Professor Philipp Otto

Professor of Statistics and Data Science (Statistics)

Biography

Philipp Otto is a Professor of Statistics and Data Science at the University of Glasgow. His research focuses on developing statistical methods to understand, model, forecast, and monitor risks in spatially and temporally interconnected systems. His methodological contributions include the introduction and further development of spatial and spatiotemporal ARCH/GARCH models, which extend traditional volatility modelling to account for spatial dependence, volatility clustering, and risk spillovers. His broader research interests encompass spatial and spatiotemporal statistics, statistical process monitoring, environmetrics, and machine learning, with applications in environmental science, climate, finance, and other data-intensive fields.

Before joining the University of Glasgow in 2023, he was Assistant Professor of Big Geospatial Data at Leibniz University Hannover, Germany (2018–2023). He has also held visiting professorships at the University of Göttingen (Germany), the University of Bergamo (Italy), and the University of Malaya (Malaysia). He obtained his PhD in Statistics with distinction (summa cum laude) from the European University Viadrina, Germany, in 2016.

Philipp is a Senior Fellow of the University of Glasgow's Recognising Excellence in Teaching (RET) framework.

For further information, please visit his personal website: www.maths.gla.ac.uk/philipp.otto

Research interests

Philipp Otto's research focuses on developing statistical methodology for spatially and temporally dependent data, with particular emphasis on modelling uncertainty, volatility, and risk in complex interconnected systems. A central contribution of his work is the development of spatial and spatiotemporal ARCH/GARCH models, extending traditional volatility modelling to capture spatial dependence, volatility clustering, and risk spillovers across geographical and network structures.

His broader methodological interests include spatial and spatiotemporal statistics, spatial econometrics, network modelling, statistical process monitoring, functional data analysis, regularisation, and machine learning. He is particularly interested in combining interpretable statistical models with modern computational and AI-based approaches to analyse complex, high-dimensional data.

His research addresses methodological and practical challenges in environmental and climate science, financial markets, and other data-intensive domains. A recurring objective is to develop reliable methods for forecasting, risk assessment, anomaly detection, and statistical monitoring, supporting evidence-based decisions in complex spatial and network systems.

Research groups

Publications

List by: Type | Date

Jump to: 2026 | 2025 | 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015

Number of items: 61.

2026

Meli Chrisko, Ariane N., Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Schmid, Wolfgang (2026) Exponential spatiotemporal GARCH model with asymmetric volatility spillovers. Spatial Statistics, 76, 101042. (doi: 10.1016/j.spasta.2026.101042)

Achim, Mihai-Adrian, Belbe, Ștefana, Mare, Codruța and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2026) Spatial effects and uncertainty in cryptocurrencies – the case of network ARCH models. Finance Research Letters, 111, 110729. (doi: 10.1016/j.frl.2026.110729)

Betkier, Igor, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Wierzbicki, Damian and Karwowska, Kinga (2026) Service–effort trade-offs in large-scale bike-sharing rebalancing: a hierarchical reinforcement learning framework with imitation-based warm-starting. Applied Soft Computing, 203(D), 116271. (doi: 10.1016/j.asoc.2026.116271)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2026) Corrigendum to: "A multivariate spatial and spatiotemporal ARCH Model". Spatial Statistics, (doi: 10.1016/j.spasta.2026.101057) (Early Online Publication)

Eckardt, Matthias and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2026) Spatiotemporal autoregressive models for areal compositional data. Annals of Applied Statistics, (Accepted for Publication)

Nasiriboroujeni, Mohammadreza and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2026) Functional spatiotemporal profile monitoring: a case study from Beijing on air quality. Journal of Quality Technology, (doi: 10.1080/00224065.2026.2657277) (Early Online Publication)

Morelli, Caterina, Maranzano, Paolo and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2026) Spatiotemporal clustering of GHGs emissions in Europe: exploring the role of spatial component. Spatial Statistics, 73, 100960. (doi: 10.1016/j.spasta.2026.100960)

Rodeschini, Jacopo, Tedesco, Lorenzo, Finazzi, Francesco, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Fassò, Alessandro (2026) Multivariate low-rank state–space model with SPDE approach for high-dimensional data. Spatial Statistics, 73, 100971. (doi: 10.1016/j.spasta.2026.100971)

Nyabuto, Elkanah Magembe, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Okhrin, Yarema (2026) Estimation of spatial and temporal autoregressive effects using LASSO - an example of hourly particulate matter concentrations. Environmetrics, 37(4), e70105. (doi: 10.1002/env.70105)

