Sheffield AI Research Engineering

The AI Research Engineering (AIRE) team is part of the University of Sheffield’s multi-million-pound investment to establish the new Centre for Machine Intelligence, working towards maximising the benefits of AI to promising and impactful areas of research across the university.

Leading AI and machine learning research is at the forefront of our endeavors, especially when it transcends disciplinary boundaries. We are committed to not only pioneering these innovations but also to crafting standardized open-source software solutions. This approach ensures that our research reaches the widest possible audience, maximizing its potential impact on the world.

The AIRE team works collaboratively with the Sheffield Data Science and AI Community in order to:

  • assess proposed AI/ML projects, analysing the requirements, feasibility, and impact
  • conduct AI/ML research to address gaps and challenges
  • design, build, and deploy software libraries following FAIR principles
  • disseminate research outcomes via top research papers, tutorials, hackathons

Stay updated on Sheffield AI Research Engineering opportunities and news. Subscribe to our AIRE Google Group to be the first to know about job openings and key announcements. Join now!

The AIRE information session video recording used during the AIRE recruitment is given below for reference.



Projects

See our current AIRE projects below, which can be filtered by the tags. Explore a list of all projects »

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Multimodal AI for Parkinson’s Disease
Utilizing advanced artificial intelligence to process multimodal data to enhance the diagnosis and prognosis of Parkinson’s Disease, paving the way to uncover the underlying mechanisms of Parkinson’s Disease and improve its prediction.
Multimodal AI for Parkinson's Disease
Digital Materials Discovery
Using digital databases and advanced AI to predict new, sustainable materials for green technologies, accelerating the shift towards net-zero emissions and driving substantial progress in global sustainability initiatives.
Digital Materials Discovery
Multi-fidelity Fusion and Optimization Theory and Applications
Exploring innovative methods to optimize AI models using multi-fidelity data, aiming to enhance accuracy and efficiency across engineering disciplines by leveraging diverse data sources to improve model performance while reducing reliance on high-cost precision data.
Multi-fidelity Fusion and Optimization Theory and Applications
AI Brain Imaging for Nerve Pain Detection (UKRI funded)
Harnessing advanced AI technology to discern novel biomarkers, paving the way for enhanced chronic nerve pain treatments and revolutionising healthcare outcomes.
AI Brain Imaging for Nerve Pain Detection (UKRI funded)
Text Correction for Historical Documents
Leveraging deep learning to refine OCR transcriptions of the extensive British Library Newspapers collection to overcome the barrier of inaccurate text data, unveiling a rich resource for exploring centuries of historical narratives and advancing global humanities research.
Text Correction for Historical Documents
Multimodal Cardiothoracic Disease Prediction
Utilising advanced AI to process multimodal cardiothoracic data for enhanced diagnosis and prognosis of Cardiothoracic Disease (CTD), paving the way for personalised medical care and transformative approaches in heart and lung health.
Multimodal Cardiothoracic Disease Prediction

Meet the Team

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Haiping Lu

Head of AI Research Engineering, Professor of Machine Learning, and Turing Academic Lead

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Haolin Wang

AI Research Engineer

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Jiayang Zhang

Deputy Assistant Head of AI Research Engineering, and AI Research Engineer

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Mohammod Suvon

AI Research Engineer

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Shuo Zhou

Deputy Head of AI Research Engineering, and Academic Fellow in Machine Learning

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Wenrui Fan

AI Research Engineer

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Xianyuan Liu

Assistant Head of AI Research Engineering, and Senior AI Research Engineer

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Prasun Tripathi

Visiting Researcher

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Alan Thomas

AI Research Engineer

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Lalu Muhammad Riza Rizky

AI Research Engineer

Contact Us

Please feel free to reach out to us via the email below.

Acknowledgement

The AIRE team is partially supported by the following project(s) and donation(s):

  • EPSRC Reference EP/Y017544/1: “A Novel Artificial Intelligence Powered Neuroimaging Biomarker for Chronic Pain”.
  • Donation from alumni medics Dr. Dinesh and Dr. Sandra Naik.