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Adam Thorpe

I am a postdoctoral researcher at the Oden Institute for Computational Engineering & Science at the University of Texas at Austin, working with Ufuk Topcu. I received my Ph.D. in 2023 from the University of New Mexico, advised by Meeko Oishi. I am broadly interested in topics related to data-driven methods for control, computational engineering, and human-centered autonomy.

adam.thorpe@ignore.meaustin.utexas.edu

Scholar GitHub CV

Research

I develop theory and algorithms for autonomy, robotics, control, and engineering that are adaptable, principled, and reliable. My research provides data-driven control algorithms based in the theory of Hilbert spaces that have quantifiable guarantees. By integrating neural network architectures with structured Hilbert space representations, I have developed principled methods for operator learning and techniques for zero-shot modeling of dynamical systems that are adaptable to new systems using only a few seconds of online data. Using statistical approaches rooted in functional analysis, I developed techniques for characterizing human-autonomy interactions to ultimately design autonomy that is responsive to individual needs and preferences.

Data-Driven Control

I developed algorithms using kernel methods to solve approximate reformulations of stochastic optimal control problems that (i) can be solved as a linear program (LP), (ii) can handle non-Gaussian disturbances, (iii) are computationally efficient, and (iv) admit finite-sample guarantees.

My representative research in this area is comprised of data-driven algorithms for solving chance-constrained control problems, dynamic programs, and stochastic reachability problems with finite-sample guarantees. These algorithms reformulate stochastic optimal control problems using kernel-based estimates of stochastic operators. By representing these operators in Hilbert space, I demonstrated that these problems can be solved efficiently as an LP, and provided solutions for mixed stochastic policies.

Computational Engineering

I have developed novel methods for Basis-to-Basis (B2B) operator learning. Basis-to-Basis operator learning uses function encoders to learn a basis for the domain and codomain of an operator on Hilbert spaces, and then independently learns a mapping between the learned spaces to approximate the operator.

B2B demonstrates an order of magnitude improvement over existing approaches on several benchmark PDE and operator learning tasks, and overcomes a key challenge faced by existing approaches, which is the need for a fixed input grid or data locations. Operator learning is a new frontier in machine learning, and I am interested in developing these new methods for operator learning, with a particular interest in applications to robtics, perception, and control.

Human-Centered Autonomy

I have developed methods for characterizing human behavior in the context of human-autonomy interaction. Using statistical techniques, I have developed methods that enable the quantification of human behavior for the purpose of designing autonomy that is responsive to individual needs and preferences.

My research in this area highlights several key insights for human-centered autonomy, such as the fact that there is no universal model of human behavior, meaning that autonomy must be designed for individuals, not populations. I am interested in developing new tools and methodologies for autonomy that can learn and adapt on the fly, without the need for extensive training data, ultimately leading to autonomy that is responsive to individual needs and preferences.

Journal Articles

  1. T. Ingebrand, A. J. Thorpe, S. Goswami, K. Kumar, and U. Topcu, “Basis-to-basis operator learning using function encoders,” Computer Methods in Applied Mechanics and Engineering, 2024, (Submitted).
    Paper Code Project Page
  2. A. J. Thorpe, F. Djeumou, C. Neary, M. M. K. Oishi, and U. Topcu, “Physics-informed kernel embeddings: A unified approach to integrating prior knowledge of dynamics and system properties,” Transactions on Automatic Control, 2024, (Submitted).
  3. H. Sridhar, G. Huang, A. Thorpe, M. Oishi, and P. J. Brandon, “Characterizing the effect of mind wandering on braking dynamics in partially autonomous vehicles,” Transactions on Cyber-Physical Systems, 2023.
    Paper
  4. A. J. Thorpe, K. R. Ortiz, and M. M. K. Oishi, “State-based confidence bounds for data-driven stochastic reachability using Hilbert space embeddings,” Automatica, vol. 138, p. 110 146, 2022.
    Paper
  5. A. P. Vinod, A. J. Thorpe, P. A. Olaniyi, T. H. Summers, and M. M. K. Oishi, “Sensor selection for dynamics-driven user-interface design,” IEEE Transactions on Control Systems Technology, vol. 30, no. 1, pp. 71–84, 2022.
    Paper
  6. A. J. Thorpe and M. M. K. Oishi, “Model-free stochastic reachability using kernel distribution embeddings,” IEEE Control Systems Letters, vol. 4, no. 2, pp. 512–517, 2020.
    Paper