Malinovskaya, Anna, Killick, Rebecca, Leeming, Kathryn and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2026) Statistical monitoring with novel temporal edge network processes. Journal of the Royal Statistical Society: Series C (Applied Statistics), qlag003. (doi: 10.1093/jrsssc/qlag003) (Early Online Publication)

2025

Mattera, Giulio, Mattera, Raffaele and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2025) Hybrid statistical process monitoring of wire Arc additive manufacturing with frequency informed deep learning. Quality and Reliability Engineering International, 41(8), pp. 3334-3349. (doi: 10.1002/qre.70041)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Doğan, Osman and Taşpınar, Süleyman (2025) A note on dynamic spatiotemporal ARCH models: small- and large-sample results. AStA Advances in Statistical Analysis, 109(4), pp. 811-828. (doi: 10.1007/s10182-025-00552-3)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Illian, Janine ORCID: https://orcid.org/0000-0002-6130-2796 (2025) Advances in spatial econometrics and geostatistics: methods, theory, and applications. AStA Advances in Statistical Analysis, 109(4), pp. 633-636. (doi: 10.1007/s10182-025-00551-4)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Doğan, Osman, Taspinar, Suleyman, Schmid, Wolfgang and Bera, Anil (2025) Spatial and spatiotemporal volatility models: a review. Journal of Economic Surveys, 39(3), pp. 1037-1091. (doi: 10.1111/joes.12643)

Colombo, Pietro, Mattera, Raffaele and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2025) Simple yet effective: a comparative study of statistical models for yearly hurricane forecasting. Environmetrics, 36(3), e70009. (doi: 10.1002/env.70009)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2025) Discussion on “Assessing predictability of environmental time series with statistical and machine learning models” by Bonas et al. Environmetrics, 36(2), e2898. (doi: 10.1002/env.2898)

2024

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Fasso, Alessandro and Maranzano, Paolo (2024) A review of regularised estimation methods and cross-validation in spatiotemporal statistics. Statistics Surveys, 18, pp. 299-340. (doi: 10.1214/24-SS150)

Belbe, Ștefana, Moldovan, Darie, Andrieș, Alin Marius, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Mare, Codruța (2024) Evaluation of the space-time effects of Covid-19 on household loans and savings in Romania - A spatial panel data approach at county level. Emerging Markets Review, 63, 101209. (doi: 10.1016/j.ememar.2024.101209)

Dorau, Kristof, Rückamp, Daniel, Weber, Christian, Scheeder, Georg, Reßing, Ronja, Peth, Stephan, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Altmann, Korinna, Fries, Elke and Hoppe, Martin (2024) Characterization and spatial distribution of mesoplastics in an arable soil. European Journal of Soil Science, 75(6), e70016. (doi: 10.1111/ejss.70016)

Knoth, Sven, Okhrin, Yarema and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (Eds.) (2024) Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science: Essays in Honour of Wolfgang Schmid. Springer: Cham. ISBN 9783031691102

Mattera, Raffaele and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2024) Network log-ARCH models for forecasting stock market volatility. International Journal of Forecasting, 40(4), pp. 1539-1555. (doi: 10.1016/j.ijforecast.2024.01.002)

Khoo, Tzung Hsuen, Pathmanathan, Dharini, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Dabo‐Niang, Sophie (2024) A Markov‐switching spatio‐temporal ARCH model. Stat, 13(3), e713. (doi: 10.1002/sta4.713)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Fusta Moro, Alessandro, Rodeschini, Jacopo, Shaboviq, Qendrim, Ignaccolo, Rosaria, Golini, Natalia, Cameletti, Michela, Maranzano, Paolo, Finazzi, Francesco and Fassò, Alessandro (2024) Spatiotemporal modelling of PM2.5 concentrations in Lombardy (Italy): a comparative study. Environmental and Ecological Statistics, 31(2), pp. 245-272. (doi: 10.1007/s10651-023-00589-0)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2024) A multivariate spatial and spatiotemporal ARCH model. Spatial Statistics, 60, 100823. (doi: 10.1016/j.spasta.2024.100823)

Mohammadi, Reza, Taleai, Mohammad, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Sester, Monika (2024) Analyzing urban crash incidents: an advanced endogenous approach using spatiotemporal weights matrix. Transactions in GIS, 28(2), pp. 368-410. (doi: 10.1111/tgis.13138)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Doğan, Osman and Taşpınar, Süleyman (2024) Dynamic spatiotemporal ARCH models. Spatial Economic Analysis, 19(2), pp. 250-271. (doi: 10.1080/17421772.2023.2254817)

