Michael Moore's CV Web

Michael John Moore

Address: Department of Computer Science, Bren School of Information and Computer Science, Irvine, CA 92697
Email:;
(office):949 824 5474

Molecular communication and bio-inspired systems researcher

My doctoral research has been in computer systems and networking with focus on approaches that integrate biological systems and computer systems. The research included a project on designing a molecular communication system and a peer-to-peer system. I aim to continue interdisciplinary research in the areas of distributed computer systems and biological systems.

Molecular Communication

Peer-to-peer Discovery

Other

Education

UC Irvine Computer Systems and Networking Phd program (2004-June 2009).
UC Irvine Computer Systems and Networking Masters (2001-2003).
UC Irvine Computer Science and Biology double major (1995-2000).

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Molecular Communication

This research explores the possibility of molecular communication as a solution for communication between nanomachines. Nanomachines are artificial or biological nano-scale devices that perform simple computation, sensing, or actuation. Molecular communication provides a mechanism for nanomachines to communicate over a short distance (adjacent nanomachines to tens of micrometers) using molecules to represent information. Communicating nanomachines can spur the creation of entirely new applications such as a nano-scale distributed computing system or a nano-scale sensing system.

The class of molecular communication systems considered in this research consists of sender nanomachines, receiver nanomachines, information molecules, and the environment that these operate in. Senders and receivers include biological (such as cells) and biologically derived (such as molecular motors or sensors taken from biological systems) nanomachines that are capable of emitting and capturing information molecules (such as proteins, ions, or DNA). The environment is the aqueous solution that is typically found within and between cells.

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Peer-to-peer Discovery

We envision the future network where a variety of data and applications are highly distributed and a large number of users often join or leave. In such a network environment, future applications need to locate distributed network object (i.e., data, application, users) that meet a certain search criteria such as keywords. We develop discovery mechanisms that are decentralized and are based on concepts of keyword similarity between network objects, history of past discovery performance, and formation of keyword strengths based on user preference. These concepts address issues of efficiency, scalability, adaptability, and usability that arise when performing discovery in a large-scale and dynamic network environment.

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