If you have trouble accessing any of these manuscripts, please email me asking for a copy.


  1. Aksoy, D., Alben, S., Deegan, R. D., and Gorodetsky, A. A. "Inverse Design of Self-Oscillatory Gels through Deep Learning.", 2021. local copy, supplementary material, code on bitbucket.
  2. Gorodetsky, A. A., Safta, S., and Jakeman, J. D. "Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning. " 2021, local_copy
  3. Pham, T., and Gorodetsky, A. A. "Ensemble approximate control variate estimators: Applications to multi-fidelity importance sampling." 2021, local copy
  4. Gorodetsky, A. A., Jakeman, J. D., and Geraci, G. "MFNets: Learning network representations for multifidelity surrogates." 2020,
  5. De, S., Salehi, H. and Gorodetsky, A., "Efficient MCMC Sampling for Bayesian Matrix Factorization by Breaking Posterior Symmetries." 2020, arXiv preprint, local hosted link
  6. Kachar, K.G., Gorodetsky, A. A., Dynamic Multi-agent assignment via discrete optimal transport. 2020, arXiv preprint local hosted link

Peer-reviewed publications

Journal articles

  1. Galioto, N., Gorodetsky, A.A. "Bayesian system ID: optimal management of parameter, model, and measurement uncertainty." Nonlinear Dyn (2020). ArXiv
  2. Gorodetsky, A. A., Jakeman, J.D., Geraci, G., Eldred, M.S., "MFNETS: multifidelity data-driven networks for Bayesian learning and prediction." International Journal of Uncertainty Quantification, 10, (2021) : 595-622. local copy
  3. Gorodetsky, A. A., Geraci, G., Eldred M. S., Jakeman, J. "A generalized approximate control variate framework for multifidelity uncertainty quantification." Journal of Computational Physics, 408, (2020): 109257. local copy
  4. Jakeman, J., Eldred, M. S., Geraci, G., Gorodetsky, A.A. "Adaptive multi-index collocation for uncertainty quantification and sensitivity analysis." International Journal for Numerical Methods in Engineering, 121 (2019) : 1314 – 1343.
  5. Gorodetsky, A. A., Karaman, S., and Marzouk, Y., "A continuous analogue of the tensor-train decomposition." Computer Methods in Applied Mechanics and Engineering 347 (2019): 59-84.
  6. Alben, S., Gorodetsky, A. A., Kim, D., Deegan, R. D. "Semi-implicit methods for the dynamics of elastic sheets." Journal of Computational Physics, 399 (2019): 108952.
  7. Wildey, T., Gorodetsky, A.A., Belme, A.C., Shadid, J. N., "Robust Uncertainty Quantification using reponse surface approximations of discontinuous functions" International Journal of Uncertainty Quantification, 9:5 (2019): 415-437. local copy
  8. Kramer, B., and Gorodetsky, A. "System identification via CUR-factored Hankel approximation." SIAM Journal on Scientific Computing, 40.2 (2018): A848-A866. local copy
  9. Gorodetsky, A. A., and Jakeman, J. D., "Gradient-based optimization for regression in the functional tensor-train format." Journal of Computational Physics 374 (2018): 1219-1238.
  10. 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.
  11. Gorodetsky, A. A., and Marzouk, Y., "Mercer Kernels and Integrated Variance Experimental Design: Connections Between Gaussian Process Regression and Polynomial Approximation" SIAM/ASA Journal on Uncertainty Quantification (2016): 4:1, 796-828.
  12. Gorodetsky, A. A., and Marzouk, Y., "Efficient Localization of Discontinuities in Complex Computational Simulations," SIAM Journal on Scientific Computing (2014): 36:6, A2584-A2610.

