Many real-world phenomena in fields such as biology, economics, and social sciences
can be modeled and predicted using probabilistic and statistical methodologies.
Our research focus is on developing robust models and reliable estimation techniques. We create techniques for data analysis, hypothesis testing, and predictive modeling. This includes exploring patterns, identifying relationships, and assessing the reliability of conclusions. Statistical methods are applied across a wide range of fields, from scientific research to business analytics, helping to make sense of complex data and inform decision-making in various industries.
- Robert Mnatsakanov: Statistical Inverse Problems, The Hausdorff and Stieltjes Moment Problems, Applications of Statistical Methods in Actuarial and Financial Mathematics
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Mihyun Kim: Functional Data Analysis, Functional Time Series, Extreme
Value Analysis
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Kenneth Ryan: Statistical Machine Learning and Experimental Design
- Youngseok Song: Graphical Models, Large-Scale Inference, Statistical Network Analysis, Robust Learning