Computational Biology · Quantitative Genetics · Plant Breeding

Jordan Ubbens

I study machine learning methods for improving crop plants, from genomic prediction and predictive breeding to image-based plant phenotyping, as a Research Officer at the National Research Council.

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2023 — Present National Research Council Canada

Research Officer, Computational Biology

As a member of the Aquatic and Crop Resource Development (ACRD) research centre, my research interests involve a broad range of machine learning methods for improving crop plants. This includes applications in bioinformatics, as well as genomic prediction, efficient cross selection, and other predictive breeding technologies. At NRC, I am involved with both basic research as well as contract work for small and medium Canadian enterprises.

  • 2026Nickerson, C. N., Hogarth, S., Stone, A. K., et al. Implementation of Machine Learning Models to Predict Functionality of Pea Flour From Its Composition. Cereal Chemistry 103: 522–536. doi:10.1002/cche.70072
  • 2025Ubbens, J. R. Neural Scaling Laws for AI in Plant Breeding. Invited keynote, Brassica 2025, Giessen, Germany, September.
  • 2025Ubbens, J. R., Stavness, I., Pound, M. P., Guo, W. Deep Learning in Plant Phenotyping: The First Ten Years. Plant Phenomics, 100062. doi:10.1016/j.plaphe.2025.100062
  • 2024Ubbens, J. R. Genomic prior-data fitted networks: a new approach for genomic prediction. Invited talk, National Association of Plant Breeders, January.
  • 2023Ubbens, J. R. AI in plant breeding and genetics: the good, the bad, and the ugly. Invited talk, Canola Innovation Day, Calgary AB, December.
2020 — 2023 Global Institute for Food Security

Research Associate, Plant Improvement

Previously, I was a Research Associate at the Global Institute for Food Security in the Plant Improvement group. My research at GIFS focused primarily on novel machine learning methods in quantitative genetics.

  • 2025Ubbens, J. R., Stavness, I., Sharpe, A. G. GPFN: Prior-Data Fitted Networks for Genomic Prediction. IEEE Transactions on Computational Biology and Bioinformatics. doi:10.1109/TCBBIO.2025.3596744
  • 2023Ubbens, J. R., Stavness, I., Sharpe, A. G. The Intrinsic Dimensionality of Genotype Data and its Implications. PAG 30 (poster), San Diego CA, February.
  • 2022Ubbens, J. R., Feldmann, M. J., Stavness, I., Sharpe, A. G. Quantitative evaluation of nonlinear methods for population structure visualization and inference. G3 Genes|Genomes|Genetics 12(9), jkac191. doi:10.1093/g3journal/jkac191
  • 2021Ubbens, J. R., Parkin, I., Eynck, C., Stavness, I., Sharpe, A. G. Deep Neural Networks for Genomic Prediction Do Not Estimate Marker Effects. Plant Genome, e20147. doi:10.1002/tpg2.20147
  • 2021Feldmann, M. J., Gage, J. L., Turner-Hissong, S. D., Ubbens, J. R. Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants. Plant Phenome J 4:e20023. doi:10.1002/ppj2.20023
2016 — 2020 University of Saskatchewan

Ph.D. in Computer Science

I did my Ph.D. in computer science at the Plant Phenotyping and Imaging Research Centre at the University of Saskatchewan with Dr. Ian Stavness. I was interested in the application of computer vision and deep learning to the problem of image-based plant phenotyping, and my dissertation was about performing plant stress phenotyping in the latent space. During the summer of 2017, I was a visiting Ph.D. student at the Biological Modelling and Visualization lab at the University of Calgary.

  • 2020Ubbens, J. R., Ayalew, T. W., Shirtliffe, S. J., Josuttes, A., Pozniak, C. J., Stavness, I. AutoCount: Unsupervised Segmentation and Counting of Plant Organs in Field Images. ECCV Workshops, August.
  • 2020Ayalew, T. W., Ubbens, J. R., Stavness, I. Unsupervised Domain Adaptation for Counting Plant Organs. ECCV Workshops, August.
  • 2020Ubbens, J. R. Counting Sorghum Heads with Density Estimation. Invited workshop, Phenome 2020, Tucson AZ, February.
  • 2020Ubbens, J. R., Cieslak, M., Prusinkiewicz, P., Parkin, I., Ebersbach, J., Stavness, I. Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies. Plant Phenomics. doi:10.34133/2020/5801869
  • 2019Higgs, N., Leyeza, B., Ubbens, J. R., Kocur, J., van der Kamp, W., Cory, T., Eynch, C., Vail, S., Eramian, M., Stavness, I. ProTractor: a lightweight ground imaging and analysis system for early-season field phenotyping. CVPR Workshops, Long Beach CA, June.
  • 2019Ubbens, J. R., Stavness, I. Latent space association analysis: towards GWAS directly from images. Phenome 2019, Tucson AZ, February.
  • 2018Ubbens, J. R., Stavness, I. An introduction to deep learning in plant phenotyping without the agonizing pain. Phenome 2018, Tucson AZ, February.
  • 2018Ubbens, J. R., Cieslak, M., Prusinkiewicz, P., Stavness, I. The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods 14(1), 6. doi:10.1186/s13007-018-0273-z
  • 2017Ubbens, J. R., Stavness, I. Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks. Front. Plant Sci. 8:1190. doi:10.3389/fpls.2017.01190