Jordan Ubbens

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  • 2023-Present

    Research Officer, National Research Council

    I am currently a Research Officer (Computational Biology) at the National Research Council of Canada. As a member of the Aquatic and Crop Resource Development (ACRD) Research Centre, my research interests involve advanced machine learning methods for improving crop plants. This includes genomic prediction, as well as efficient cross selection and other predictive breeding technologies.

    Publications and Presentations ▾

    Ubbens, J. R. (2024). Genomic prior-data fitted networks: a new approach for genomic prediction. Invited talk presented at the National Association of Plant Breeders. January 2024.

    Ubbens, J. R. (2023). AI in plant breeding and genetics: the good, the bad, and the ugly. Invited talk presented at Canola Innovation Day, Calgary, AB. December 2023.

  • 2020-2023

    Research Associate, Global Institute for Food Security

    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.

    Publications and Presentations ▾

    Ubbens, J. R., Stavness, I. & Sharpe, A. G. (2023). GPFN: Prior-Data Fitted Networks for Genomic Prediction. bioRxiv 2023.09.20.558648; doi:10.1101/2023.09.20.558648

    Ubbens, J. R., Stavness, I. & Sharpe, A. G. (2023). The Instrinsic Dimensionality of Genotype Data and its Implications. PAG 30 (poster). San Diego, CA. February 2023.

    Ubbens, J. R., Feldmann, M. J., Stavness, I. & Sharpe, A. G. (2022). Quantitative evaluation of nonlinear methods for population structure visualization and inference. G3 Genes|Genomes|Genetics, Volume 12, Issue 9, September 2022, jkac191, doi:10.1093/g3journal/jkac191

    Ubbens, J. R., Parkin, I., Eynck, C., Stavness, I. & Sharpe, A. G. (2021). Deep Neural Networks for Genomic Prediction Do Not Estimate Maker Effects. Plant Genome, 2021;e20147. doi:10.1002/tpg2.20147

    Feldmann, M. J., Gage, J. L., Turner-Hissong, S. D. & Ubbens, J. R. (2021). Images carried before the fire: The power, promise, and responsibility of latent phenotyping in plants. Plant Phenome J, 2021; 4:e20023. doi:10.1002/ppj2.20023

  • 2016-2020

    Ph.D., University of Saskatchewan

    I did my Ph.D. in computer science at the Plant Phenotyping and Imaging Research Center 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 Modeling and Visualization lab at the University of Calgary.

    Publications and Presentations ▾

    Ubbens, J. R., Ayalew, T. W., Shirtliffe, S. J., Josuttes, A., Pozniak, C. J. & Stavness, I. (2020). AutoCount: Unsupervised Segmentation and Counting of Plant Organs in Field Images. European Conference on Computer Vision (ECCV) Workshops, 2020. August 2020.

    Ayalew, T. W., Ubbens, J. R. & Stavness, I. (2020). Unsupervised Domain Adaptation for Counting Plant Organs. European Conference on Computer Vision (ECCV) Workshops, 2020. August 2020.

    Ubbens, J. R. (2020). Counting Sorghum Heads with Density Estimation. Invited workshop presented at Phenome 2020, Tucson, AZ. February 2020.

    Ubbens, J. R., Cieslak, M., Prusinkiewicz, P., Parkin, I., Ebersbach, J., & Stavness, I. (2020). Latent Space Phenotyping: Automatic Image-Based Phenotyping for Treatment Studies. Plant Phenomics, vol. 2020, 13 pages. doi:10.34133/2020/5801869

    Higgs, N., Leyeza, B., Ubbens, J. R., Kocur, J., van der Kamp, W., Cory, T., Eynch, C. Vail, S., Eramian, M. & Stavness, I. (2019). ProTractor: a lightweight ground imaging and analysis system for early-season field phenotyping. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019. Long Beach, CA. June 2019.

    Ubbens, J. R. & Stavness, I. Latent space association analysis: towards GWAS directly from images. Presented at Phenome 2019, Tucson, AZ. February 2019.

    Ubbens, J. R. & Stavness, I. An introduction to deep learning in plant phenotyping without the agonizing pain. Presented at Phenome 2018, Tucson, AZ. February 2018.

    Ubbens, J. R., Cieslak, M., Prusinkiewicz, P. & Stavness, I. (2018). 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

    Ubbens, J. R. & Stavness, I. (2017). Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks. Front. Plant Sci. 8:1190. doi:10.3389/fpls.2017.01190

  • 2015-2016

    Project Manager, Industry

    I worked as a project manager at Shiverware, where I led the Swimlytics project for a year following my M.Sc. My role involved machine learning and data analytics, as well as development of the client and server side applications.

    Publications and Presentations ▾

    Barden, J. M., Gerhard, D. B., Vila, O., Ubbens, J. R. & Park, B. The effect of breathing asymmetry on stroke periodicity in competitive front-crawl swimming. Presented at the Sport Innovation (SPIN) Summit, Richmond, BC. October 2017.

  • 2014-2015

    M.Sc., University of Regina

    As a masters student at the University of Regina, my work primarily involved machine learning with audio data. My thesis was about the application of classical sparse coding to audio information using local image features of the spectrogram.

    Publications and Presentations ▾

    Ubbens, J. R. & Gerhard, D. B. (2016). Information rate for fast time-domain instrument classification. In R. Kronland-Martinet, M. Aramaki & S. Ystad (Eds.), Music, Mind & Embodiment: Volume 9617 of the series Lecture Notes in Computer Science. Springer International Press. 297-308. doi:10.1007/978-3-319-46282-0_19

  • 2009-2013

    B.Sc. (Hons), University of Regina

    As an undergraduate student, I was involved in projects related to scaling treatment protocols in clinical psychology using mobile technology such as phones and tablets.

    Publications and Presentations ▾

    Carleton, R. N., Teale Sapach, M. J. N., Oriet, C., Duranceau, S., Lix, L. M., Thibodeau, M. A., Horswill, S. C., Ubbens, J. R., & Asmundson, G. J. G. (2015). A randomized controlled trial of attention modification for social anxiety disorder. Journal of Anxiety Disorders, 33, 35-44. doi:10.1016/j.janxdis.2015.03.011

    Carleton, R. N., Teale, M. J. N., Horswill, S. C., Oriet, C., Ubbens, J. R., & Asmundson, G. J. G. Attending to the details: A longitudinal RCT of an attention modification program for Social Anxiety Disorder. Presented at the 33rd annual conference of the Anxiety Disorders Association of America, La Jolla, California. April 2013.