Jung Jin Cho, ISEN Ph.D. Candidate
Contact: Email - Webpage
Advisor: Dr. Yu Ding
Contact: Email - Webpage

"On the robustness of clustered sensor network"
Short Bio:
Jung Jin Cho is a PhD candidate in the Department of Industrial and Systems Engineering at Texas A&M University. His primary research interest lies in retrieving pertinent information accurately and reliably in a data-rich environment using tools from robust statistics, quality & reliability engineering, and discrete mathematics. The current research focuses on the robust and reliable operations of distributed sensor networks, applications of which include wireless ad-hoc sensor networks, distributed sensor systems in manufacturing assembly processes, and surveillance sensor networks.

Seminar Abstract:
Recent innovations in sensor technology make it possible to deploy large number of sensors to monitor multiple target signals. As the number of deployed sensors gets large, a massive amount of information about the monitored targets becomes available in real-time. One critical issue in the reliable operation of a distributed sensor network is its capability of functioning properly in the presence of sensor anomalies; the redundant information from networked sensors gives a chance to eliminate sensor anomalies. We focuses on identifying the redundancy degree of sensor measurements and subsequently quantifying the robustness of a sensor network. In the presentation, we will address two issues regarding the robustness of sensor networks: 1) redundancy and robustness measure of a sensor network; 2) a robust procedure, which is less sensitive to sensor anomalies, to estimate the status of monitored targets. In our study, a particular emphasis is put on the sensor network that has a cluster structure in its configuration.

Balabhaskar (Baski) Balasundaram, ISEN Ph.D. Candidate
Contact: Email - Webpage
Advisor: Dr. Sergiy Butenko
Contact: Email - Webpage

"The Maximum k-Plex Problem"
Short Bio:
Balabhaskar (Baski) Balasundaram received his Bachelor of Technology degree in Mechanical Engineering from the Indian Institute of Technology-Madras, India. He is currently a Ph. D. candidate in Industrial and Systems Engineering department at Texas A&M University, College Station.

His research interests include combinatorial optimization, graph theory, algorithms and complexity, focusing on clustering and data-mining applications in social network analysis, computational biology and telecommunication. Under the guidance of Prof. Sergiy Butenko, he is working on graph theoretic generalizations of the maximum clique problem that arise in social network analysis.

Seminar Abstract:
In this talk we will present a graph theoretic generalization of a clique, known as a k-plex, originally introduced in social network analysis. Based on this model, systematic generalizations for independent sets and coloring are also introduced. We will discuss applications of these models in clustering and data-mining networks. Computational complexity results, integer programming formulation and facets and valid inequalities for the associated polytope are presented. Branch-and-cut approaches are discussed and computational test results are provided.