M
My T. Thai
Researcher at University of Florida
Publications - 283
Citations - 8247
My T. Thai is an academic researcher from University of Florida. The author has contributed to research in topics: Approximation algorithm & Computer science. The author has an hindex of 42, co-authored 252 publications receiving 7084 citations. Previous affiliations of My T. Thai include Kyung Hee University & University of Arkansas.
Papers
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Journal ArticleDOI
Construction of d(H)-disjunct matrix for group testing in hypergraphs
TL;DR: This paper presents a general construction for constructions of nonadaptive algorithms for group testing in hypergraphs, and shows how this problem has been found to have many applications in molecular biology.
Proceedings ArticleDOI
Rate alteration attacks in smart grid
TL;DR: This paper studies the problem of rate alteration attack through fabrication of price messages which induces changes in load profiles of individual users and eventually causes major alteration in the load profile of the entire network and proves that the problem is NP-Complete and provides its inapproximability.
Proceedings Article
Scalable Differential Privacy with Certified Robustness in Adversarial Learning
TL;DR: A scalable algorithm to preserve differential privacy (DP) in adversarial learning for deep neural networks (DNNs), with certified robustness to adversarial examples, is developed by leveraging the sequential composition theory in DP.
Journal ArticleDOI
Catastrophic cascading failures in power networks
TL;DR: This paper designs a new metric to evaluate the importance of nodes in the network and uses it as the base to design the Fully Adaptive Cascading Potential algorithm, and proposes an alternative algorithm, the Cooperating Attack algorithm, which includes several novel properties to solve the problem.
Journal ArticleDOI
Parking Assignment: Minimizing Parking Expenses and Balancing Parking Demand Among Multiple Parking Lots
TL;DR: A new method that considers both of minimizing parking expenses and balancing parking demand is proposed, and the ADMM-based algorithm outperforms the matching- based algorithm and the greedy algorithm in terms of the balancing parkingDemand and reducing parking expenses.