Publications & Manuscripts

Risk-Averse Control Theory & Safety Analysis

  • Wang & Chapman, Risk-averse autonomous systems: A brief history and recent developments from the perspective of optimal control, Journal of Artificial Intelligence, 2022, in press. [PDF]

  • Arsenault, Wang, & Chapman, Toward scalable risk analysis for stochastic systems using extreme value theory, IEEE Control Systems Letters, 2022, in press. [PDF]

  • Wei, Fauss, & Chapman, CVaR-based safety analysis in the infinite time horizon setting, American Control Conference, 2022. [PDF]

  • Chapman, Fauss, & Smith, On optimizing the Conditional Value-at-Risk of a maximum cost for risk-averse safety analysis, conditionally accepted by IEEE Transactions on Automatic Control, May 2022. [Extended Version]

  • Smith & Chapman, On Exponential Utility and Conditional Value-at-Risk as risk-averse performance criteria, under review for IEEE Transactions on Control Systems Technology. [PDF]

  • Chapman & Smith, Classical risk-averse control for a finite-horizon Borel model, IEEE Control Systems Letters, 2021.* [PDF]

  • Chapman & Lessard, Toward a scalable upper bound for a CVaR-LQ problem, IEEE Control Systems Letters, 2021.* [PDF]

  • Chapman, Bonalli, Smith, Yang, Pavone, & Tomlin, Risk-sensitive safety analysis using Conditional Value-at-Risk, IEEE Transactions on Automatic Control, 2022, in press. [PDF]

  • Chapman, Lacotte, Tamar, Lee, Smith, Cheng, Fisac, Jha, Pavone, & Tomlin, A risk-sensitive finite-time reachability approach for safety of stochastic dynamic systems, American Control Conference, 2019. [PDF] [Extended Version]

Modeling & Control of Cancer Systems

  • Chapman, Jensen, Chan, & Lessard, Information-theoretic multi-time-scale partially observable systems with relevance to leukemia treatment, under review for Automatica. [PDF]

  • Wiggert, Turnidge, Cohen, Langer, Sears, Chapman, & Tomlin, Data-driven identification of cancer cell population dynamics leveraging the effect of pre-treatment for drug schedule design, American Control Conference, 2021. [PDF]

  • Chapman, Risom, Aswani, Langer, Sears, & Tomlin, Modeling differentiation-state transitions linked to therapeutic escape in triple-negative breast cancer, PLoS Computational Biology, 2019. [PDF] [CODE]

  • Risom, Langer, Chapman#, Rantala#, Fields, Boniface, Alvarez, Kendsersky, Pelz, Johnson-Camacho, Dobrolecki, Chin, Aswani, Wang, Califano, Lewis, Tomlin, Spellman, Adey, Gray, & Sears, Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer, Nature Communications, 2018. #equal [PDF] [CODE]

  • Chapman, Mazumdar, Langer, Sears, & Tomlin, On the analysis of cyclic drug schedules for cancer treatment using switched dynamical systems, IEEE Conference on Decision and Control, 2018. [PDF] [CODE]

  • Chapman, Risom, Aswani, Dobbe, Sears, & Tomlin, A model of phenotypic state dynamics initiates a promising approach to control heterogeneous malignant cell populations, IEEE Conference on Decision and Control, 2016. [PDF] [CODE]

Additional Research

  • Yeh & Chapman, A non-linear differentiable model for stormwater-based irrigation of a green roof in Toronto, IEEE Conference on Technologies for Sustainability, 2022. [PDF]

  • Chapman, Smith, Cheng, Freyberg, & Tomlin, Reachability analysis as a design tool for stormwater systems, IEEE Conference on Technologies for Sustainability, 2018. [PDF] [CODE]

  • Fridovich-Keil, Hanford, Chapman, Tomlin, Farrens, & Ghosal, A model predictive control approach to flow pacing for TCP, Allerton Conference on Communication, Control, and Computing, 2017. [PDF]

  • Chapman, Rotella, & Okamura, Position and velocity cursor mappings contribute to distinct muscle forces in simulated isometric and movement reaching, IEEE Conference on Biomedical Robotics and Biomechatronics, 2014. [PDF]


  • Chapman & Smith 2021 [PDF], Chapman & Lessard 2021 [PDF]: The published articles omit almost-everywhere notions in statements about conditional expectations. Revised articles (see previous links) make such notions explicit.

  • A preliminary version of risk-averse safety analysis is described in Chapter 3 of Chapman, Risk-sensitive safety analysis and control for trustworthy autonomy, Ph.D. thesis, UC Berkeley, 2020. The theory of risk-averse safety analysis has been significantly revised and improved in subsequent works, e.g., see Chapman, Bonalli, et al. 2022 [PDF] and Chapman, Fauss, & Smith 2022 [PDF].