Data analytics
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Data analytics

Prof. Dr. habil. Rouven E. Haschka

Dr. Samyajoy Pal

Research Associate

Dr. Samyajoy Pal is currently a Postdoctoral Research Associate at the Chair of Data Analytics of the Faculty of Business Studies and Economics at RPTU Kaiserslautern-Landau.

She received her doctorate in statistics from the Department of Statistics at LMU Munich. His research lies at the intersection of statistics, machine learning and artificial intelligence, with a focus on the methodological and theoretical foundations of statistical learning methods.

Central areas of work are flexible multivariate mixed models and their application in unsupervised learning tasks such as model-based clustering and classification. Further research focuses on the analysis of compositional and high-dimensional data. The current focus is on advanced statistical and econometric models for the treatment of endogenous structures in economic data using multivariate distributions and mixed models, with the aim of theoretically sound and interpretable AI.

Research interests

Multivariate Mixture Models, Model-Based Clustering, Compositional Data Analysis, High-Dimensional Data, Computational Statistics, Statistical Learning, Statistical Inference

Further information

  • Pal, S. (2025). Advances in finite mixture models with applications to unsupervised learning. PhD Dissertation, LMU Munich.

  • Pal, S., & Heumann, C. (2025). Revisiting Dirichlet Mixture Model: unraveling deeper insights and practical applications. Statistical Papers, 66 (1), 2.

  • Pal, S., & Heumann, C. (2024). Flexible Multivariate Mixture Models: A Comprehensive Approach for Modeling Mixtures of Non-Identical Distributions. International Statistical Review.

  • Pal, S., & Heumann, C. (2024). Gene coexpression analysis with Dirichlet mixture model: accelerating model evaluation through closed-form KL divergence approximation using variational techniques. In: International Workshop on Statistical ModelingSpringer Nature Switzerland, 134-141.

  • Pal, S., & Heumann, C. (2024). Gaussian mixture model with modified hard EM algorithm in clustering problems. In: Statistical Modeling and Applications on Real-Time ProblemsCRC Press, 153-179.

  • Pal, S., & Heumann, C. (2022). Clustering compositional data using Dirichlet mixture model. PLOS One, 17 (5), e0268438.