Computer Laboratory

Paul Scherer

Picture of Paul Scherer

I am a PhD student at the University of Cambridge Computer Laboratory under the supervision of Prof. Pietro Lio' and Prof. Mateja Jamnik as part of the Artificial Intelligence Group and the Computational Biology Group. I am generously funded by the W.D Armstrong Fund.

My research interests lie within the fields of information theory, machine learning, and biomedical informatics. My current research looks into developing learning algorithms applicable to irregular structured data such as graphs and domain specific applications in precision oncology. However my general interest in the design of useful inductive biases for representation learning goes beyond graph contexts. In previous years my research has focused on developing clustering algorithms on graphs, heterogenuous data integration, and data harmonization techniques.

Outside of working I enjoy motorcycling, camping, cooking, and reading basic maths.

Other Academic Projects

  • Conducting federated data analysis using DataSHIELD and R for epidemiology research (2016-2017).
  • Basic research into harmonization of heterogenuous epidemiological data (2016-2017)
  • Investigation into role privacy of multi-robot formations using generative adversarial networks (2018).
  • Currently developing an ABM-CA model of land use transformation with the Department of Land Economy at the University of Cambridge (2019).


  • 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 Under Review (Preprint Available)
  • Paul Scherer and Pietro Lio Learning Distributed Representations of Graphs with Geo2DR ICML 2020 Workshop in Graph Representation Learning and Beyond, 2020
  • Paul Scherer, Helena Andres Terre, Pietro Lio, Mateja Jamnik Decoupling feature propagation from the design of graph auto-encoders Arxiv Preprint, 2019
  • 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
  • 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.

Talks and 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 and Supervisions

  • Supervised 1B Artificial Intelligence Easter 2019
  • Demonstrator 1A Scientific Computing Lent 2020
  • Supervisor MPhil RM03 Spatial Analysis and Modelling Lent 2020

For MPhil/Part 3 2020-2021 Students

I will be co-supervising (with Prof Lio) a couple of projects on graph representation learning and biomedical data integration, the suggestions can be seen here (you will need to log in to google with your Raven details), or email directly to have a chat.


Paul Scherer
Department of Computer Science and Technology
University of Cambridge
15 JJ Thomson Avenue
Cambridge CB3 0FD

Last updated 2020/03/14