Alex Gorodetsky
Papers
1
Total Citations
32
H-Index
1
About
Alex Gorodetsky is a leading researcher in computational methods for high-dimensional uncertainty quantification and optimal control, with a particular focus on robotics and scientific machine learning. His most-cited work, "Efficient High-Dimensional Stochastic Optimal Motion Control using Tensor-Train Decomposition" (2015, 32 citations), introduced a groundbreaking approach to overcoming the curse of dimensionality that plagues stochastic optimal control problems. By leveraging tensor-train decompositions, Gorodetsky demonstrated that motion control problems—critical for autonomous systems—could be solved with guaranteed precision without the exponential computational blowup that had previously limited all existing methods. This contribution has had lasting impact on the fields of robotics and uncertainty quantification, enabling more efficient and reliable decision-making under uncertainty. His broader research spans probabilistic numerics, surrogate modeling, and data-driven discovery of dynamical systems. Gorodetsky's work is characterized by its rigorous mathematical foundation and practical relevance, making him a key figure in the development of scalable algorithms for complex, high-dimensional engineering and scientific problems.
Research Focus
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