Current Research
I am based in the Data-Intensive Astronomical Analysis research group where I am working on:
- Improving astronomical classification using Generative Models.
- Parameter estimation of simulations using SBI.
- Calibration of simulations within Digital Twins.
- Creating interactive software for researchers to make use of cutting-edge machine learning techniques.
PhD Research
My PhD: Improving The Practicality of Active Learning Pipelines in Real-World Problem Settings: A Case Study in The Classification of Astronomical Data explored the following topics:
- Provides a How-to guide for applying Active Learning to real-world data for experts from any scientific domain.
- Creating novel query strategies to improve accuracy and reduce labelling costs for active learning.
- Combining the use of weak supervision methods with active learning to improve performance on datasets where labels are scarce, noisy, or difficult to obtain.
- Using active learning for galaxy morphology classification with noisy image data and unreliable labels.
- Source classification (star, galaxy, AGN, QSO separation) using Active Learning and Outlier Detection methods.
- Creating interactive software for researchers to make use of cutting-edge machine learning techniques.
Supervisory Team
Sotiria Fotopoulou , Oliver Ray and Malcolm BremerRecent Publications
ECML / Data Mining and Knowledge Discovery
PDF DOI BIB ABSTRACTAstronomy & Astrophysics
PDF DOI BIB ABSTRACTICLR 2025 - First Workshop on Scalable Optimization for Efficient and Adaptive Foundation Models
PDF ARXIV BIB ABSTRACTArxiv Preprint
PDF DOI BIB ABSTRACT