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Events for November
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PhD Defense - Charith Wickramaarachchi
Fri, Nov 10, 2017 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Dynamic Graph Analytics for Cyber Systems Security Applications
Ph.D. candidate: Charith Wickramaarachchi
Friday, November 10, 2017
1:00PM, SAL 322
Abstract:
State of the art cyber systems are becoming an organic part of our day to day life with the advancement of internet infrastructure, mobile technologies, and sensor networks. As a result, protecting cyber systems against attacks has become a task of vital importance. However, the highly complex nature of modern cyber systems makes designing of security solutions a challenging task. The mission-critical nature of these systems demands low latency solutions that identify and prevent attacks.
Graphs are fundamental in representing complex interconnected systems and data. Thus, graph representation based security solutions will play a crucial role in future cyber systems security solutions. We propose a set of fundamental dynamic graph algorithms that can be used to develop cyber systems security solutions.
First, we present distributed dynamic graph algorithms that can be used to prevent attacks on cyber systems. We develop distributed algorithms to monitor vertices in a dynamic network to detect if they become a part of a given graph pattern. Evaluations on a diverse set of real-world datasets demonstrate that ~99% savings in computation and communication is achieved by the proposed algorithms compared with state of the art.
Next, to provide high accuracy subgraph pattern matching in dynamic networks, we present a distributed algorithm for exact subgraph matching (i.e., subgraph isomorphism). To improve the latency and scalability of the solution, we propose a lossless distributed graph pruning technique based on graph simulation. Evaluation results demonstrate that our proposed method is highly effective on small-world graphs.
Finally, we present a set of dynamic Steiner tree based protection schemes to address a security vulnerability in the smart grid state estimation process. The proposed protection schemes consider the dynamic nature of the criticality of buses in power transmission networks to provide optimal cost protection recommendations. We develop scalable, highly accurate heuristic algorithms to obtain security recommendations with low latency.
Biography:
Charith Wickramaarachchi received the BSc (Hons) degree (2010) in Computer Science and Engineering from University of Moratuwa and the MS degree (2016) in Computer Science from University of Southern California. He is currently a Ph.D. candidate at the Department of Computer Science at University of Southern California. His research interests are in the areas of large-scale graph processing and data stream processing in distributed environments such as Clouds. He is a member of IEEE, an elected committer and a project management committee member of Apache Software Foundation.
Defense Committee: Viktor K. Prasanna (chair), Rajgopal Kannan, Aiichiro Nakano, Cauligi RaghavendraLocation: 322
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense- Dehua Cheng
Tue, Nov 14, 2017 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Tuesday, November, 14th, 10 a.m. to 12 p.m., PHE 223
Title: Improving machine learning algorithms with efficient data relevance discovery
Abstract:
This is the era of big data, where both challenges and opportunities lie ahead for the machine learning research. The data are created nowadays at an unprecedented pace with an unignorable cost in collecting, storing, and computing with the current scale of data. As the computational power that we possess gradually plateaus, it is an ever-increasing challenge to fully utilize the wealth of big data, where better data reduction techniques and scalable algorithms are the keys to a solution. We observe that to answer a certain query, the data are not equally important. Based on the models and the query, we provide efficient access to the numerical scores of the data points that represent their relevance in the current task. It enables us to wisely devote the computation resources to the important data, which improves the scalability and the reliability. We present our work under three applications: 1) tensor CP decomposition, 2) random-walk matrix-polynomial sparsification, where we provide an efficient access to the statistical leverage score for a faster numerical routine; and 3) matrix completability analysis, where we analyze the underlying completability structure for a more reliable estimation.
Location: Charles Lee Powell Hall (PHE) - 223
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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PhD Defense - David Inkyu Kim
Mon, Nov 20, 2017 @ 09:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
University Calendar
Monday, November 20th, 9 a.m. to 11 a.m, RTH 406
PhD Candidate: David Inkyu Kim
Title: Learning affordances by interactive perception and manipulation
Abstract:
Robots can plan and accomplish various tasks in unknown environment by understanding underlying functionalities of objects around. These attributes are called affordances, describing action possibilities between robot and objects in the environment. Affordance is not an universal property due to its relative nature, therefore must be learned from experiences. Such learning would involve predicting affordances from perception, followed by interactive manipulation. Learned affordance models can be directly applied to robotic tasks as the model describes how to manipulate and what the consequence will be.
In the presentation, methods to learn affordances with interactive perception and manipulation will be introduced. For the developed affordance models, extensive experiments were performed to verify the models and its application to robotic tasks.
Committee:
Gaurav S. Sukhatme
Stefan Schaal
Satyandra K. Gupta
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
