Dr Nina Otter

Teaching

• Semester B 2023: MTH767P Neural Networks and Deep Learning
• Semester A 2022: MTH765P Storing, Manipulating and Visualising Data
• Semester B 2022: MTH767P Neural Networks and Deep Learning
• Semester A 2021: MTH793P Advanced Machine Learning

Research

Research Interests:

In my research I am interested in using methods from algebra, geometry and topology to study data. I am currently particularly interested in advancing our understanding of weather regimes using methods from topology, as well as developing algebraic and topological methods to model higher-order relationships in social systems.

Examples of research funding:

•  Royal Society Research Grant (£16,959.60) 02.2022 - 02.2023

Publications

My research is interdisciplinary, and the order of authors in different publications follows conventions dictated by different disciplines.

Weather regimes

• A topological perspective on weather regimes, K. Strommen, M. Chantry, J. Dorrington, NO, Climate Dynamics, 2022 https://doi.org/10.1007/s00382-022-06395-x

Persistent homology

• On the effectiveness of persistent homology, R. Turkeš, G. Montúfar, NO, 2022, preprint

• Amplitudes on abelian categories, B. Giunti, J. Nolan, NO, L. Waas, 2021, preprint

• Stratifying multiparameter persistent homology, H. Harrington, NO, H. Schenck, U. Tillmann), SIAM Journal on Applied Algebraic Geometry, 3(3):439-471 (2019)

• A roadmap for the computation of persistent homology, NO, M. Porter, U. Tillmann, P. Grindrod, H. Harrington, EPJ Data Science, 2017, 6:17 (2017)

Magnitude

• Alpha magnitude, M. O’Malley, S. Kalisnik, NO, 2022, preprint

• Magnitude meets persistence. Homology theories for filtered simplicial sets, NO, Homology, Homotopy and Applications, 24(2), 2022, pp.401-423

Social networks

• A unififed framework for equivalences in social networks, NO, M. A. Porter, 2020, preprint

Geometric deep learning

• Weisfeiler and Lehman Go Cellular: CW Networks, C. Bodnar, F. Frasca, NO, Y. G. Wang, P. Liò, G. Montúfar, M. Bronstein, Advances in Neural Information Processing Systems (NeurIPS 2021), 34:2625–2640, 2021

• Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks, C. Bodnar, F. Frasca, Y. G. Wang, G. Montufar, NO, P. Lio, M. Bronstein, Proceedings of the 38th International Conference on Machine Learning (ICML 2021) PMLR 139:1026–1037, 2021

• Can neural networks learn persistent homology features?, G. Montufar, Y. G. Wang, NO, Topological Data Analysis and Beyond Workshop, at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada

Phylogenetics

• Operads and phylogenetic trees, J. Baez, NO, Theory and Applications of Categories, 33(40):1397-1453 (2017).

• O'Malley M, Kalisnik S, Otter N (2023). Alpha magnitude Journal of Pure and Applied Algebra nameOfConference.

  10.1016/j.jpaa.2023.107396

  qmroHref
• Otter N (2022). Magnitude meets persistence. Homology theories for filtered simplicial sets Homology, Homotopy and Applications nameOfConference.

  10.4310/HHA.2022.v24.n2.a12

  https://qmro.qmul.ac.uk/xmlui/handle/123456789/79678
• Otter N, Strommen K, Chantry M et al. (2022). A topological perspective on weather regimes Climate Dynamics nameOfConference.

  10.1007/s00382-022-06395-x

  https://qmro.qmul.ac.uk/xmlui/handle/123456789/79680
• Harrington HA, Otter N, Schenck H et al. (2019). Stratifying Multiparameter Persistent Homology SIAM Journal on Applied Algebra and Geometry nameOfConference.

  10.1137/18m1224350

  qmroHref
• Otter N, Porter MA, Tillmann U et al. (2017). A roadmap for the computation of persistent homology. EPJ Data Sci nameOfConference.

  10.1140/epjds/s13688-017-0109-5

  qmroHref