EIN 4517 Systems Engineering (Spring 2012)

Course Goals:

  • To give student basic understanding of systems engineering science.
  • To introduce the student to the basic principles of system modeling.
  • To help student identify the main applications and challenges of systems engineering.
  • To provide the student with basic software knowledge of the system modeling language (sysML) for modeling real world systems.

Topics Covered: Introduction to systems engineering, introduction to sysML, systems modeling, sysML diagrams, modeling requirements, physical systems, interfaces and constraints, process modelling

To download a copy of the syllabus click here.

ESI 5236 Reliability Engineering (Fall 2011)

Course Goals: Course main goal is to introduce students to the statistical methods that are used in reliability and maintainability engineering. As a graduate course we will focus we will focus more in the mathematical and computational aspects and less in software related topics.

Topics Covered: failure distribution, constant failure rate model, time dependent failure models, reliability of systems, state dependent systems, maintainability.

To download a copy of the syllabus click here.

My Erdos Number

My Erdos number 3 through the following chain:

P. Erdos–> R. Graham–> P. M. Pardalos –> P. Xanthopoulos

If you want to learn more about Erdos number click here.

Robust Data Mining

Robust data mining coverXanthopoulos, Petros, Pardalos, Panos M., Trafalis, Theodore B.

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise.

This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents  the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems.

This brief will appeal to theoreticians and data miners working in this field.

ISBN 978-1-4419-9877-4

 Check it on Springer