Giulia Ferrandi

Giulia Ferrandi is a Researcher and Guest Researcher at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). She completed her PhD under the supervision of Prof. Michiel Hochstenbach (TU/e) and Prof. Rosário Oliveira (IST, Lisbon) as part of the European BIGMATH project. Her research focuses on Statistics, Linear Algebra, and Optimization applied to statistical problems, with contributions to gradient methods, trace ratio problems, and Markov chain analysis. She holds a master’s degree from Università degli Studi di Milano (Italy), specializing in Probability and Statistics. Prior to her academic roles, she briefly taught in high school and worked in industry.

Research Interests:
Her work bridges numerical linear algebra and optimization, with emphasis on:

• Development and analysis of gradient methods (e.g., limited memory, harmonic frameworks)
• Applications of Rayleigh quotients in optimization and eigenvalue problems
• Robust multigroup classification via trace ratio techniques
• Statistical analysis of Markov chains for farmland transitions and non-stationary processes

Publications: Her recent work demonstrates contributions to:

• Subspace methods for large-scale trace ratio problems
• Advancements in unconstrained optimization via limited-memory gradient approaches
• Integration of Rayleigh quotients into gradient-based algorithms
• Harmonic frameworks for optimizing stepsizes in numerical methods

Collaborations & Grants:
Her PhD was funded by the EU’s BIGMATH project. Collaborators include Prof. Michiel Hochstenbach (TU/e) and Prof. Nataša Krejić (University of Novi Sad). No explicit advising roles or grants are listed beyond her doctoral funding.

Technical Expertise: Expertise in numerical analysis, statistical modeling, and linear algebra applications.