Information, Inference and Machine Learning Group at University College London

Maria Carolina Novitasari

Third-year PhD Student

Contact

Where to Find Me

Malet Place Engineering Building
Torrington Place
London WC1E 7JE
United Kingdom

Research Interests

Machine Learning and its applications, Deep Learning, Computer Vision, Active Learning

About

Maria is a Marie Skłodowska-Curie PhD fellow (imiracli) at UCL working on algorithms for multimodal data-based inference and prediction for climate science applications under the supervision of Professor Miguel Rodrigues. She obtained her master degree in computer science from National Taiwan University of Science and Technology where her research revolved around the combination of active learning and periodicity analysis.

Achievements (Awards)

Publications

@inproceedings{novitasari2021leveraging,
					title={Leveraging Machine Learning to Predict the Autoconversion Rates from Satellite Data},
					author={Novitasari, Maria C and Quaas, Johannes and Rodrigues, Miguel},
					booktitle={NeurIPS 2021 Workshop on Tackling Climate Change with Machine Learning},
					url={https://www.climatechange.ai/papers/neurips2021/59},
					year={2021}
					}
					@misc{watson2022climatebench,
						author = {Watson-Parris, D. and Rao, Y. and Olivié, D. and Seland, Ø. and Nowack, P. and Camps-Valls, G. and Stier, P. and Bouabid, S. and Dewey, M. and Fons, E. and Gonzalez, J. and Harder, P. and Jeggle, K. and Lenhardt, J. and Manshausen, P. and Novitasari, M. and Ricard, L. and Roesch, C.},
						title = {ClimateBench: A benchmark dataset for data-driven climate projections},
						journal = {Journal of Advances in Modeling Earth Systems},
						year = {2022},
						url = {https://doi.org/10.1029/2021MS002954},
					}
					@inproceedings{novitasari2023estimating,
					  title={Estimating the Autoconversion Rates from Satellite Data: Machine Learning-Based Approaches},
					  author={Novitasari, Maria Carolina and Quaas, Johannes and Rodrigues, Miguel RD},
					  booktitle={103rd AMS Annual Meeting},
					  year={2023},
					  url={https://ams.confex.com/ams/103ANNUAL/meetingapp.cgi/Paper/419087},
					  organization={AMS}
					}
					@inproceedings{novitasarimachine,
					  title={Machine Learning-Based Approaches to Predict the Autoconversion Rates from Satellite Data},
					  author={Novitasari, Maria C and Quaas, Johannes and Rodrigues, Miguel RD},
					  booktitle={ECMWF–ESA Workshop on Machine Learning for Earth Observation and Prediction},
					  year={2022},
					  url = {https://events.ecmwf.int/event/304/},
					  organization={ECMWF-ESA}
					}

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