Tensor Data (Co-supervision with 25% capacity, Director of study is Prof Peter Tino at School of Computer Science of The University of Birmingham);
  2. MSc/MRes student projects
    • Gulrukh Turabee (completed in 2019) – Classification of all-night  EEG data for predicting sleep stages: a deep learning approach (Co-supervision with 50% capacity at Nottingham Trent University)
    • Ruchi Bharat Patel (completed in 2020) – Unsupervised feature selection for identification of bio-marker genes: a deep learning approach (Nottingham Trent Univeristy);
  3. FYP student projects
    • Shengjie Sun (completed in 2018) – Efficient Markov Chain Monte Carlo Algorithms for Bayesian Inference in Dynamical Systems (Supervision at XJTLU)

Projects

  1. Understanding dynamic steroid biosynthesis in health and disease through machine learning in the space of mechanistic models (co-PI, March - August 2020, seed corn project funded by EPSRC Centre for Predictive Modelling in Healthcare)