Dr Jennifer Hoyal Cuthill
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Email
j.hoyal-cuthill@essex.ac.uk -
Telephone
+44 (0) 1206 873307
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Location
3SW.5.39, Colchester Campus
Profile
Qualifications
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PhD University of Cambridge, (2011)
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MSc Palaeobiology University of Bristol, (2007)
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BSc Zoology University of Bristol, (2005)
Appointments
University of Essex
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Postdoctoral Research Fellow, Institute for Analytics and Data Science and School of Life Sciences (1/10/2019 - 30/9/2022)
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Lecturer, School of Life Sciences, University of Essex (1/10/2022 - present)
Other academic
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Affiliate Researcher, Tokyo Institute of Technology, Earth Life Science Institute (1/3/2018 - present)
Research and professional activities
Research interests
Biological machine learning
Developing and applying machine learning for the life sciences
Computational palaeobiology
Using computational methods to understand life's evolutionary history
Quantifying evolutionary convergence
Using computational methods to understand why evolution repeats itself
Ediacaran palaeobiology and palaeoecology
Understanding the early animals of the Ediacaran geological period (from 635 to 541 million years ago).
Current research
Machine learning on butterfly phenotypes
Developing and applying spatial embedding methods for taxonomic classification, phenomic phylogenetics and analysis of mimicry evolution
Machine learning on the fossil record
Using new methods of machine learning to understand extinction and diversification in the history of life
Homoplasy: measuring evolutionary convergence on phylogenetic trees
Investigating the limits on basic measures of homoplasy
Computational Ediacaran Palaeobiology
Using computational methods to learn about Earth's early macro-organisms
Conferences and presentations
Session Chair: Animal, Cell, and Non-Human Communication
Invited presentation, After Babel: The Quest for Universal Communication, Japan, 23/4/2024
Quantifying phenotypic evolution: connections to information, complexity and predictability
Invited presentation, The Evolution of Complexity, Bath, United Kingdom, 29/7/2022
Cross University Research Event: Applications of AI
Invited presentation, University of Essex CURE, Colchester, United Kingdom, 20/4/2022
Testing the balance of mass radiation and extinction
Invited presentation, Department of Earth Sciences Seminar Series, Uppsala University, Sweden, 16/3/2022
Diversification through the looking glass: the continuum of mass radiation and extinction
65th Palaeontological association annual Meeting, Manchester, United Kingdom, 19/12/2021
Does the Ediacaran biota match Darwin’s predictions? Phylogenetic, taxonomic and evolutionary implications of the Precambrian fossil record
The Ediacaran Taxonomy Meeting, Oxford, United Kingdom, 4/10/2021
Machine learnt spatial embeddings for new biological insights
Invited presentation, The use of Machine Learning models in Behaviour, Ecology & Evolution, Neuchatel, Switzerland, 10/3/2021
The evolutionary decay-clock: persistence versus decay of evolutionary biotas through the Phanerozoic
The Palaeontological Association Virtual Annual Meeting, Oxford, United Kingdom, 16/12/2020
(Marx, Musk and) the Ediacaran Biota
Invited presentation, Open University Geological Society Annual General Meeting, Open University Geological Society Annual General Meeting, Milton Keynes, United Kingdom, 8/2/2020
Putting the AI into Palaeontology: using new methods of machine learning to capture evolutionary history
Palaeontological Association Annual Meeting, The Palaeontological Association 63rd Annual Meeting, Valencia, Spain, 20/12/2019
Faculty-track research fellowships: an interdisciplinary example
Invited presentation, Getting a lectureship for physical science postdocs, Getting a lectureship for physical science postdocs, 9/12/2019
Ediacaran origins of complex animal behaviour: trace fossil evidence from the Hoogland Member of Namibia
International Meeting on the Ediacaran System and the Ediacaran-Cambrian Transition, Guadalupe, Spain, 20/10/2019
Teaching and supervision
Current teaching responsibilities
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Genetics and Evolution (BS102)
