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

Optimization and Data Analysis in Biomedical Informatics

Pardalos, Panos M.; Coleman, Thomas F.; Xanthopoulos, Petros (Eds.)

This volume covers some of the topics that are related to the rapidly growing field of biomedical informatics. In June 11–12, 2010 a workshop entitled ‘Optimization and Data Analysis in Biomedical Informatics’ was organized at The Fields Institute. Following this event invited contributions were gathered based on the talks presented at the workshop, and additional invited chapters were chosen from world’s leading experts. In this publication, the authors share their expertise in the form of state-of-the-art research and review chapters, bringing together researchers from different disciplines and emphasizing the value of mathematical methods in the areas of clinical sciences.

This work is targeted to applied mathematicians, computer scientists, industrial engineers, and clinical scientists who are interested in exploring emerging and fascinating interdisciplinary topics of research. It is designed to further stimulate and enhance fruitful collaborations between scientists from different disciplines.

ISBN 978-1-4614-4132-8

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Data Mining for Biomarker Discovery

Pardalos, Panos M.; Xanthopoulos, Petros; Zervakis, Michalis (Eds.)

Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics.

This volume is a collection of  state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms.

ISBN 978-1-4614-2106-1

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Computational Neuroscience

Pardalos, Panos M.; Xanthopoulos, Petros; Zervakis, Michalis (Eds.)

Computational Neuroscience Book

The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from a large enough number of neurons to empirically test the theories of human brain function computationally. This book is composed of state-of-the-art experiments and computational techniques that provide new insights and improve our understanding of the human brain.

This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience.

The results presented in this book are of great interest and value to scientists, graduate students, researchers and medical practitioners interested in the most recent developments in computational neuroscience.

Check it on Springer or Amazon