Isabel Valera

Isabel Valera is a full Professor in the Department of Computer Science at Saarland University, Saarbrücken, Germany. She also holds an adjunct faculty position at the Max Planck Institute for Software Systems (MPI-SWS) and is a fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS), contributing to the Robust Machine Learning Program and the Saarbrücken AI & Machine Learning (Sam) Unit.

Research Interests: Her work lies at the intersection of machine learning, fairness, and causality. She focuses on developing methods for algorithmic recourse, fair decision-making, causal modeling, and robust learning. She is particularly interested in designing models that provide actionable explanations and ensure equitable outcomes in AI systems. Her research leverages probabilistic modeling, variational inference, and deep generative architectures.

Publication Trends: Her recent publications (2021–2023) show a strong emphasis on algorithmic recourse, fairness under uncertainty, causal representation learning, and multimodal and graph-based generative models. She frequently publishes in top-tier venues such as NeurIPS, ICML, ICLR, AAAI, and FAccT, often in collaboration with leading researchers like Bernhard Schölkopf and Zoubin Ghahramani.

Scientific Awards and Fellowships:

• Humboldt Post-Doctoral Fellowship
• Minerva Fast Track Fellowship (Max Planck Society)
• ELLIS Fellow

Advising and Grants: She has mentored several early-career researchers who are now active contributors in machine learning, including Adrián Javaloy and Amir-Hossein Karimi. While specific grant details are not listed, her leadership roles and fellowships indicate sustained funding support from major research organizations. She has served as a co-editor for major conferences such as AISTATS and ECML PKDD, demonstrating her active role in the academic community.

Labs and Research Groups: She leads or has led research groups at both Saarland University and the Max Planck Institutes (MPI for Intelligent Systems and MPI for Software Systems), focusing on foundational and applied aspects of machine learning with societal impact.