The merger of statistics and informatics inside the GSEM recognizes that statistics, to be effective in practice, cannot live without informatics, and informatics, to go beyond information or say, data management with some descriptive statistics, needs to get enriched by sound data analytical methods. The modern conception of data science as a discipline is often attributed to William S. Cleveland, a Professor of Statistics and Computer Science at Purdue University. In its original definition, statistics and informatics are the fundamental pillars on which data science is based.

Members of the Institute publish scientific work in top journals that range from information systems to fundamental statistics and have a special focus on applied research areas such as environmental sciences, financial econometrics, health, engineering, psychology, etc.

Regarding statistics, our researchers have expertise in robust inference, extreme events, small sample inference, indirect inference, non-parametric statistics, model selection, time series analysis, latent variable and mixture models, non-Euclidian and functional data analysis, etc.

Our researchers more tailored to informatics are strongly involved in industrial technology transfer projects in domains as diverse as formal models of information visualization in 3D virtual environments, services innovation, large-scale services, indoor positioning and navigation systems, services for seniors, mobile sensors: smartphone, smartwatch, wristband, and other wearables, and algebraic operations for the management of knowledge resources.

The Institute's strong involvement in interdisciplinary think groups places it at the forefront of the technology watch in Information Science and various areas of Statistics in Switzerland.


RECENT Publications

Guerrier, S., Kuzmics, C., & Victoria-Feser, M.-P. 2024. Assessing COVID-19 Prevalence in Austria with Infection Surveys and Case Count Data as Auxiliary Information. Journal of the American Statistical Association.

Deuber D., Li J., Engelke, S., & Maathuis M. 2023. Estimation and Inference of Extremal Quantile Treatment Effects for Heavy-Tailed Distributions. Journal of the American Statistical Association.

Jiang, C.La Vecchia, D.Ronchetti, E., & Scaillet, O. 2023. Saddlepoint approximations for spatial panel data models. Journal of the American Statistical Association, 118(542), 11641175.

La Vecchia, D., Moor, A., & Scaillet, O. 2023. A higher-order correct fast moving-average bootstrap for dependent data. Journal of Econometrics, 235(1), 6581.

Reluga, K., Lombardía, M.-J., & Sperlich, S. 2023. Simultaneous inference for empirical best predictors with a poverty study in small areas. Journal of the American Statistical Association118(541), 583–595.

Röttger, F., Engelke, S., & Zwiernik, P. 2023. Total positivity in multivariate extremes. The Annals of Statistics51(3), 9621004.

Engelke, S., & Volgushev, S. 2022. Structure learning for extremal tree models. Journal of the Royal Statistical Society Series B: Statistical Methodology84(5), 2055–2087.

Hallin, M., La Vecchia, D., & Liu, H. 2022. Center-Outward R-Estimation for Semiparametric VARMA Models. Journal of the American Statistical Association, 117(538), 925938.

Lideikyte-Huber, G., & Pittavino, M. 2022. Who donates and how? New evidence on the tax incentives in the canton of Geneva, Switzerland. Journal of Empirical Legal Studies19(3), 758–797.

Zakeri, S., Chatterjee, P., Cheikhrouhou, N., & Konstantas, D. (2022). Ranking based on optimal points and win-loss-draw multi-criteria decision-making with application to supplier evaluation problem. Expert Systems with Applications, 191, Article 116258.


> For a complete list, please visit our Knowledge & Publications page.


Recent Ph.D. Theses

Ph.D. in Statistics

Contributions to the Statistical Analysis of Networks and Graphs (Miglioli, C. 2024)

Indirect Estimators and Computational Methods for Models with Unobserved Variables in High Dimensions (Blanc, G. 2023)

Robustness in models for categorical variables (Miron, J. 2023)

Quantitative methods for non-linear models (Shan, J. 2023)

Causal Inference for Extremes (Gnecco, N. 2022)

Contributions to higher-order correct and robust inference for dependent data (Moor, A. 2022)

Domain-Tailored Approaches to Statistical Learning (Bakalli, G. 2021)

Contributions to high-dimensional and semiparametric statistics for dependent data (Bodelet, J. 2021)

Statistical Inference on Network Data: Spatial Panel and Latent Variables (Jiang, C. 2021)

Topics in Statistics and Financial Econometrics: Penalized Estimators and Stochastic Discount Factors (Quaini, A. 2021)

Rare Events, Data Science and Climate Modeling (Vignotto, E. 2021)

Contributions to time series analysis (Xu, H. 2021)

Simultaneous and post-selection inference for mixed parameters (Reluga, K. 2020)

> Click here for more information on the Ph.D. in Statistics program.


Ph.D. in Information Systems

Predicting Ocular Exposure to Natural and Artificial Light by Means of Numerical Simulations (Marro, M. 2024)

Holistic Risk Assessment based on continuous data from the user's behavior and environment (Carrodano Tarantino, C. 2024)

Predicate Extraction as a Generic approach to address different Artificial Intelligence tasks, application to NLP and Computer Vision tasks (Ghadfi, S. 2024)

The Theory of Everything: A Model That Provides a Unified Solution for Dealing with Uncertainty in Solving MCDM Problems (Zakeri, S. 2024)

Context-aware Mobile Internet Quality Model: Quantifying and Facilitating Smartphone's Quality of Experience (De Masi, A. 2023)

Automated Risk Assessment for Cyber Threats Identification in IoT Environments (Collen, A. 2022)

Personalized, narrative and interactive simulation based on a rules-engine system designed to confront caregivers with personalized virtual Alzheimer's patients and to train their communicative coping strategy skills (Chauveau, L. 2020)

Self-monitoring technologies to promote healthy behavior in the long term (Randriambelonoro, M. M. 2020)

> Click here for more information on the Ph.D. in Information Systems program.

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