Malinovskaya, Anna, Mozharovskyi, Pavlo and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2024) Statistical process monitoring of artificial neural networks. Technometrics, 66(1), pp. 104-117. (doi: 10.1080/00401706.2023.2239886)

Fülle, Markus J. and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2024) Spatial GARCH models for unknown spatial locations – an application to financial stock returns. Spatial Economic Analysis, 19(1), pp. 92-105. (doi: 10.1080/17421772.2023.2237067)

2023

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Doğan, Osman and Taşpınar, Süleyman (2023) A dynamic spatiotemporal stochastic volatility model with an application to environmental risks. Econometrics and Statistics, (doi: 10.1016/j.ecosta.2023.11.002) (In Press)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Schmid, Wolfgang (2023) A general framework for spatial GARCH models. Statistical Papers, 64(5), pp. 1721-1747. (doi: 10.1007/s00362-022-01357-1)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Steinert, Rick (2023) Estimation of the spatial weighting matrix for spatiotemporal data under the presence of structural breaks. Journal of Computational and Graphical Statistics, 32(2), pp. 696-711. (doi: 10.1080/10618600.2022.2107530)

Fassò, Alessandro, Rodeschini, Jacopo, Fusta Moro, Alessandro, Shaboviq, Qendrim, Maranzano, Paolo, Cameletti, Michela, Finazzi, Francesco, Golini, Natalia, Ignaccolo, Rosaria and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2023) Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. Scientific Data, 10(1), 143. (doi: 10.1038/s41597-023-02034-0) (PMID:36934159) (PMCID:PMC10024000)

Harke, Franz and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2023) Solar self-sufficient households as a driving factor for sustainability transformation. Sustainability, 15(3), 2734. (doi: 10.3390/su15032734)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Otto, Philip (2023) What’s in a name? Significance, 20(1), pp. 34-37. (doi: 10.1093/jrssig/qmad010)

2022

Garthoff, Robert and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2022) Spatiotemporal procedures for the statistical surveillance of spatial autoregressive models with heavy tails. Communications in Statistics: Simulation and Computation, 51(10), pp. 5709-5737. (doi: 10.1080/03610918.2020.1779294)

Otto, Philipp and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2022) Impact of academic authorship characteristics on article citations. Revstat Statistical Journal, 20(4), pp. 427-447. (doi: 10.57805/revstat.v20i4.382)

Harke, Franz H., Merk, Miryam S. and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2022) Estimation of Asymmetric Spatial Autoregressive Dependence on Irregular Lattices. Symmetry, 14(7), 1474. (doi: 10.3390/sym14071474)

Piter, Andreas, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Alkhatib, Hamza (2022) The Helsinki bike‐sharing system—Insights gained from a spatiotemporal functional model. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185(3), pp. 1294-1318. (doi: 10.1111/rssa.12834)

Fassò, Alessandro, Maranzano, Paolo and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2022) Spatiotemporal variable selection and air quality impact assessment of COVID-19 lockdown. Spatial Statistics, 49, 100549. (doi: 10.1016/j.spasta.2021.100549)

Merk, Miryam S. and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2022) Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross-sectional resampling. Environmetrics, 33(1), e2705. (doi: 10.1002/env.2705)

Malinovskaya, Anna, Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Peters, Torben (2022) Statistical learning for change point and anomaly detection in graphs. In: Steland, Ansgar and Tsui, Kwok-Leung (eds.) Artificial Intelligence, Big Data and Data Science in Statistics. Springer, pp. 85-109. ISBN 9783031071553 (doi: 10.1007/978-3-031-07155-3_4)

2021

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Piter, Andreas and Gijsman, Rik (2021) Statistical analysis of beach profiles – A spatiotemporal functional approach. Coastal Engineering, 170, 103999. (doi: 10.1016/j.coastaleng.2021.103999)

Malinovskaya, Anna and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2021) Online network monitoring. Statistical Methods and Applications, 30, pp. 1337-1364. (doi: 10.1007/s10260-021-00589-z)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682, Schmid, Wolfgang and Garthoff, Robert (2021) Stochastic properties of spatial and spatiotemporal ARCH models. Statistical Papers, 62(2), pp. 623-638. (doi: 10.1007/s00362-019-01106-x)

Merk, M.S. and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2021) Directional spatial autoregressive dependence in the conditional first- and second-order moments. Spatial Statistics, 41, 100490. (doi: 10.1016/j.spasta.2020.100490)

