{"id":13617,"date":"2026-01-29T18:01:00","date_gmt":"2026-01-29T17:01:00","guid":{"rendered":"https:\/\/www.uni.lu\/lcsb-en\/?post_type=events&p=13617"},"modified":"2026-01-29T18:01:22","modified_gmt":"2026-01-29T17:01:22","slug":"artificial-intelligence-for-bioscientific-research-prof-pengyi-yang","status":"publish","type":"events","link":"https:\/\/www.uni.lu\/lcsb-en\/events\/artificial-intelligence-for-bioscientific-research-prof-pengyi-yang\/","title":{"rendered":"Artificial Intelligence for Bioscientific Research – Prof. Pengyi Yang"},"content":{"rendered":"\n
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This is an online event.<\/strong> Tune in via Webex here<\/a>.<\/p>\n\n\n

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\nDeep learning methods for integrative analysis of single-cell omics data<\/h2>\n\n\n\n

Single-cell multimodal omics technologies provide unprecedented opportunities to characterise cell identity and cell-fate decisions, but they also pose fundamental challenges for data integration, feature selection, and robust inference across modalities and tasks. In this talk, I will present a series of deep learning frameworks developed to address these challenges through multi-task and ensemble learning strategies. I will first introduce a multi-task deep learning framework for integrative analysis of multimodal single-cell data. I will then discuss systematic benchmarking of deep learning\u2013based feature selection methods, highlighting a trade-off between predictive performance and reproducibility, and present an ensemble deep learning approach that improves robustness, particularly for rare cell types and multimodal data. Finally, I will outline recent efforts toward multi-task benchmarking of single- cell multimodal integration methods, emphasising the need for task-aware evaluation frameworks. Together, these examples illustrate how deep learning can be used not only for performance gains, but also for principled and reproducible analysis of complex single-cell omics data.<\/p>\n\n\n

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\nAbout the speaker<\/h3>\n\n\n\n

Pengyi Yang is Associate Professor at the School of Mathematics and Statistics, University of Sydney<\/a>, and Head of the Computational Systems Biology Unit at Children\u2019s Medical Research Institute. His research focuses on developing machine learning and deep learning methods to reconstruct trans-regulatory networks in stem cells using multi-omics data. His group specializes in analyzing mass spectrometry-based phosphoproteomics and single-cell omics, with notable contributions including computational methods for kinase identification, kinase-substrate prediction, and cell type annotation. He has received multiple awards including the ARC Discovery Early Career Researcher Award and the National Stem Cell Foundation\u2019s Metcalf Prize.<\/p>\n\n\n

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