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Animal Evolution, Ecology and Behaviour (BS113)
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Transferable Skills in Life Sciences (BS143)
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Employability Skills for the Biosciences (BS211)
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The Theory of Evolution (BS347)
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Methods in Marine Biology (BS707)
Publications
Journal articles (19)
Hoyal Cuthill, J., (2022). Ediacaran survivors in the Cambrian: suspicions, denials and a smoking gun. Geological Magazine. 159 (7), 1210-1219
Hoyal Cuthill, JF. and Hunter, AW., (2020). Fullerene‐like structures of Cretaceous crinoids reveal topologically limited skeletal possibilities. Palaeontology. 63 (3), 513-524
Conway Morris, S., Smith, RDA., Hoyal Cuthill, J., Bonino, E. and Lerosey-Aubril, R., (2020). A possible Cambrian stem-group gnathiferan-chaetognath from the Weeks Formation (Miaolingian) of Utah. Journal of Paleontology. 94 (4), 624-636
Hoyal Cuthill, J., Guttenberg, N. and Budd, GE., (2020). Impacts of speciation and extinction measured by an evolutionary decay clock. Nature. 588 (7839), 636-641
Han, J., Conway Morris, S., Hoyal Cuthill, JF. and Shu, D., (2019). Sclerite-bearing annelids from the lower Cambrian of South China. Scientific Reports. 9 (1), 4955-
Hoyal Cuthill, JF., Guttenberg, N., Ledger, S., Crowther, R. and Huertas, B., (2019). Deep learning on butterfly phenotypes tests evolution’s oldest mathematical model. Science Advances. 5 (8), eaaw4967-
Wood, R., Liu, AG., Bowyer, F., Wilby, PR., Dunn, FS., Kenchington, CG., Cuthill, JFH., Mitchell, EG. and Penny, A., (2019). Integrated records of environmental change and evolution challenge the Cambrian Explosion. Nature Ecology and Evolution. 3 (4), 528-538
Hoyal Cuthill, JF. and Han, J., (2018). Cambrian petalonamid Stromatoveris phylogenetically links Ediacaran biota to later animals. Palaeontology. 61 (6), 813-823
Shu, D., Conway Morris, S., Han, J., Hoyal Cuthill, JF., Zhang, Z., Cheng, M. and Huang, H., (2017). Multi-jawed chaetognaths from the Chengjiang Lagerstätte (Cambrian, Series 2, Stage 3) of Yunnan, China. Palaeontology. 60 (6), 763-772
Hoyal Cuthill, JF. and Conway Morris, S., (2017). Nutrient-dependent growth underpinned the Ediacaran transition to large body size. Nature Ecology and Evolution. 1 (8), 1201-1204
Hoyal Cuthill, JF., Sewell, KB., Cannon, LRG., Charleston, MA., Lawler, S., Littlewood, DTJ., Olson, PD. and Blair, D., (2016). Australian spiny mountain crayfish and their temnocephalan ectosymbionts: an ancient association on the edge of coextinction?. Proceedings of the Royal Society B: Biological Sciences. 283 (1831), 20160585-20160585
Hoyal Cuthill, JF., (2015). The morphological state space revisited: what do phylogenetic patterns in homoplasy tell us about the number of possible character states?. Interface Focus. 5 (6), 20150049-20150049
Hoyal Cuthill, JF. and Charleston, M., (2015). Wing patterning genes and coevolution of Müllerian mimicry inHeliconiusbutterflies: Support from phylogeography, cophylogeny, and divergence times. Evolution. 69 (12), 3082-3096
Hoyal Cuthill, J., (2015). The size of the character state space affects the occurrence and detection of homoplasy: Modelling the probability of incompatibility for unordered phylogenetic characters. Journal of Theoretical Biology. 366, 24-32
Conway Morris, S., Hoyal Cuthill, JF. and Gerber, S., (2015). Hunting Darwin's Snark: which maps shall we use?. Interface Focus. 5 (6), 20150078-20150078
Hoyal Cuthill, JF. and Conway Morris, S., (2014). Fractal branching organizations of Ediacaran rangeomorph fronds reveal a lost Proterozoic body plan. Proceedings of the National Academy of Sciences. 111 (36), 13122-13126
Cuthill, JH. and Charleston, MA., (2013). A SIMPLE MODEL EXPLAINS THE DYNAMICS OF PREFERENTIAL HOST SWITCHING AMONG MAMMAL RNA VIRUSES. Evolution. 67 (4), 980-990
Hoyal Cuthill, J. and Charleston, M., (2012). Phylogenetic Codivergence Supports Coevolution of Mimetic Heliconius Butterflies. PLoS ONE. 7 (5), e36464-e36464
Cuthill, JFH., Braddy, SJ. and Donoghue, PCJ., (2010). A formula for maximum possible steps in multistate characters: isolating matrix parameter effects on measures of evolutionary convergence. Cladistics. 26 (1), 98-102
Grants and funding
2024
Is evolution predictable? Unlocking fundamental biological insights using new machine learning methods
Medical Research Council
