My current research is mainly focused on topological and geometric machine learning and its application. Previously I have worked on topics ranging from epidemiology, toxicology and geopyhsics to statistics and topological data analysis. Below you find a list of publications.

[1]
G. H. Bringeland, N. Blaser, K.-M. Myhr, C. A. Vedeler, and S. Gavasso, “Wearing-off symptoms during standard and extended natalizumab dosing intervals: Experiences from the COVID-19 pandemic,” Journal of the Neurological Sciences, p. 117622, 2021, doi: https://doi.org/10.1016/j.jns.2021.117622.
[2]
N. Galmiche, H. Hauser, T. Spengler, C. Spensberger, M. Brun, and N. Blaser, Revealing Multimodality in Ensemble Weather Prediction,” in Machine learning methods in visualisation for big data, 2021, doi: 10.2312/mlvis.20211073.
[3]
K. Gundersen, A. Oleynik, N. Blaser, and G. Alendal, “Semi-conditional variational auto-encoder for flow reconstruction and uncertainty quantification from limited observations,” Physics of Fluids, vol. 33, no. 1, p. 017119, 2021, doi: 10.1063/5.0025779.
[4]
N. Blaser and M. Aupetit, “Research directions to validate topological models of multi-dimensional data,” 2020, [Online]. Available: https://openreview.net/references/pdf?id=amu4diB8yU.
[5]
K. Gundersen, G. Alendal, A. Oleynik, and N. Blaser, “Binary time series classification with bayesian convolutional neural networks when monitoring for marine gas discharges,” Algorithms, vol. 13, p. 145, Jun. 2020, doi: 10.3390/a13060145.
[6]
N. Blaser and M. Brun, Relative Persistent Homology,” in 36th international symposium on computational geometry (SoCG 2020), 2020, vol. 164, pp. 18:1–18:10, doi: 10.4230/LIPIcs.SoCG.2020.18.
[7]
A. Oleynik, M. I. García-Ibáñez, N. Blaser, A. Omar, and G. Alendal, “Optimal sensors placement for detecting CO2 discharges from unknown locations on the seafloor,” International Journal of Greenhouse Gas Control, vol. 95, p. 102951, 2020, doi: 10.1016/j.ijggc.2019.102951.
[8]
G. H. Bringeland, N. Blaser, K.-M. Myhr, C. A. Vedeler, and S. Gavasso, “Wearing-off at the end of natalizumab dosing intervals is associated with low receptor occupancy,” Neurology - Neuroimmunology Neuroinflammation, vol. 7, no. 3, 2020, doi: 10.1212/NXI.0000000000000678.
[9]
N. Blaser and M. Brun, “Sparse nerves in practice,” in Machine learning and knowledge extraction, 2019, pp. 272–284, doi: 10.1007/978-3-030-29726-8_17.
[10]
L. Bader et al., “Candidate markers for stratification and classification in rheumatoid arthritis,” Frontiers in Immunology, vol. 10, p. 1488, 2019, doi: 10.3389/fimmu.2019.01488.
[11]
M. Brun and N. Blaser, “Sparse dowker nerves,” Journal of Applied and Computational Topology, pp. 1–28, 2019, doi: 10.1007/s41468-019-00028-9.
[12]
K. Dale et al., “Contaminant accumulation and biological responses in atlantic cod (gadus morhua) caged at a capped waste disposal site in kollevåg, western norway,” Marine Environmental Research, 2019, doi: 10.1016/j.marenvres.2019.02.003.
[13]
G. H. Bringeland et al., “Optimization of receptor occupancy assays in mass cytometry: Standardization across channels with QSC beads,” Cytometry Part A, 2019, doi: 10.1002/cyto.a.23723.
[14]
K. E. M. Ahmed et al., “An LC-MS/MS approach to reveal the effects of POPs and endocrine disruptors on the steroidogenesis of the human H295R adrenocortical cell line,” Chemosphere, 2019, doi: 10.1016/j.chemosphere.2018.11.057.
[15]
H. G. Frøysa, S. Fallahi, and N. Blaser, “Evaluating model reduction under parameter uncertainty,” BMC Systems Biology, vol. 12, no. 1, p. 79, 2018, doi: 10.1186/s12918-018-0602-x.
