Monica Danilevicz

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I am a postdoc at the University of Western Australia, after finishing my PhD in the group supported by the Forrest Research Foundation scholarship. My research in the group has focused on combining multidimensional datasets (describing the plant traits and environment) and training deep learning models for early yield prediction of crops, preliminary selection of crop varieties under field trial, and distinguishing visually similar plant species for weed detection. Advances in camera technology and artificial intelligence are transforming how we can observe plant development, enabling the development of more productive crop varieties, so my focus has been on their use for Australian agriculture.

Before joining the group, I completed my Bachelor's and Master's degrees in Brazil at the Universidade Federal do Rio de Janeiro, looking into the interaction of crops (mainly maize and sugarcane) with beneficial bacteria when subjected to water deficit stress. There, I primarily worked with bioinformatics, doing some RNA-seq analysis and looking into the expression of microRNAs and long non-coding RNAs.

Areas of Interest

Plant Biology, Plant Genetics, Machine learning, Deep neural networks, Bioinformatics, Data Science, Plant Phenotyping.

Academic History

2019 to 2023 Ph.D. in Biological Sciences at the University of Western Australia (Australia), supervised by Prof. David Edwards, Prof. Mohammed Bennamoun, Dr. Philipp Bayer and Prof. Jacqueline Batley.

2017 - 2019 MA in Plant Biotechnology with Honors at Universidade Federal do Rio de Janeiro (Brazil), supervised by Prof. Paulo C.G. Ferreira and Dr. Flavia Thiebaut. Honors project title “Uncovering long non-coding RNAs in copaiba and maize”.

2011 - 2016 Bsc. in Biotechnology at Universidade Federal do Rio de Janeiro (Brazil), supervised by Prof. Paulo C.G. Ferreira, with an exchange period at Australia National University (Australia). Dissertation title “Expression analysis of microRNAs from maize associated to beneficial bacteria and subjected to hydric deficit stress”.


Current record of publication is available at [1]