Conference Papers

  1. A. J. Thorpe, T. Ingebrand, S. Goswami, K. Kumar, and U. Topcu, “Basis-to-basis operator learning: A paradigm for scalable and interpretable operator learning on Hilbert spaces,” in 2025 SIAM Conference on Computational Science and Engineering, (Submitted), 2025.
    Code Project Page
  2. T. Ingebrand, A. J. Thorpe, and U. Topcu, “Zero-shot transfer of neural ODEs,” in Advances in Neural Information Processing Systems, 2024.
    Paper Code Project Page
  3. Y. Yu, A. J. Thorpe, J. Milzman, D. Fridovich-Keil, and U. Topcu, “Sensing resource allocation against data-poisoning attacks in traffic routing,” in 2024 63rd IEEE Conference on Decision and Control, 2024.
    Paper
  4. K. Ortiz, R. DiPirro, A. Thorpe, and M. Oishi, “Online learning of dynamical systems using low-rank updates to physics-informed kernel distribution embeddings,” in 2024 IEEE 63rd Conference on Decision and Control (CDC), Milan, Italy, 2024.
  5. M. S. Yuh, E. Rabb, A. Thorpe and N. Jain, "Using Reward Shaping to Train Cognitive-Based Control Policies for Intelligent Tutoring Systems," 2024 American Control Conference (ACC), Toronto, ON, Canada, 2024.
    Paper
  6. A. J. Thorpe, F. Djeumou, C. Neary, M. M. K. Oishi, and U. Topcu, “Physics-informed kernel embeddings: Integrating prior system knowledge with data-driven control,” in 2024 American Control Conference (ACC), 2024.
    Paper
  7. K. R. Ortiz, J. G. Hunter, A. J. Thorpe, et al., “Assessing the relationship between learning stages and prefrontal cortex activation in a psychomotor task,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2024.
    Paper
  8. K. S. Miller, A. J. Thorpe, and U. Topcu, “Active learning of dynamics using prior domain knowledge in the sampling process,” in 2024 63rd IEEE Conference on Decision and Control, 2024.
    Paper
  9. H. I. Khan, A. J. Thorpe, and D. Fridovich-Keil, “Act natural! projecting autonomous system trajectories into naturalistic behavior sets,” in IFAC Workshop on Cyber-Physical & Human Systems, 2024.
    Paper
  10. A. Thorpe, T. Lew, M. Oishi, and M. Pavone, “Data-driven chance constrained control using kernel distribution embeddings,” in Proceedings of The 4th Annual Learning for Dynamics and Control Conference, vol. 168, PMLR, 23–24 Jun 2022, pp. 790–802.
    Paper
  11. K. R. Ortiz, A. J. Thorpe, A. Perez, M. Luster, B. J. Pitts, and M. Oishi, “Characterizing within-driver variability in driving dynamics during obstacle avoidance maneuvers,” in IFAC Workshop on Cyber-Physical & Human Systems, 2022.
    Paper
  12. A. J. Thorpe and M. M. K. Oishi, “SOCKS: A stochastic optimal control and reachability toolbox using kernel methods,” in 25th ACM International Conference on Hybrid Systems: Computation and Control, ser. HSCC ’22, Milan, Italy: Association for Computing Machinery, 2022.
    Paper Code Project Page Video
  13. A. J. Thorpe, V. Sivaramakrishnan, and M. M. K. Oishi, “Approximate stochastic reachability for high dimensional systems,” in 2021 American Control Conference, 2021, pp. 1287–1293.
    Paper
  14. A. J. Thorpe, K. R. Ortiz, and M. M. K. Oishi, “SReachTools kernel module: Data-driven stochastic reachability using Hilbert space embeddings of distributions,” in 2021 60th IEEE Conference on Decision and Control, 2021, pp. 5073–5079.
    Paper
  15. A. J. Thorpe, K. R. Ortiz, and M. M. K. Oishi, “Learning approximate forward reachable sets using separating kernels,” in Proceedings of the 3rd Conference on Learning for Dynamics and Control, vol. 144, PMLR, Jul. 2021, pp. 201–212.
    Paper
  16. A. J. Thorpe and M. M. K. Oishi, “Stochastic optimal control via Hilbert space embeddings of distributions,” in 2021 60th IEEE Conference on Decision and Control, 2021, pp. 904–911.
    Paper
  17. A. Abate, H. Blom, M. Bouissou, et al., “ARCH-COMP21 category report: Stochastic models,” in 8th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH21), ser. EPiC Series in Computing, United Kingdom, Dec. 2021, pp. 55–89.
    Paper
  18. A. J. Thorpe, J. A. Gonzales, and M. M. K. Oishi, “Data-driven stochastic optimal control using kernel gradients,” in 2023 American Control Conference (ACC), 2023, pp. 2548–2553.
    Paper
  19. A. J. Thorpe, “Refining human-centered autonomy using side information,” in 14th ACM/IEEE International Conference on Cyber-Physical Systems, Humans in Cyber-Physical Systems Workshop, 2023.
    Paper
  20. A. J. Thorpe, C. L. Arrington, and J. R. Pillars, “Additively manufactured electrochemical plating enclosures,” in ECS Meeting Abstracts, IOP Publishing, 2018, p. 857.

Talks

  1. A. J. Thorpe, “Data-Driven Stochastic Optimal Control Using Hilbert Space Embeddings of Distributions,” Dissertation Defense, University of New Mexico, 2023
    Paper Slides
  2. A. J. Thorpe, “Stochastic Optimal Control & Safety Via Kernel Embeddings,” University of Texas at Austin, 2022
    Slides
  3. A. J. Thorpe, “Stochastic Optimal Control & Safety Via Kernel Embeddings: A Data-Driven Approach,” Weierstrass Institute for Applied Analysis and Stochastics in Berlin, Germany, 2022
    Slides
  4. A. J. Thorpe, “LP Solutions for Stochastic Optimal Control Problems via Hilbert Space Embeddings of Distributions,” NASA ULI Seminar Series, Stanford University, 2022
    Video Slides