Alex Gorodetsky Midas

Alex Gorodetsky Midas Alex gorodetsky’s research is at the intersection of applied mathematics, data science, and computational science, and is focused on enabling autonomous decision making under uncertainty. We develop approaches to automate reasoning and decision making in the presence of uncertainty. our approaches focus on foundational algorithm development — we seek to create scalable, general, and adaptive algorithms that can intelligently work across many different application regimes.

Alex Gorodetsky Michigan Institute For Computational Discovery And Efficient high dimensional stochastic optimal motion control using tensor train decomposition. 6th european conference on computational mechanics—7th european conference …. In this paper, we present an adaptive algorithm to construct response surface approximations of high‐fidelity models using a hierarchy of lower fidelity models. We propose an optimal solution to a deterministic dynamic assignment problem by leveraging connections to the theory of discrete optimal transport to convert the combinatorial assignment problem. Alex gorodetsky is an associate professor of aerospace engineering at the university of michigan. his research interests include using applied mathematics and computational science to enhance autonomous decision making under uncertainty and for developing new tools for high fidelity simulation.

Alex Gorodetsky Michigan Aerospace Engineering We propose an optimal solution to a deterministic dynamic assignment problem by leveraging connections to the theory of discrete optimal transport to convert the combinatorial assignment problem. Alex gorodetsky is an associate professor of aerospace engineering at the university of michigan. his research interests include using applied mathematics and computational science to enhance autonomous decision making under uncertainty and for developing new tools for high fidelity simulation. Leveraging evidential regression to predict epistemic and aleatoric uncertainties, we demonstrate a median 0.996 correlation between network prediction error and these uncertainty estimates on keypoint coordinates within synthetic images. Gorodetsky a. a., karaman, s., and marzouk y.m. "high dimensional stochastic optimal control using continuous tensor decompositions." international journal of robotics research, 37.2 3 (2018): 340 377. doi.org 10.1177 0278364917753994. Alex completed his ph.d. (2016) and s.m. (2012) in the department of aeronautics and astronautics at the massachusetts institute of technology, where he worked on algorithms for stochastic optimal control and estimation in dynamical systems. Alex gorodetsky is an assistant professor in the department of aerospace engineering. his research includes using applied mathematics and computational science to enhance autonomous decision making under uncertainty.

Alex Gorodetsky Michigan Aerospace Engineering Leveraging evidential regression to predict epistemic and aleatoric uncertainties, we demonstrate a median 0.996 correlation between network prediction error and these uncertainty estimates on keypoint coordinates within synthetic images. Gorodetsky a. a., karaman, s., and marzouk y.m. "high dimensional stochastic optimal control using continuous tensor decompositions." international journal of robotics research, 37.2 3 (2018): 340 377. doi.org 10.1177 0278364917753994. Alex completed his ph.d. (2016) and s.m. (2012) in the department of aeronautics and astronautics at the massachusetts institute of technology, where he worked on algorithms for stochastic optimal control and estimation in dynamical systems. Alex gorodetsky is an assistant professor in the department of aerospace engineering. his research includes using applied mathematics and computational science to enhance autonomous decision making under uncertainty.