Profile
I am Evangelia (Eva) Deliporanidou, a PhD candidate in Astrophysics at the University of Cambridge. My work focuses on modelling solar and stellar irradiance with statistical methods, machine learning, and physically informed models.
I enjoy connecting scientific rigor with computational efficiency, especially when building models that are interpretable, uncertainty-aware, and predictive under complex conditions.
I am also highly interested in quantitative finance, especially where statistical learning, risk modelling, and decision-making under uncertainty intersect with real-world markets.
Education
University of Cambridge · PhD in Astrophysics (Oct 2023 - Present)
University College London · MSc in Astrophysics (Sept 2022 - Sept 2023)
University of Strathclyde · BSc (Hons) Physics, First Class (Sept 2019 - Sept 2022)
Additional Education (Online Courses & Workshops)
Machine Learning, DeepLearning.AI - Stanford (via Coursera)
- Supervised ML: Regression & Classification.
- Advanced Learning Algorithms.
- Unsupervised Learning, Recommenders, Reinforcement Learning.
Data Analysis Workshop, Imperial College London
- Learning Bayesian methods and numerical techniques for astrophysical data analysis.
- Applying data analysis approaches to large datasets, improving model selection and parameter estimation.
CS50's Introduction to Cybersecurity, Harvard (online)
- Learning core cybersecurity principles: threat identification, risk assessment, and mitigation.
- Gaining hands-on skills in account security, multi-factor authentication, and social engineering awareness.
Memberships-Fellowships
Fellow of the Royal Astronomical Society (RAS).
Member of the Cambridge University Algorithmic Trading Society.
Member of the Cambridge University Astronomical Society.