Conference publications

  1. Chen, B., Tandon, S., Gorsich, D., Gorodetsky, A., Veerapaneni, S. "Behavioral Cloning in Atari Games Using a Combined Variational Autoencoder and Predictor Model." IEEE Conference on Evolutionary Computation, Virtual, June 28 - July 1, 2021. local copy
  2. Yang, H. , Gorodetsky, A. A., Fujii, Y., Wang, K-W. "Multifidelity Uncertainty Quantification for Online Simulations of Automotive Propulsion Systems.", Proceedings of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC/CIE2021. Virtual, August 17-20, 2021.
  3. Galioto, N. Gorodetsky, A. A., "A new objective for identification of partially observed linear time-invariant dynamical systems from input-output data." Learning for Dynamics and Control (L4DC), Virtual, June 7-8, 2021. local_copy
  4. Galioto, N. Gorodetsky, A. A., "Bayesian Identification of Hamiltonian Dynamics from Symplectic Data." Conference on Decision and Control (CDC), 2020. local copy
  5. Yang, H. Kidambi, N., Fujii, Y., Gorodetsky, A., Wang, K-W. "Uncertainty Quantification Using Generalized Polynomial Chaos for Online Simulations of Automotive Propulsion Systems." American Control Conference (ACC) 2020. pp. 295-300. IEEE, 2020. local copy
  6. Baskar, D., Gorodetsky, A. A., "Simulated Wind-field Dataset for Testing Energy Efficient Path-Planning Algorithms for UAVs in Urban Environment." 2020 AIAA Aviation Form, 2020.
  7. Whittaker, C. B., Gorodetsky, A., and Jorns, B. "Quantifying Uncertainty in the Scaling Laws of Porous Electrospray Emitters." AIAA Propulsion and Energy 2020 Forum. 2020. local_copy
  8. He, K., Wang, J. and Gorodetsky, A. A., "Uncertainty Analysis of Trajectory Tracking for Autonomous Dynamic Soaring." AIAA Scitech 2020 Forum. 2020. local copy
  9. Jorns, B., Gorodetsky, A., Lasky, I. Kimber, A., Dahl P., St. Peter, B., Dressler, R. "Uncertainty Quantification of Electrospray Thruster Array Lifetime." 36th International Electric Propulsion Conference, University of Vienna, Austria, September 15 – 20, 2019. local copy
  10. Geraci, G., Eldred, M. S., Gorodetsky, A., and Jakeman, J. "Recent advancements in Multilevel-Multifidelity techniques for forward UQ in the DARPA SEQUOIA project." AIAA Scitech Forum January 2019. local copy
  11. Sayre-McCord, R. T., Guerra, W., Antonini, A., Arneberg, J., Brown, A., Cavalheiro,G., Fang, Y., Gorodetsky, A., McCoy, D., Quilter, S., Riether, F., Tal, E., Terzioglu, Y., Carlone, L., Karaman, S. "Visual-inertial navigation algorithm development using photorealistic camera simulation in the loop." Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, 2018.
  12. Geraci, G., Eldred, M.S., Gorodetsky, A. A., Jakeman J. "Leveraging Active Subspaces for Efficient Multifidelity Uncertainty Quantification." ECCM-ECCFD 2018, Glasgow, Scotland, UK 2018. local_copy
  13. Eldred, M. S., Geraci, G., Gorodetsky, A., and Jakeman, J. "Multilevel-Multidelity Approaches for Forward UQ in the DARPA SEQUOIA project." 2018 AIAA Non-Deterministic Approaches Conference. 2018.
  14. Tal, E., Gorodetsky, A. and Karaman, S., 2018, June. Continuous tensor train-based dynamic programming for high-dimensional zero-sum differential games. In 2018 Annual American Control Conference (ACC) (pp. 6086-6093). IEEE. https://10.23919/ACC.2018.8431472
  15. Gorodetsky, A. A., Karaman, S., and Marzouk, Y. M., "Low-rank tensor integration for Gaussian filtering of continuous time nonlinear systems," 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, VIC, 2017, pp. 2789-2794. https://10.1109/CDC.2017.8264064
  16. Alora, J. I., Gorodetsky, A. A., Karaman, S., Marzouk, Y. and Lowry, N., "Automated synthesis of low-rank control systems from sc-LTL specifications using tensor-train decompositions," 2016 IEEE 55th Conference on Decision and Control (CDC), Las Vegas, NV, 2016, pp. 1131-1138,
  17. Gorodetsky, A.A., Karaman, S. and Marzouk, Y.M., 2015, July. Efficient High-Dimensional Stochastic Optimal Motion Control using Tensor-Train Decomposition. In Robotics: Science and Systems.

PhD Thesis

Gorodetsky, Alex Arkady. Continuous low-rank tensor decompositions, with applications to stochastic optimal control and data assimilation. Diss. Massachusetts Institute of Technology, 2017.

S.M Thesis

Gorodetsky, Alex Arkady. A learning method for the approximation of discontinuous functions for stochastic simulations. Diss. Massachusetts Institute of Technology, 2012.

Copyright (c) 2020, Alex Gorodetsky, License: CC BY-SA 4.0