Antoniuk, Anna, Merk, Miryam S. and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2021) Spatial statistics, or how to extract knowledge from data. In: Werner, Martin and Chiang, Yao-Yi (eds.) Handbook of Big Geospatial Data. Springer, pp. 399-426. ISBN 9783030554620 (doi: 10.1007/978-3-030-55462-0_15)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2021) Parallelized monitoring of dependent spatiotemporal processes. In: Knoth, Sven and Schmid, Wolfgang (eds.) Frontiers in Statistical Quality Control 13. Springer, pp. 165-183. ISBN 9783030678562 (doi: 10.1007/978-3-030-67856-2_10)

2020

Merk, Miryam S. and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2020) Estimation of anisotropic, time-varying spatial spillovers of fine particulate matter due to wind direction. Geographical Analysis, 52(2), pp. 254-277. (doi: 10.1111/gean.12205)

2019

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2019) spGARCH: An R-package for spatial and spatiotemporal ARCH and GARCH models. R Journal, 11(2), pp. 401-420.

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2019) Modeling Spatial Dependence in Local Risks and Uncertainties. In: 29th European Safety and Reliability Conference, ESREL 2019, Hannover, Germany, 22 September-26 September 2019, pp. 2685-2692. ISBN 9789811127243

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2019) Parallelized Monitoring of Dependent Spatiotemporal Processes. In: 13th International Workshop on Intelligent Statistical Quality Control, IWISQC 2019, Hong Kong, 12 - 14 August 2019, pp. 181-194.

2018

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Schmid, Wolfgang (2018) Discussion of “Statistical methods for network surveillance” by Daniel Jeske, Nathaniel Stevens, Alexander Tartakovsky, and James Wilson. Applied Stochastic Models in Business and Industry, 34(4), pp. 452-456. (doi: 10.1002/asmb.2360)

Garthoff, Robert and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2018) Verfahren zur Überwachung räumlicher autoregressiver Prozesse mit externen Regressoren = Statistical surveillance of spatial autoregressive processes with exogenous regressors. AStA Wirtschafts- und Sozialstatistisches Archiv, 12(2), pp. 107-133. (doi: 10.1007/s11943-018-0224-1)

2017

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2017) A note on efficient simulation of multidimensional spatial autoregressive processes. Communications in Statistics: Simulation and Computation, 46(6), pp. 4547-4558. (doi: 10.1080/03610918.2015.1122050)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Lange, Anna-Liesa (2017) Arbeitsbuch der Angewandten Statistik: Mit Aufgaben zur Software R und detaillierten Lösungen. Springer Gabler: Berlin, Heidelberg. ISBN 9783662492116 (doi: 10.1007/978-3-662-49212-3)

2016

Lange, Anna-Liesa and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2016) Bayes’sche Statistik in der Dienstleistungsforschung. AStA Wirtschafts- und Sozialstatistisches Archiv, 10(4), pp. 247-267. (doi: 10.1007/s11943-016-0189-x)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Schmid, Wolfgang (2016) Spatiotemporal analysis of German real-estate prices. Annals of Regional Science, 60, pp. 41-72. (doi: 10.1007/s00168-016-0789-y)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2016) Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models. Biometrical Journal, 58(5), pp. 1113-1137. (doi: 10.1002/bimj.201500148)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Schmid, Wolfgang (2016) Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models. Biometrical Journal, 58(5), pp. 1113-1137. (doi: 10.1002/bimj.201500148)

Garthoff, Robert and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2016) Control charts for multivariate spatial autoregressive models. AStA Advances in Statistical Analysis, 101, pp. 67-94. (doi: 10.1007/s10182-016-0276-x)

2015

Garthoff, Robert and Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2015) Simultaneous surveillance of means and covariances of spatial models. In: Steland, A., Rafajłowicz, E. and Szajowski, K. (eds.) Stochastic Models, Statistics and Their Applications. Series: Springer proceedings in mathematics and statistics (122). Springer: Cham, pp. 271-281. ISBN 9783319138800 (doi: 10.1007/978-3-319-13881-7_30)

This list was generated on Thu Oct 29 03:39:00 2026 GMT.

Jump to: Articles | Books | Book Sections | Edited Books | Conference Proceedings

Number of items: 61.

