Alex Gorodetsky
Ann Arbor, Michigan, United States
Summary
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SeniorAlex Gorodetsky is an associate professor and computational scientist who develops scalable algorithms for reasoning under uncertainty, with applications spanning autonomous vehicles, time-series analysis, and physics-informed data-driven models. With 11 years of academic and industry experience and a PhD from MIT, he blends Bayesian statistics, probabilistic modeling, and numerical analysis to tackle stochastic control and sequential decision-making problems. At the University of Michigan he progressed from assistant to associate professor while also advising industry as Chief AI Scientist at Geminus, reflecting a rare mix of theory and applied impact. His background includes a John von Neumann fellowship at Sandia and hands-on robotics and real-time systems work at MIT, showing a track record of translating methods into operational systems. Notably, he pairs deep mathematical rigor with practical deployment experience in safety-critical domains, making him adept at bridging research and production. Based in Ann Arbor, he maintains an active research portfolio focused on scalable Bayesian inference and reinforcement learning for complex dynamical systems.
11 years of coding experience
Github Skills (28)
multidimensional10
vectorization9
matrix-factorization9
syntax-highlighting9
gaussian8
opencl8
julia8
matrix8
api8
matrix-multiplication8
java8
gpgpu8
markov-chain7
gpu7
clojure7
Programming languages (4)
Github contributions (5)
goroda/cdyn
Sep 2016 - Jun 2020
Contributions:2 PRs, 11 pushes, 3 branches in 3 years 10 months
dynamical-systemsdynamical
goroda/GPEXP
Feb 2015 - Dec 2021
Contributions:1 PR, 21 pushes, 1 branch in 6 years 11 months
regressionpythongaussiangaussian-process-regressiongaussian-process
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