Nina Otter

Nina Otter is a researcher at Inria Saclay's DataShape team and a member of the Mathematics Laboratory of Orsay (LMO) at Université Paris-Saclay. She specializes in topological data analysis, bridging mathematical disciplines like persistent homology, magnitude theory, and geometric deep learning.

• Education: PhD in Topological Data Analysis (Oxford, 2018), Master's in Pure Mathematics (ETH Zurich, 2014), Bachelor's in Physics and Mathematics (ETH Zurich, 2011)

Research Interests:
Nina Otter’s work focuses on generalizing topological data analysis tools for multiparametric datasets, merging deep learning with persistent homology, and applying these methods to climate modeling and social network analysis. She investigates theoretical foundations of topological invariants and their practical utility in complex systems. Her interdisciplinary projects connect mathematics with biology, machine learning, and climate science.

Article Trends:
Her publications demonstrate a trajectory from foundational mathematics (tropical elliptic curves) to applied topological methods in data science. Key themes include persistent homology software evaluation, categorical frameworks for biological data, and neural architecture design for topological summarization.

Initiatives:Nina founded DeMoS in 2021 to promote inclusive, interdisciplinary research practices. She actively explores climate data analysis using topological tools, addressing challenges like El Niño quasi-periodicity and atmospheric pattern modeling.