Biography
My ucampas style website can be found here: https://www.cl.cam.ac.uk/~pms69/ which will be updated more frequently.
I am a postdoctoral research associate at the University of Cambridge Computer Laboratory as part of the Artificial Intelligence Group and the Computational Biology Group. I was also PhD student under the supervision of Prof. Pietro Lio' and Prof. Mateja Jamnik. I was funded by the W.D. Armstrong Fund for Engineering in Medicine.
My research interests lie within the fields of machine learning and biomedical informatics. My current research looks into developing learning algorithms applicable to irregular structured data such as graphs and its applications in biomedicine. However my general interest in the design of useful inductive biases for representation learning goes beyond graph contexts and have dabbled in the design of convolutional and recurrent neural network architectures for biomedical applications as well. In previous years my research has focused on developing clustering algorithms on graphs, heterogeneous data integration, and data harmonization techniques. I am also lightly involved in the study of active learning.
Outside of working I enjoy motorcycling, camping, cooking, playing games, and reading basic maths.
Software
Talks and Lectures
- Distributional and relational inductive biases for graph representation learning. 2023. School of Clinical Medicine, University of Cambridge. AI in Medicine research seminar.
- Representation learning on dynamic graphs. 2022. University of Cambridge, Spatial Analysis and Modelling Special Topics Lectures.
- Principles of model fitting in differentiable and non-differentiable contexts. 2021. University of Cambridge, Spatial Analysis and Modelling Special Topics Lectures.
- Outlining the importance of inductive biases in machine learning and the principles for achieving them . 2020. University of Cambridge, Spatial Analysis and Modelling Special Topics Lectures.
- Learning Distributed Representations of Graphs and Other Discrete Structures. 2019. University of Cambridge AI Research Seminars
- Federated Data Analysis using DataSHIELD and R. 2017. University of Cambridge MRC Epidemiology Unit, Addenbrookes Hospital
- Weighted Clustering Algorithms for Protein-Protein Interaction Networks 2016, University of Edinburgh
Teaching
- Supervisor 1B Artificial Intelligence Easter 2019
- Demonstrator 1A Scientific Computing Lent 2020
- Supervisor MPhil RM03 Spatial Analysis and Modelling Lent 2020
- Supervisor MPhil RM03 Spatial Analysis and Modelling Lent 2021
- Supervisor/Lecturer MPhil RM03 Spatial Analysis and Modelling Lent 2022
- Supervisor/Assistant L45 Graph Representation Learning Lent 2022
Publications
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Ramon Viñas, Paul Scherer, Nikola Simidjievski, Mateja Jamnik, Pietro Liò Spatio-relational inductive biases in spatial cell-type deconvolution International Conference in Machine Learning (ICML2023) CompBio Workshop
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Yana Lishkova, Paul Scherer, Steffen Ridderbusch, Mateja Jamnik, Pietro Liò, Sina Ober-Blobaum, Christian Offen Discrete Lagrangian neural networks with automatic symmetry discovery 22nd World Congress of the International Federation of Automatic Control (IFAC2023)
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Paul Scherer, Pietro Liò, Mateja Jamnik Distributed representations of graphs for drug pair scoring 1st Learning on Graphs Conference (LOG2022) Best reviewer award
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Paul Scherer, Thomas Gaudelet, Alison Pouplin, Jyothish Soman, Lindsay Edwards, Jake Taylor-King, PyRelationAL: a library for active learning research and development ArXiv Preprint 2022
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Benedek Rozemberczki, Paul Scherer,Yixuan He, George Panagopoulos, Alexander Riedel, Maria Astefanoaei, Oliver Kiss, Ferenc Beres, Guzman Lopez, Nicolas Collignon, Rik Sarkar PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models 30th ACM International Conference on Information and Knowledge Management (CIKM2021) Best Paper Award
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Paul Scherer, Maja Trębacz, Nikola Simidjievski, Ramon Viñas, Zohreh Shams, Helena Andres Terre, Mateja Jamnik, Pietro Liò Unsupervised construction of computational graphs for gene expression data with explicit structural inductive biases OUP Bioinformatics (2021)
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Benedek Rozemberczki, Paul Scherer, Oliver Kiss, Rik Sarkar, Tamas Ferenci Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural Networks (WWW’21: Graph Learning Benchmarks Workshop)
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Paul Scherer, Maja Trȩbacz, Nikola Simidjievski, Zohreh Shams, Helena Andres Terre, Pietro Liò, Mateja Jamnik Incorporating network based protein complex discovery into automated model construction 15th Machine Learning in Computational Biology (MLCB20)
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Maja Trȩbacz, Zohreh Shams, Mateja Jamnik, Paul Scherer, Nikola Simidjievski, Helena Andres Terre, Pietro Liò Using ontology embeddings for structural inductive bias in gene expression data analysis 15th Machine Learning in Computational Biology (MLCB20)
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Paul Scherer and Pietro Lio Learning Distributed Representations of Graphs with Geo2DR ICML 2020 Workshop in Graph Representation Learning and Beyond, 2020
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Paul Scherer, Helena Andres Terre, Pietro Lio, Mateja Jamnik Decoupling feature propagation from the design of graph auto-encoders Arxiv Preprint, 2019
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Nikola Simidjievski, Cristian Bodnar, Ifrah Tariq, Paul Scherer, Helena Andres-Terre, Zohreh Shams, Mateja Jamnik, Pietro Liò Variational Autoencoders for Cancer Data Integration: Design Principles and Computational Practice Front. Genet. , 2019
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S Pastorino, T Bishop, SR Crozier, C Granström, K Kordas, LK Küpers, EC O'Brien, K Polanska, KA Sauder, MH Zafarmand, RC Wilson, C Agyemang, PR Burton, C Cooper, E Corpeleijn, D Dabelea, W Hanke, HM Inskip, FM McAuliffe, SF Olsen, TG Vrijkotte, S Brage, A Kennedy, D O'Gorman, P Scherer, K Wijndaele, NJ Wareham, G Desoye, KK Ong Associations between maternal physical activity in early and late pregnancy and offspring birth size: remote federated individual level meta-analysis from eight cohort studies. BJOG, Volume 126, Issue 4, 2018.
