Givanna Putri is a post-doctoral researcher at Walter and Eliza Hall Institute of Medical Research (WEHI). She is a computer scientist, and has extensive experience in developing and applying machine learning (ML) algorithms to analyse multi-omics data. Her work focuses heavily on developing and applying ML algorithms to uncover biological insights from multi-omics datasets, including cytometry, scRNAseq, and spatial transcriptomics.
Givanna was awarded her PhD in Computer Science in 2021 from the University of Sydney. During her PhD, she developed algorithms to cluster and track cellular populations development in time-course high dimensional cytometry data. This included the TrackSOM algorithm which was used to profile SARS-CoV-2/COVID-19 patients, and the ChronoClust algorithm which publication on the Knowledge Based System journal won the Dolby scientific paper competition in 2019. She also developed a novel benchmarking methodology ParetoBench which uses the Pareto Front framework to fairly compare the performances of clustering algorithms using several performance metrics. Her PhD research was funded by The Australian Government Research Training Program Scholarship and The University of Sydney Alumni Scholarship.
In her current role as a postdoc at WEHI, she developed an R package SuperCellCyto to reduce the size of cytometry data while minimising the loss in biological heterogeneity. She was awarded a travel award by the CASS foundation to present SuperCellCyto at the recent CYTO 2024 conference.
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