M
Ming-Yang Kao
Researcher at Northwestern University
Publications - 202
Citations - 4582
Ming-Yang Kao is an academic researcher from Northwestern University. The author has contributed to research in topics: Time complexity & Planar graph. The author has an hindex of 37, co-authored 202 publications receiving 4438 citations. Previous affiliations of Ming-Yang Kao include Tufts University & Indiana University.
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Proceedings ArticleDOI
Hamsa: fast signature generation for zero-day polymorphic worms with provable attack resilience
TL;DR: Hamsa is proposed, a network-based automated signature generation system for polymorphic worms which is fast, noise-tolerant and attack-resilient, and significantly outperforms Polygraph in terms of efficiency, accuracy, and attack resilience.
Proceedings ArticleDOI
A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry
TL;DR: The de novo peptide sequencing problem is to reconstruct the peptide sequence from a given tandem mass spectral data of k ions by implicitly transforming the spectral data into an NC-spectrum graph G (V, E) where /V/ = 2k + 2, and this approach can be further used to discover a modified amino acid in O(/V//E/) time.
Journal ArticleDOI
Complexities for Generalized Models of Self-Assembly
Gagan Aggarwal,Qi Cheng,Michael H. Goldwasser,Ming-Yang Kao,Pablo Moisset de Espanés,Robert T. Schweller +5 more
TL;DR: In this paper, the authors studied the complexity of tile self-assembly under various generalizations of the tile selfassembly model and provided a lower bound of Ω( √ n 1/k) for the standard model.
Journal ArticleDOI
A dynamic programming approach to de novo peptide sequencing via tandem mass spectrometry.
TL;DR: In this paper, the authors proposed a dynamic programming-based method to reconstruct the peptide sequence from a given tandem mass spectral data of k ions by implicitly transforming the spectral data into an NC-spectrum graph G (V, E).
Journal ArticleDOI
Searching in an Unknown Environment
TL;DR: In this paper, the cow-path problem is studied and the first randomized algorithm for the cow path problem is presented. But the algorithm is optimal for two paths (w = 2) and is not optimal for larger values of w.