A roadmap for the computation of persistent homology. - Nina Otter; Mason A. Porter; Ulrike Tillmann; Peter Grindrod; Heather A. Harrington (EPJ Data Sci., 2017)

Stratifying Multiparameter Persistent Homology. - Heather A. Harrington; Nina Otter; Hal Schenck; Ulrike Tillmann (SIAM J. Appl. Algebra Geom., 2019)

A unified framework for equivalences in social networks. - Nina Otter; Mason A. Porter (CoRR, 2020)

Can neural networks learn persistent homology features? - Guido Montúfar; Nina Otter; Yuguang Wang 001 (CoRR, 2020)

Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks. - Cristian Bodnar; Fabrizio Frasca; Yuguang Wang 001; Nina Otter; Guido F. Montúfar; Pietro Lió; Michael M. Bronstein (ICML, 2021)

Weisfeiler and Lehman Go Cellular: CW Networks. - Cristian Bodnar; Fabrizio Frasca; Nina Otter; Yuguang Wang 001; Pietro Liò; Guido F. Montúfar; Michael M. Bronstein (NeurIPS, 2021)

On the Effectiveness of Persistent Homology. - Renata Turkes; Guido F. Montúfar; Nina Otter (NeurIPS, 2022)

Time-optimal persistent homology representatives for univariate time series. - Antonio Leitao; Nina Otter (CoRR, 2024)

Shoving tubes through shapes gives a sufficient and efficient shape statistic. - Adam Onus; Nina Otter; Renata Turkes (CoRR, 2024)

Pull-back Geometry of Persistent Homology Encodings. - Shuang Liang; Renata Turkes; Jiayi Li; Nina Otter; Guido Montúfar (Trans. Mach. Learn. Res., 2024)

Fractal dimensions of complex networks: advocating for a topological approach. - Rayna Andreeva; Haydeé Contreras-Peruyero; Sanjukta Krishnagopal; Nina Otter; Maria Antonietta Pascali; Elizabeth Thompson (CoRR, 2025)