Articles

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2024) A multivariate spatial and spatiotemporal ARCH model. Spatial Statistics, 60, 100823. (doi: 10.1016/j.spasta.2024.100823)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 and Otto, Philip (2023) What’s in a name? Significance, 20(1), pp. 34-37. (doi: 10.1093/jrssig/qmad010)

Otto, Philipp ORCID: https://orcid.org/0000-0002-9796-6682 (2019) spGARCH: An R-package for spatial and spatiotemporal ARCH and GARCH models. R Journal, 11(2), pp. 401-420.

Books

Book Sections

Edited Books

Conference Proceedings

This list was generated on Thu Oct 29 03:39:00 2026 GMT.

Grants

I received research grants amounting to 1,038,847 euros (counting my own share only). My main/recent research projects dealt with:

- Time series analysis of historical maps,
- Statistical estimation of high-dimensional, spatial dependence structures using machine learning methods,
- Agricultural impact on air quality,
- Spatial and spatiotemporal GARCH models,
- Detection of spatiotemporal clusters.

Furthermore, I successfully collaborated on industry-funded projects, e.g., on survival analysis for building information models.

More information about my current projects can be found on my website: www.maths.gla.ac.uk/philipp.otto/projects.html

Supervision

Current PhD Students

Ding Wang (University of Glasgow)
Hybrid models for volatility prediction on financial networks
Khaloud Al Ajmi (University of Glasgow)
Hybrid statistical modelling techniques in environmetrics
Elkanah Nyabuto (University of Glasgow)
Regularised estimation methods for spatiotemporal autoregressive and ARCH models
Mohammadreza Nasiriboroujeni (Leibniz University Hannover, Germany)
Statistical process monitoring for spatiotemporal functional data and additive manufacturing
Mireille Fangueng (Leibniz University Hannover, Germany)
Flexible time series analysis of historical maps
Han Ying Lim (University of Malaya, Malaysia)
Co-supervised with Dharini Pathmanathan and Sophie Dabo

Former PhD Students

Ariane Nidelle Meli Chrisko (University of Glasgow, 2026)
Spatiotemporal GARCH Modelling with Asymmetric Volatility Spillovers and Applications
Anna Malinovskaya (Leibniz University Hannover, 2024)
Statistical Process Monitoring of Networks
Miryam S. Merk (European University Viadrina, 2021)
Spatial and Spatiotemporal Stochastic Processes: Estimation and Modeling of Spatial Dependencies with Applications in Environmetrics
Caterina Morelli (University of Milano-Bicocca, 2025; co-supervised)
Sustainability by Proximity: How Geographic Closeness Shapes Corporate and Regional Carbon Emissions

Further Information

Further information about my research group, including visiting PhD students, postdoctoral researchers, and collaborators, is available on my personal website and the dedicated research group page.

Alajmi, Khaloud Ibrahim Ali
Data-Driven Spatial Analysis of Climate and Environmental Variability

Teaching

Philipp Otto has extensive teaching experience in statistics and data science at undergraduate, postgraduate, and doctoral levels. His teaching covers statistical modelling, generalised linear models, probability, sampling, computational statistics, and data analytics, combining rigorous theoretical foundations with practical applications and modern computational methods.

He has taught students from a wide range of disciplines, including statistics, mathematics, economics, engineering, mechatronics, computer science, and geodesy/geoinformatics. His teaching experience encompasses lectures, seminars, tutorials, computer-based practical sessions, and research supervision, delivered in both traditional classroom and online learning environments.

At the University of Glasgow, he leads the postgraduate online distance learning provision in Statistics and Data Analytics and contributes to postgraduate education and programme development. He is a Senior Fellow of the University's Recognising Excellence in Teaching (RET) framework, reflecting his commitment to high-quality teaching and student learning.

Professional activities & recognition

Prizes, awards & distinctions

2017: Fellowship to attend the Lindau Nobel Laureate Meeting (6th Lindau Nobel Laureate Meeting on Economic Sciences)
2017: Best Presentation Award (Data Science, Statistics and Visualisation)
2024: Senior Fellow (Recognising Excellence in Teaching)
2025: (Springer Editorial Award)

Grant committees & research advisory boards

2026: Royal Statistical Society, Climate Change and Net Zero Task Force
2024 - 2024: German Research Foundation (DFG), External Review Panel

Editorial boards

2021: Environmetrics
2020: AStA Advances in Statistical Analysis
2020: AStA Wirtschafts- und Sozialstatistisches Archiv

Professional & learned societies

2013: Executive Board Member and Treasurer, German Statistical Society
2024: Head of the Section on Computational Statistics and Data Science, German Statistical Society
2020: Elected Member, Young Braunschweig Scientific Society

Philipp Otto

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School of Mathematics & Statistics