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The Department of Computer Science at the University of Cyprus cordially invites you to the PhD Defense entitled:

SCIENTIFIC WORKFLOW SYSTEMS AND MULTI-OBJECTIVE EVOLUTIONARY ALGORITHMS FOR LIFE SCIENCES INFORMATICS

Speaker: Mr. Christos Kannas
Affiliation: University of Cyprus, Cyprus
Category: PhD Defense
Location: Room 148, Faculty of Pure and Applied Sciences (FST-01), 1 University Avenue, 2109 Nicosia, Cyprus (directions)
Date: Tuesday, June 6, 2017
Time: 10:00-11:00 EET
Host: Constantinos Pattichis (pattichi-AT-cs.ucy.ac.cy)
URL: https://www.cs.ucy.ac.cy/colloquium/presentations.php#cs.ucy.pres.2017.kannas

Abstract:
The field of Scientific Workflow Management Systems (SWMSs) has been receiving considerable interest in recent years. We have developed and evaluated a SWMS specialised for Virtual Screening (VS), the Life Sciences Informatics (LiSIs) platform, which is: (1) a novel integrated web based VS framework, and (2) has been successfully used to identify novel cancer chemopreventive agents from a commercial database of available molecules. Self-adaptation is an efficient way to control the search parameters of an Evolutionary Algorithm (EA) automatically during optimization. It is based on implicit evolutionary search in the space of search parameters, and has been proven to work well as on-line parameter control method for a variety of search parameters. Our proposed Self-Adaptive Multi-Objective Evolutionary Algorithm (Self-Adaptive MOEA) is a two level algorithm. The outer level is the algorithms that is responsible for the self-adaptive techniques and is based on a Multi-Objective Genetic Algorithm (MOGA) implementation. The inner level is the actual elite Multi-Objective Evolutionary Graph Algorithm (eMEGA). Both the outer and inner algorithm are variations of our previously proposed Multi-Objective Evolutionary Graph Algorithm (MEGA) framework. The proposed Self-Adaptive MOEA has been designed to be adaptable, expandable and scalable (utilising multi-core parallelism). The Self-Adaptive MOEA proposed interesting solutions in all problems that has been applied to, though further in-vitro investigation is required to understand the behaviour of the proposed designed molecules in real environment.

Short Bio:
Christos C. Kannas is a Ph.D. candidate at the Department of Computer Science under the supervision of Professor Constantinos S. Pattichis. He earned his BSc in Computer Science from the University of Ioannina, Greece and his MSc in Advanced Information Technologies from the University of Cyprus. He worked as a Junior Programmer at Noesis Chemoinformatics Ltd. He worked as Research Assistant at the Department of Computer Science, University of Cyprus for two EU FP7 funded projects, GRANATUM and Linked2Safety. He worked as Research Associate in Chemoinformatics at the Chemoinformatics Research Group at Information School, University of Sheffield. His research interests include Knowledge Discovery and Data Mining, Knowledge Management, Multi-Objective Optimization, Many-Objective Optimization, Chemoinformatics, Bioinformatics, and Molecule Design. He has hands-on experience in commercial software development, algorithm design, pharmaceutical data analysis, computer-aided drug design, and multi-objective optimization.

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  Colloquia Web: https://www.cs.ucy.ac.cy/colloquium/
  Calendar: https://www.cs.ucy.ac.cy/colloquium/schedule/cs.ucy.pres.2017.kannas.ics