About me

Since April 2024, I am a Research Associate for Bayesian Statistics and Causal Inference at School of Public Health, Imperial College London, working with Verena Zuber and Leonardo Bottolo. My main research focus lies in discovering patterns and causal relationship in single-cell genomic and genetic variation data with tools from Bayesian statistics, such as model-based clustering and latent factor analysis.

In 2023, I obtained my PhD at Vienna University of Economics and Business under the supervision of Sylvia Frühwirth-Schnatter and Alfred Stiassny. My dissertation titled “Bayesian methods for unsupervised data analysis in application to data sets exhibiting non-Gaussianity” was devoted to discovering structure in complex data with no or minimal initial assumptions. It comprises of three parts which include a comprehensive review of infinite factorisiation models, a model that relaxes the assumtion of Gaussian factors habitual in Bayesian latent factor models, and a flexible mixture of factor analysers model which provides fully automatic detection of both clusters in the data set and latent dimensions in each cluster. The latter paper, which has recently been published in Bayesian Analysis, employs the connection between infinite nonparametric priors and their finite counterparts to provide a fully automatic inference on data structure without resorting to infinite models.

Prior to that, I worked for several years at Erste Group as a Quantitative Analyst in charge of time series modeling and macroecnomic forecasting. My detailed CV can be found here: CV

Research Interests

• Bayesian nonparametrics
• Bayesian latent factor models
• Mixture models based clustering (Dirichlet process mixtures, Pitman-Yor process mixtures, mixture of finite mixtures models)
• Time series models and forecasting
• Variational inference

Upcoming Events

Publications

Papers and Preprints

• Dynamic Mixture of Finite Mixtures of Factor Analysers. M. Grushanina and S. Frühwirth-Schnatter (2025). Bayesian Analysis (advance publication).
  paper

• A Review of Bayesian Methods for Infinite Factorisations.
  M. Grushanina (2023).
  arXiv preprint

Conference Proceedings

• Bayesian infinite factor models with non-Gaussian factors.
  M. Grushanina and S. Frühwirth-Schnatter (2021).
  In JSM Proceedings, International Society for Bayesian Analysis (ISBA) Section. Alexandria, VA: American Statistical Association. 396-415.
  conference paper

Discussions

• Discussion on Sparse Bayesian Factor Analysis When the Number of Factors Is Unknown by S. Frühwirth-Schnatter, D. Hosszejni, and H. F. Lopes.
  M. Grushanina (2025). Bayesian Analysis, 20(1), pp. 316-318.
  discussion

Presentations & Posters

• Seminar at EUSP (virtual, January 2025). Dynamic mixture of finite mixtures of factor analysers.
• Invited talk at CFE-CMStatistics 2024 (London, December 2024). Dynamic mixture of finite mixtures of factor analysers.
• Poster presentation at 2024 ISBA World Meeting (Venice, July 2024). Dynamic mixture of finite mixtures of factor analysers model with automatic inference on number of clusters and factors.
• Contributed talk at EWMES 2023 (Manchester, December 2023). Dynamic mixture of finite mixtures of factor analysers model with automatic inference on number of clusters and factors.
• Invited talk at IDWSDS 2023 (virtual, October 2023). Dynamic mixture of finite mixtures of factor analysers model with automatic inference on number of clusters and factors.
• Contributed talk at ESOBE 2023 (Glasgow, September 2023). Dynamic mixture of finite mixtures of factor analysers model with automatic inference on number of clusters and factors.
• Invited talk at CMStatistics 2022 (London, December 2022). Dynamic mixture of finite mixtures of factor analysers model with automatic inference on number of clusters and factors.
• Talk in young researcher session at ESOBE 2022 (Salzburg, September 2022). Dynamic mixture of finite mixtures of factor analysers model with automatic inference on number of clusters and factors.
• Poster presentation at 2022 ISBA World Meeting (Montreal, June 2022). Mixture of factor analysers and adaptive telescoping sampler for automatic inference on number of clusters and factors.
• Contributed talk at BNP 2022 Networking Event (Nicosia, April 2022). Mixture of factor analysers and adaptive telescoping sampler for automatic inference on number of clusters and factors.
• Seminar at CEBA (virtual, December 2021). Bayesian infinite factor models with non-Gaussian factors.
• Contributed talk at JSM 2021 (virtual, August 2021). Bayesian infinite factor models with non-Gaussian factors.
• Contributed talk at 2021 ISBA World Meeting (virtual, July 2021). Bayesian infinite factor models with non-Gaussian factors.

Selective Work Experience

• 2024-present: Research Associate for Bayesian Statistics and Causal Inference, School of Public Health, Imperial College London (London, UK)
• 2014-2024: Economist/quantitative analyst at Research Department of Erste Group (Vienna, Austria).
• 2007-2011: Data analyst at Strategic Risk Management Department of Erste Group (Vienna, Austria).
• 2006: Researcher at UNIDO, Strategic Research and Economics Branch.
• 2004 - 2005: Lecturer, Saint Petersburg State University, Department of Economics (Saint Petersburg, Russia).

Miscellanea

I love music and have studied classical piano and vocals for some years. I have also participated in several summer courses in jazz and creative music/songwriting at Guildhall School of Music and Drama as well as had some amateur performances at various events.

Contact me

m.grushanina at imperial.ac.uk
margarita.grushanina at gmail.com