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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Exact Convex Relaxation of Optimal Power Flow in Tree Networks
TL;DR: In this paper, the authors study the optimal power flow problem in tree networks and prove that after a small modification to the OPF problem, its global optimum can be recovered via a second-order cone programming (SOCP) relaxation, under a "mild" condition that can be checked apriori.
Book ChapterDOI
Maximum Realizability for Linear Temporal Logic Specifications
TL;DR: In this article, the authors study the synthesis problem in settings where the overall specification is unrealizable, more precisely, when some of the desirable properties have to be (temporarily) violated in order to satisfy the system's objective.
Journal Article
Class-Aware Generative Adversarial Transformers for Medical Image Segmentation
Chenyu You,Ruihan Zhao,Fenglin Liu,Sandeep Chinchali,Ufuk Topcu,Lawrence H. Staib,James S. Duncan +6 more
TL;DR: CASTformer is presented, a novel type of generative adversarial transformers, for 2D medical image segmentation that takes advantage of the pyramid structure to construct multi- scale representations and handle multi-scale variations and designs a novel class-aware transformer module to better learn the discriminative regions of objects with semantic structures.
Journal ArticleDOI
Control-Oriented Learning on the Fly
TL;DR: An algorithm is proposed that uses small perturbations in the control effort to learn system dynamics around the current system state, while ensuring that the system moves in a nearly optimal direction, and provide bounds for its suboptimality.
Posted Content
Optimal control in Markov decision processes via distributed optimization
Jie Fu,Shuo Han,Ufuk Topcu +2 more
TL;DR: In this article, a decomposition-based distributed synthesis algorithm is proposed to solve the problem of optimal control synthesis in stochastic systems with respect to quantitative temporal logic constraints, which can be solved through distributed optimization methods.