Researchers


Dr. Y. Huang (Yilin)

Email: y.huang@tudelft.nl
Homepage: http://www.tudelft.nl/yhuang
My research focuses on how to fully utilize data in Modeling and Simulation (M&S). The data used in M&S are often not at the level needed to construct and validate the simulation models. Hence, understanding and bridging the gap could not only contribute to formalizing the transformation process from data to valid models but also offers a possibility to automate the process. The research could have a wide range of application areas. The application domain of the current case study is public rail transportation.

Dr. Farideh Heidari

Email: f.heidari@tudelft.nl
Homepage: http://www.tbm.tudelft.nl/fheidari
Business process modeling is an important part of information systems design as well as of any business engineering or reengineering activity. Business process modeling techniques provide standard ways of presentation and communication between different stakeholders. A business process model is the externalization of the conceptualization of some parts of the object world that deal with those aspects that pertain to the way business transactions are carried out and supported by an information system. My research deals with an essential issue in this context namely, development of a framework, factors and metrics for understanding and measuring objectively the quality of business processes considering their goals and objectives. This objective raises three major issues, (a) the development of a quality evaluation framework that is intended to assist business process modellers and analysts to work in a systematic and generic manner when including quality factors in their BPM activities, (b) the identification of a set of key quality factors relevant to business processes and their concepts, and (c) the definition of the metrics that provide a means for objectively and quantitatively measuring quality of business processes and their concepts. My research is supervised by Professor Brazier as my promoter and Dr. van Langen as my daily supervisor.

Dr.Ir. D. Datcu (Dragos)

Email: D.Datcu@tudelft.nl
Homepage: http://mmi.tudelft.nl/~Dragos.Datcu
My research interests focus on the perception of presence as well as context awareness in teams working jointly on complex problem solving in new interaction spaces such as augmented reality. In the considered interaction scenarios human expertise is often a scarce and expensive resource. The research ambition is to design novel augmented reality-driven solutions that enable participation among distributed. To establish such participatory systems, the foundations of merging realities need to be researched from a technical but also human experience perspective. To address current issues around presence, awareness and human experience in interaction spaces such as augmented reality I intend to apply affective computing technology for automatic emotion analysis. Recognizing emotions will help to adapt novel interaction spaces to the needs and dynamics of the participating users.

Dr. Z. Genc, (Zulkuf)

Email: z.genc@tudelft.nl
Homepage: http://www.tbm.tudelft.nl/index.php?id=50680
Zulkuf Genc received a B.S. degree in Computer Engineering from Istanbul Technical University, Istanbul, Turkey, in 2003 and an M.Sc. degree in Electrical and Computer Engineering from Koc University, Istanbul, Turkey, in 2006. He worked on digital home and use of 60 GHz radio in home networks during his Ph.D. study between 2006 and 2011 in the Faculty of Electrical Engineering, Mathematics, and Computer Science (EEMCS) of Delft University of Technology (TUDelft), Netherlands. He is currently working as postdoctoral researcher in the Systems Engineering and Simulation Section of the Technology, Policy and Management (TPM) Faculty in TUDelft. His research focuses on the application of multi-agent paradigm in understanding, design and evaluation of complex socio-technical systems.

Dr. M. Cidota (Marina)

Email: m.a.cidota@tudelft.nl
Homepage: http://www.tbm.tudelft.nl/index.php?id=100074
My current research interest is in the field of object recognition in a video frame. This includes face detection and analysis with the purpose of person identification and emotion classification. These issues can be approached through multidisciplinary research that uses tools and methods from statistics, pattern recognition and machine learning.

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