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Digital Twins for Sustainable Development Goals

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Are you passionate about SDG and looking to innovate with Digital Twins? "Diverse thinking creates brilliant breakthroughs" - join our multidisciplinary team and take part in cutting-edge research.
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With its multidisciplinary approach, Digital Twins for Sustainable Development Goals (DT4SDG) brings together a range of subject-matter experts. Have a look at our ‘who’s who’ and find out more about their work.

Who we are and what we do:

  • Director and Industry Engagement: Dr Mona Jaber (IoT/ML/Digital Twin)
  • Technology Lead: Dr Jesús Requena Carrión (AI/ML expert)
  • Cloud/AI expert: Dr Ahmad Sayed 
  • IoT expert: Dr Matthew Tang
  • Communication expert: Professor Michael Chai
  • Domain expert in Cyber-Physical Systems for smart grids (University of Manchester): Dr Mahdieh Sadabadi
  • Domain expert in Energy: Professor Christian Beck (Digitally twinned grid)
  • Domain expert in Intelligent Transportation: Dr Jun Chen (multimodal journey – airport airside)
  • Domain expert in Future Mobility and Environmental and Climate Technologies: Professor Sergey Karabasov
  • Domain expert in Human Geography: Professor Phillippa Williams 
  • Domain expert in Earth-Centric AI: Professor Cedric John
  • Domain expert in Social Science for Smart Cities (LSE): Dr Anushri Gupta
  • Supporting academic - Professor Greg Slabaugh: Director of DERI – expert in AI/ML
  • Supporting academic - Dr Eirini Marouli expert in computational biology
  • Supporting academic - Dr Caroline Roney expert in computational medicine
  • Research staff - Dr Ruikang Zhong leading DASMATE research
  • PhD candidate - Chia-Yen Chiang working on multimodal data for smart mobility.
  • PhD candidate - Zunaira Nadeem working on machine learning for energy theft detection using IoT energy consumption data.
  • PhD candidate - Yuqin Liu working on energy efficiency in 5G and beyond multiple access schemes.
  • PhD candidate - Moudy Alshareef working on privacy-preserving deep learning for remote stress detection using IoT data

 

 

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