[16]
N. Blaser and M. Brun, “Divisive cover,” Mathematics in Computer Science, vol. 13, no. 1, pp. 21–29, 2019, doi: 10.1007/s11786-018-0352-6.
[17]
F. Yadetie et al., RNA-seq analysis of transcriptome responses in atlantic cod (gadus morhua) precision-cut liver slices exposed to benzo[a]pyrene and 17--ethynylestradiol,” Aquatic Toxicology, vol. 201, pp. 174–186, 2018, doi: 10.1016/j.aquatox.2018.06.003.
[18]
J. Estill et al., “The effect of monitoring viral load and tracing patients lost to follow-up on the course of the HIV epidemic in malawi: A mathematical model,” Open Forum Infectious Diseases, vol. 5, no. 5, p. ofy092, 2018, doi: 10.1093/ofid/ofy092.
[19]
N. Blaser et al., Impact of screening and ART on anal cancer incidence in HIV-positive men who have sex with men: Mathematical modeling study,” AIDS, 2017, doi: 10.1097/QAD.0000000000001546.
[20]
J. Estill et al., “Estimating the need of second-line antiretroviral therapy in sub-Saharan Africa up to 2030: A mathematical model,” The Lancet HIV, vol. 3, no. 3, pp. e132–e139, 2016, doi: 10.1016/S2352-3018(16)00016-3.
[21]
N. Blaser et al., “Tuberculosis in cape town: An age-structured transmission model,” Epidemics, vol. 14, pp. 54–61, 2016, doi: 10.1016/j.epidem.2015.10.001.
[22]
J. Estill, L. Salazar-Vizcaya, N. Blaser, M. Egger, and O. Keiser, “The Cost-Effectiveness of Monitoring Strategies for Antiretroviral Therapy of HIV Infected Patients in Resource-Limited Settings: Software Tool,” PLoS ONE, vol. 10, no. 3, p. e0119299, 2015, doi: 10.1371/journal.pone.0119299.
[23]
N. Blaser et al., gems: An R package for simulating from disease progression models,” Journal of Statistical Software, vol. 64, no. 10, pp. 1–22, 2015, doi: 10.18637/jss.v064.i10.
[24]
L. Salazar-Vizcaya et al., “Viral load versus CD\(4^{+}\) monitoring and 5-year outcomes of antiretroviral therapy in HIV-positive children in Southern Africa: A cohort-based modelling study,” AIDS, vol. 28, no. 16, pp. 2451–2460, 2014, doi: 10.1097/QAD.0000000000000446.
[25]
M. Petersen, J. Schwab, S. Gruber, N. Blaser, M. Schomaker, and M. van der Laan, “Targeted maximum likelihood estimation for dynamic and static longitudinal marginal structural working models,” Journal of Causal Inference, vol. 2, no. 2, pp. 147–185, 2014, doi: 10.1515/jci-2013-0007.
[26]
N. Blaser et al., “Impact of viral load and the duration of primary infection on HIV transmission: Systematic review and meta-analysis,” AIDS, vol. 28, no. 7, pp. 1021–1029, 2014, doi: 10.1097/QAD.0000000000000135.
[27]
J. Estill et al., “Tracing of patients lost to follow-up and HIV transmission: Mathematical modeling study based on 2 large ART programs in Malawi,” Journal of acquired immune deficiency syndromes, vol. 65, no. 5, pp. e179–186, 2014, doi: 10.1097/QAI.0000000000000075.
[28]
D. Keebler et al., “Cost-effectiveness of different strategies to monitor adults on antiretroviral treatment: A combined analysis of three mathematical models,” The Lancet Global Health, vol. 2, no. 1, pp. e35–e43, 2014, doi: 10.1016/S2214-109X(13)70048-2.
[29]
J. Estill et al., “Cost-effectiveness of point-of-care viral load monitoring of antiretroviral therapy in resource-limited settings: Mathematical modelling study,” AIDS, vol. 27, no. 9, pp. 1483–1492, 2013, doi: 10.1097/QAD.0b013e328360a4e5.
[30]
C. Wettstein et al., “Missed opportunities to prevent mother-to-child-transmission: Systematic review and meta-analysis,” AIDS, vol. 26, no. 18, pp. 2361–2373, 2012, doi: 10.1097/QAD.0b013e328359ab0c.