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Avneet Kaur
avneetreen[dot]github[dot]io
Hi, I'm Avneet. Based in Copenhagen, I work as a data engineer at Novo Nordisk. I'm passionate about developing software and data products, machine learning for social action and interested in building & working with technology that adds value to society. I hold an M.Sc. in Computer Science from the University of Copenhagen. Some of my projects are highlighted below! If you're interested in collaborating, please drop me a line! Email • CV • Linkedin • Google Scholar • Github |
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Research Work
Multidimensional Analysis of Trust in News Articles
Kaur, A., Leekha M., Chawla U., Agarwal A., Saxena M., Madaan Ni., Kannan K., Mehta S.(2019). Proceedings of the AAAI Conference on Artificial Intelligence (AAAI 2020)
That's interesting, tell me more! finding descriptive support passages for knowledge graph relationships
Bhatia S., Dwivedi P., Kaur, A. Proceedings of the AAAI Conference on Artificial Intelligence (ISWC 2018)
Garg N., Sethupathy A., Tuwani R., Nk R., Dokania S., Iyer A., Gupta A., Agrawal S., Singh N., Shukla S., Kathuria K., Badhwar R., Kanji R., Jain A., Kaur, A., Nagpal R., Bagler G. Nucleic acids research (NAR 2018)
Research Projects
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Development of a robust and reproducible preprocessing pipeline for Positron Emission Tomography (PET) data
M.Sc. Thesis, University of Copenhagen, Department of Computer Science, January 2022 Avneet Kaur, under the supervision of, Melanie Ganz-Benjaminsen, Martin Nørgaard, Vincent Beliveau We developed an open, robust and automated preprocessing pipeline for PET data using existing state-of-the-art neuroimaging software and implemented using Nipype. To test the pipeline for robustness and computational reproducibility, the pipeline was tested on three datasets from different scanners and tracers across different computational environments. Code / PDF / Slides |
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Field Boundary Delineation applied to Danish agriculture
project in practice, University of Copenhagen, Department of Computer Science, January 2021 Avneet Kaur, under the supervision of, Stefan Oehmcke, Kenneth Grogan Danish parcel delineation is an important task that currently has to be done manually. In this work, we automate this laborious and error-prone process. Based on real data from Danish agriculture, we applied deep learning methods to efficiently detect parcels from freely available satellite images. To that end, we created a complete data pipeline, from data collection over to processing, then building and evaluating the model. Code / PDF / Slides |