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Director, UK Open Multimodal AI Network | Professor of Machine Learning, University of Sheffield
🏛️ I am a Professor of Machine Learning at the School of Computer Science and the Head of AI Research Engineering at the Centre for Machine Intelligence, University of Sheffield. I am also the Director of the UK Open Multimodal AI Network (UKOMAIN), funded by EPSRC, building on the Meta-learning for Multimodal Data interest group at the Alan Turing Institute. 🖥️ My research focuses on deployment-centric multimodal AI for healthcare and scientific discovery: - Multimodal AI: Foundation models, generative AI, domain adaptation, and transfer learning. - Healthcare: Brain and cardiac imaging, and cancer diagnosis and treatment. - Scientific discovery: Protein engineering, and drug and materials discovery. I lead the development of the open-source software library PyKale, part of the PyTorch ecosystem, enabling accessible machine learning for interdisciplinary research. 🧩 Editorial Service - Editorial Board Member, Science and AI (Springer Nature), 2026–present - Associate Editor, IEEE Transactions on Neural Networks and Learning Systems, 2022–2025 - Associate Editor, IEEE Transactions on Cognitive and Developmental Systems, 2022–2025 🏅 Selected Awards - Turing Network Development Award - Amazon Research Award - NIHR AI in Health and Care Award - Wellcome Trust Innovator Awards: Digital Technologies My detailed CV is available at https://haipinglu.github.io/files/cv.pdf Follow our UKOMAIN LinkedIn page for the latest updates and opportunities: https://www.linkedin.com/company/ukomain
University of Toronto
Ph.D., Electrical and Computer Engineering
January 1, 2004 – January 1, 2008
Nanyang Technological University Singapore
M.ENG & B.ENG, EEE
January 1, 1997 – January 1, 2003
UK Open Multimodal AI Network (UKOMAIN)
Director, UK Open Multimodal AI Network (UKOMAIN)
January 1, 2025 – Present
Sheffield, England, United Kingdom · On-site
The University of Sheffield
Turing Academic Lead
April 1, 2023 – Present
The University of Sheffield
Professor of Machine Learning
January 1, 2023 – Present
The University of Sheffield
Head of AI Research Engineering
January 1, 2023 – Present
The University of Sheffield
Turing Network Development Award Lead
February 1, 2022 – March 1, 2023
The University of Sheffield
Insigneo Research Director for Healthcare Data / AI
November 1, 2021 – October 1, 2023
The University of Sheffield
AI Strategy Lead
March 1, 2021 – Present
The University of Sheffield
Senior Lecturer in Machine Learning
January 1, 2020 – December 1, 2022
The University of Sheffield
Lecturer in Machine Learning
November 1, 2016 – December 1, 2019
Hong Kong Baptist University
Assistant Professor
August 1, 2013 – July 1, 2016
Institute for Infocomm Research (I2R)
Scientist I
October 1, 2009 – July 1, 2013
University of Toronto
Post-doctoral Fellow
August 1, 2008 – August 1, 2009
University of Toronto
Research Assistant, Teaching Assistant
January 1, 2004 – August 1, 2008
Nanyang Technological University
Undergraduate student and Master student
July 1, 1997 – December 1, 2003
PyKale: accessible machine learning from multiple sources for interdisciplinary research
June 1, 2020 – Present
PyKale is a library in the PyTorch ecosystem aiming to make machine learning more accessible to interdisciplinary research by bridging gaps between data, software, and end users. Both machine learning experts and end users can do better research with our accessible, scalable, and sustainable design, guided by green machine learning principles. PyKale has a unified pipeline-based API and focuses on multimodal learning and transfer learning for graphs, images, texts, and videos at the moment, with supporting models on deep learning and dimensionality reduction. PyKale enforces standardization and minimalism, via green machine learning concepts of reducing repetitions and redundancy, reusing existing resources, and recycling learning models across areas. PyKale will enable and accelerate interdisciplinary, knowledge-aware machine learning research for graphs, images, texts, and videos in applications including bioinformatics, graph analysis, image/video recognition, and medical imaging, with an overarching theme of leveraging knowledge from multiple sources for accurate and interpretable prediction.
Cultural Fit Analysis
The candidate's background is heavily academic and research-oriented, with a focus on leading large-scale AI initiatives and developing open-source tools. While this demonstrates a strong commitment to advancing AI, the target role of 'Data Analyst' typically requires more direct experience with business intelligence, data visualization, and stakeholder communication in a corporate setting. The candidate's project diversity is strong within the AI/ML research domain, but less so in traditional data analysis applications. This could indicate a potential mismatch in day-to-day responsibilities and cultural expectations compared to a typical data analyst role.
Soft Skills & Operational Fit
The candidate's extensive leadership roles in academic and research settings suggest strong organizational, strategic planning, and collaboration skills. Their work on PyKale indicates a focus on accessibility and sustainability in machine learning, which aligns with modern operational best practices. However, the provided data does not offer direct insights into typical corporate soft skills like direct client communication or agile team participation outside of an academic context.