U
Ufuk Topcu
Researcher at University of Texas at Austin
Publications - 504
Citations - 11791
Ufuk Topcu is an academic researcher from University of Texas at Austin. The author has contributed to research in topics: Markov decision process & Computer science. The author has an hindex of 44, co-authored 437 publications receiving 9636 citations. Previous affiliations of Ufuk Topcu include Google & University of Illinois at Urbana–Champaign.
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Active Sampling-based Binary Verification of Dynamical Systems
TL;DR: This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets.
Proceedings ArticleDOI
Compositional analysis of autocatalytic networks in biology
TL;DR: The effects of parameter variations on the stability properties of autocatalytic pathway models and the extent of the regions of attraction around nominal operating conditions are studied.
Journal ArticleDOI
Control Software Synthesis and Validation for a Vehicular Electric Power Distribution Testbed
TL;DR: A design workflow is discussed that aims to transition from the traditional "design and verify" approach to a “specify and synthesize” approach in the context of reconfiguration of the networks in reaction to the changes in their operating environment.
Journal Article
No-Regret Learning in Dynamic Stackelberg Games
TL;DR: This work designs a no-regret learning algorithm that, with high probability, achieves a regret bound (when compared to the best policy in hindsight) which is sublinear in the number of time steps; the degree of sublinearity depends on thenumber of features representing the follower’s utility function.
Proceedings ArticleDOI
On Submodularity of Quadratic Observation Selection in Constrained Networked Sensing Systems
TL;DR: The problem of observation selection in a resource-constrained networked sensing system, where the objective is to select a small subset of observations from a large network to perform a state estimation task, is studied and new optimality criteria are derived under certain conditions.