Haiping Lu

Director of the UK Open Multimodal AI Network, Professor of Machine Learning, and Head of AI Research Engineering

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. Subscribe to the Multimodal AI Community mailing list for updates on UKOMAIN.

🖥️ My research focuses on translational multimodal AI technologies for healthcare and scientific discovery:

Multimodal AI: Foundation models, generative AI, domain adaptation, and transfer learning.
Healthcare: Brain/cardiac imaging, and cancer diagnosis/treatment.
Scientific Discovery: Protein engineering, and drug/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.

🏅 I serve as an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Cognitive and Developmental Systems. My awards include a Turing Network Development Award, an Amazon Research Award, and joint Wellcome Trust Innovator and NIHR AI in Health and Care awards.

📘 Three of my MSc dissertation students have won the Fretwell-Downing Prize for the best MSc Dissertation: Peizhen Bai (2017), Hao Xu (2019), and Mohammod N. I. Suvon (2022). Learn more about the whereabouts of my past team members.

📩 PhD enquiries: Email me ONE PDF including your CV, a statement on why and your source of funding, 1-3 papers, and transcripts before applying. Also check the English language requirements.

📚 Research & Publications

Adventure and voyage of discovery

Our research advances multimodal AI technologies for healthcare and scientific discovery, resulting in high-impact publications and open-source software. Explore selected projects and their associated papers below, or view the full list of projects for more details.

📄 Publication Profiles: Google Scholar | ResearcherID | Scopus Author ID | Semantic Scholar

🔍 Explore publications up to Feb 2022 >. Publications after Feb 2022 are not updated here due to time constraints. For more recent publications, view the full list of publications in my CV (page 2 onward) or visit Google Scholar.

Inverse protein folding via denoising diffusion

Design proteins using mask prior-guided denoising diffusion for inverse protein folding

Multimodal VAE for low-cost cardiac assessment

Develop a cost-effective cardiac instability detection tool using multimodal variational autoencoder

Molecular property prediction via line graph transformer

Predict molecular properties with geometry-aware line graph transformer pre-training

Drug–target interaction prediction via bilinear attention network

Advance drug discovery with interpretable bilinear attention network and domain adaptation

Uncertainty estimation for landmark localisation

Quantify the uncertainty in automatic anatomical landmark localisation

PyKale: open-source multimodal learning software library

Enable accessible machine learning from multiple data sources for interdisciplinary research

Mixed-order spectral clustering for networks

Model both second-order and third-order structures simultaneously for complex networks

Multisite brain fMRI classification

Classify autism across multiple sites via site-dependence minimisation & second-order functional connectivity

Learnable GCN aggregator

Construct a feature-importance-aware and robust aggregator for graph convolutional networks

Learn via tensor modelling

Learn low-dimensional representations of high-dimensional data from their natural tensors

🌱 The KALE Group

📩 PhD enquiries: Email me ONE PDF including your CV, a statement on why and your source of funding, 1-3 papers, and transcripts before applying. Also check the English language requirements.

KALE (Knowledge-Aware machine LEarning) leverages diverse data sources and expert knowledge to advance AI and machine learning applications, focusing on green and healthy AI. Below is our July 2024 group photo; find older photos from 2023, 2021, 2020, and 2019. Explore profiles of current research staff, students, collaborators, and alumni below.

Research Staff and Students

Xianyuan Liu

Assistant Head of AI Research Engineering & Senior AI Research Engineer

Video Domain Adaptation, Machine Learning, AI Engineering

Mohammod Suvon

AI Research Engineer & PhD Student

Multimodal Learning, Meta-Learning, Natural Language Processing

Prasun Tripathi

Visiting Researcher

Medical Image Analysis, Machine Learning, Computer Vision

Wenrui Fan

AI Research Engineer & PhD Student

Multimodal Learning, Meta-Learning, Self-supervised Learning, Causality, Computer Vision

Alan Thomas

AI Research Engineer & PhD Student

Generative AI, Foundation Models, Natural Language Processing

Haolin Wang

AI Research Engineer & PhD Student

Domain Adaptation, Generative AI, Feature Fusion

Jiayang Zhang

AI Research Engineer

Machine Learning for Healthcare, Bioinformatics, Medical Imaging

Lalu Muhammad Riza Rizky

AI Research Engineer

Probabilistic Machine Learning, Neuroimaging, Representation Learning

Pawel Pukowski

PhD Student

Machine Learning Theory, Deep Learning

Sina Tabakhi

PhD Student

Feature Selection for Big Data, Multi-omics Data Integration

Collaborators

Filip Miljković

Associate Principal AI Scientist at AstraZeneca

Andrew Swift

Professor of Cardiothoracic Radiology at University of Sheffield

Chen Chen

Lecturer in Computer Vision at University of Sheffield

Shuo Zhou

Academic Fellow at University of Sheffield (past PhD Student)

Venet Osmani

Professor of Clinical AI and Machine Learning at Queen Mary University of Londo

Gaolang Gong

Professor at Beijing Normal University

Dinesh Selvarajah

Senior Clinical Lecturer in Diabetes at University of Sheffield

Kathy Christofidou

Chair in Digital and Sustainable Metallurgy at University of Sheffield

Nicola Morley

Professor in Material Physics at University of Sheffield

Wei Sang

Professor at Shanxi Medical University

Alumni

Peizhen Bai

PhD Student (now a Senior Machine Learning Scientist at AstraZeneca)

Lawrence Schobs

PhD Student

Hao Xu

MSc Student (now a PhD student at UCSD)

Raivo Koot

BSc Student (now an MLOps Engineer at Apple)

Li Zhang

PhD Student (now an Associate Lecturer at University College London)

Yan Ge

PhD Student (now a Lecturer in Financial Technology at University of Bristol)

Alexandra Herghelegiu

BSc Student (now a Software Engineer at Apple)

Yang Zhou

PhD Student (now a Senior Scientist at IHPC, A*STAR)

Johanna Uthoff

Postdoctoral Research Associate (now a Data Engineer at Auto Trader)

Wenwen Li

Postdoctoral Research Associate (now a Senior AI Training Engineer at Matlab)

Qiquan Shi

PhD Student (now an AI Researcher at Huawei)

🎓 Teaching Highlights

Learn to teach, teach to learn

I developed and lead a course on An Introduction to Transparent Machine Learning, part of the Alan Turing Institute’s online learning courses in responsible AI.

At the University of Sheffield, I taught two modules below, with teaching materials available on GitHub via links below and video lectures on YouTube. For a full teaching history, see my CV.

Spring 2017-2023, 2025: COM6012 Scalable Machine Learning
Fall 2018-2021: COM4509/6509 Machine Learning and Adaptive Intelligence