Difference between revisions of "Research Statement"
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| − | My research focuses on the problems of model selection in Machine Learning. It explores methods of Applied Mathematics and Computer Science. The central problem is selecting the simplest structure’s most accurate and robust forecasting model. To define the algebraic structure of this set according to the application and the origin of data, I use various tools: from linear and tensor algebras to differential and geometric algebras. I use multivariate statistics, | + | My research focuses on the problems of model selection in Machine Learning. It explores methods of Applied Mathematics and Computer Science. The central problem is selecting the simplest structure’s most accurate and robust forecasting model. To define the algebraic structure of this set according to the application and the origin of data, I use various tools: from linear and tensor algebras to differential and geometric algebras. I use multivariate statistics, Bayesian inference, and graph probability to induce the criteria of model selection. My work joins theory and practical applications. I believe multi-model decoding problems for heterogeneous data are the most promising. Forecasting the variable of a complex structure requires several models to recover dependencies in source and target spaces and to settle the forecast. The examples to investigate are various spatial-time measurements. |
Revision as of 03:49, 7 October 2022
Vadim, 2023
My research focuses on the problems of model selection in Machine Learning. It explores methods of Applied Mathematics and Computer Science. The central problem is selecting the simplest structure’s most accurate and robust forecasting model. To define the algebraic structure of this set according to the application and the origin of data, I use various tools: from linear and tensor algebras to differential and geometric algebras. I use multivariate statistics, Bayesian inference, and graph probability to induce the criteria of model selection. My work joins theory and practical applications. I believe multi-model decoding problems for heterogeneous data are the most promising. Forecasting the variable of a complex structure requires several models to recover dependencies in source and target spaces and to settle the forecast. The examples to investigate are various spatial-time measurements.