Moiz Khan Sherwani
Karolinska Institute, Senior AI/Data Research Scientist
Stockholm, Sweden
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Moiz Khan Sherwani is a Senior Postdoctoral Researcher at the Karolinska Institute, Stockholm, and External Data Scientist at the University of Copenhagen, with over 10 years of experience in AI-driven biomedical research.
His work sits at the frontier of graph neural networks, multi-omics data integration, federated learning, and synthetic data generation — with applications spanning precision medicine, cardiovascular disease prediction, microbiome genomics, and medical imaging.
Moiz holds a PhD in Computer Science (AI in Medicine) from the University of Calabria, Italy, and has published across Frontiers in Radiology, Briefings in Bioinformatics, Medical & Biological Engineering & Computing, and Springer. He is currently a contributor to the EU Horizon Europe–funded NextGen Tools project, building privacy-preserving multimodal AI frameworks across European and US biobanks.
A Hamlyn Winter School Champion, Hackathon winner, and invited speaker at DataMakersFest — Europe's premier data festival — Moiz is equally committed to advancing the science and building the community around it. He teaches at the University of Copenhagen, co-supervises PhD students, and reviews for IEEE, AAAI, and leading biomedical journals.
Area of Expertise
Topics
Unraveling Graphs to Decode Biomedical Complexity
Modern biomedical data is inherently interconnected, genes interact with proteins, diseases share pathways and patients relate through phenotypic and molecular similarity. Traditional deep learning models often fail to capture this complex relational structure. This session explores how Graph Neural Networks (GNNs) are transforming biomedical discovery by enabling us to model these intricate connections directly.
The talk will walk through real-world examples of how graph learning can be applied to biological networks and patient stratification. It will also compare GNN-based approaches to conventional deep learning, highlighting where graph-based reasoning provides unique advantages in interpretability and data efficiency. Attendees will gain practical insights into structuring biomedical data as graphs, selecting the right GNN architectures, and evaluating model trustworthiness. By the end, participants will understand how GNNs can help uncover hidden biological relationships that traditional methods often overlook.
Machine Learning and Multi-Omics
Our application for Data Maker Fest explores the cutting-edge intersection of machine learning and multi-omics. Using advanced algorithms, we are deciphering complex biological data across multiple omics layers. Through multi-omics, our approach reveals hidden patterns, enabling breakthrough insights into human health and disease mechanisms. Our mission is to revolutionize the field of biotechnology through data-driven discovery. I will also present the challenges of data collection and processing before using it in ML algorithms.
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