Haiping Lu, University of Sheffield
Meet: https://meet.google.com/niy-gtpk-sro
YouTube Stream: https://youtube.com/live/PSHKhQyyztc
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Abstract:
Artificial intelligence is reshaping how we discover and design in science. This talk introduces a deployment-centric perspective on building trustworthy AI systems that connect generative creativity with real-world impact. I will present recent work from our group on MapDiff, a diffusion-based generative framework that models protein sequences from 3D structures, and DrugBAN, an interpretable bilinear attention network for drug–target prediction. These examples illustrate how generative and multimodal learning can accelerate discovery when guided by deployment-aware principles. By coupling generative capability with interpretability, multimodal integration, and domain knowledge, we move towards AI systems that advance both molecular research and the broader scientific enterprise.
Bio:
Haiping Lu is Professor of Machine Learning at the University of Sheffield, UK, where he leads AI Research Engineering at the Centre for Machine Intelligence. He is also Director of the UK Open Multimodal AI Network (UKOMAIN), funded by the UK Engineering and Physical Sciences Research Council (EPSRC). His research focuses on deployment-centric multimodal AI, integrating diverse types of data to tackle challenges in healthcare and scientific discovery, with methodological interests spanning foundation models, generative AI, domain adaptation, and transfer learning. His recent work spans brain and cardiac imaging, cancer diagnosis, protein design, and drug and materials discovery. He leads the open-source project PyKale for knowledge-aware machine learning and serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Cognitive and Developmental Systems. He has received awards from the Alan Turing Institute, Amazon, the Wellcome Trust, and the UK's National Institute for Health and Care Research.
The deployment-centric concept in the title builds on our recent Nature Machine Intelligence Perspective, available here: https://www.nature.com/articles/s42256-025-01116-5