Nora Gourmelon, M. Sc.
Researcher
Martensstr. 3
91058 Erlangen
Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)
Martensstr. 3
91058 Erlangen
- Phone number: +49 9131 85 28977
- Fax number: +49 9131 85 27270
- Email: nora.gourmelon@fau.de
- Website: https://lme.tf.fau.de/person/gourmelon/
- Since 11/2020:
Ph.D. Student at Pattern Recognition Lab, FAU Erlangen-Nürnberg - 10/2018 – 11/2020:
M. Sc. Computer Science, FAU Erlangen-Nürnberg - 08/2019 – 12/2019:
Semester abroad at Norges teknisk-naturvitenskapelige universitet in Trondheim, Norway - 10/2014 – 09/2019:
B. Sc. Computer Science, FAU Erlangen-Nürnberg - 09/2016 – 12/2016:
Semester abroad at Saint Mary`s University in Halifax, Canada
2019
-
Tapping the potential of Earth Observations
(FAU Funds)
Term: April 1, 2019 - March 31, 2022Ziel des Projekts ist es, die Zeitreihen von
Erdbeobachtungs(EO)-Daten mit innovativen Methoden des „Deep Learnings“
zu analysieren, um effiziente Algorithmen zur Bewältigung der großen
Datenmengen zu entwickeln. Der Wert dieser EO-Produkte wird durch
fortgeschrittene Interpolationstechniken und Assimilation in
geophysikalische Modelle, die es in der angewandten Mathematik gibt,
weiter erhöht.
2021
Journal Articles
- , , , , , , , :
Implications of Experiment Set-Ups for Residential Water End-Use Classification
In: Water 13 (2021), Article No.: 236
ISSN: 2073-4441
DOI: 10.3390/w13020236
URL: https://www.mdpi.com/2073-4441/13/2/236
BibTeX: Download
Conference Contributions
- , , , , , :
Tapping the Potential of Earth Observation - Calving Front Detection in SAR Images using Deep Learning Techniques
EGU General Assembly 2021 (, April 19, 2021 - May 30, 2021)
DOI: 10.5194/egusphere-egu21-11280
BibTeX: Download
2020
Conference Contributions
- , , , , , , , :
Implications of Experiment Set-Ups for Residential Water End-Use Classification
5th International Electronic Conference on Water Sciences (, November 16, 2020 - November 30, 2020)
DOI: 10.3390/ECWS-5-08225
URL: https://sciforum.net/paper/view/conference/8225
BibTeX: Download
- 2021:
“Gletscherfronten und wo sie zu finden sind”, Fachsymposium “Artificial Intelligence for Life”, Hochschule für angewandte Wissenschaften Weihenstephan-Triesdorf, 22. Oktober
- 2021:
“Calving Front Detection in SAR Images using Deep Learning Techniques”, #GeoWoche2021, Arbeitskreises Fernerkundung, 08. Oktober, link to abstract, link to poster
- 2021:
Prize for excellent Master’s thesis “End-use Classification Using High-Resolution Smart Water Meter Data” - 2020:
Best Paper Award at the 5th International Electronic Conference on Water Sciences in the Session “Water Resources Management and the Ecosphere Resilience and Adaptation”
Übung (UE)
-
Introduction to Machine Learning Tutorial
- Thu 8:30-10:00
-
Introduction to Machine Learning Exercises
- Wed 14:00-16:00
- Tue 10:00-12:00
- Thu 10:00-12:00
- Mon 14:00-16:00
-
Introduction to Machine Learning Exercises
- Thu 10:00-12:00
- Mon 14:00-16:00
- Tue 10:00-12:00
- Wed 14:00-16:00
Vorlesung (VORL)
-
Introduction to Machine Learning
Information regarding the online teaching will be added to the studon course
- Wed 8:30-10:00, Room H7
| Type | Title | Status |
|---|---|---|
| MA thesis | Multi-Task Learning for Glacier Segmentation and Calving Front Detection with the nnU-Net Framework | running |
| Project | Temporal Information in Glacier Front Segmentation Using a 3D Conditional Random Field | running |
| Project | Evaluation of Different Loss Functions for Highly Unbalanced Segmentation | running |
| MA thesis | Incorporating Time Series Information into Glacier Segmentation and Front Detection using U-Nets in Combination with LSTMs and Multi-Task Learning | finished |
| MA thesis | Image Segmentation via Transformers | finished |