Researchers


Dr. C.I.M. Nevejan (Caroline)

Email: c.i.m.nevejan@tudelft.nl
Homepage: http://www.tbm.tudelft.nl/index.php?id=30894
My research interest is focused on the design of presence and the design of trust in social interactions between people, in organizations and in larger social and political structures. I use methodologies from the social sciences as well as from the design discipline. Having a profound theoretical interest I find it a challenge to bridge knowledge, insight and skills between different domains. When 'making things happen' in a design process I am convinced this only works when the people involved contribute.

Drs. M.E.D. van den Bogaard (Maartje)

Email: m.e.d.vandenbogaard@tudelft.nl
Homepage: http://www.tudelft.nl/medvandenbogaard
I am interested in facilitating change and innovation in knowledge intensive systems. Learners, trainers and managers share similar goals, but they often have different interests, ideas and motivations on how to get there. This makes it very difficult to design effective solutions. I combine social science research with modelling techniques to understand the dynamics of such systems and to find out what works and why. Currently I am involved in a project on student success: over the past years governments and universities have tried to improve student success, but none of the efforts for change had lasting effects. We focus on the questions why this is the case and on the conditions for real change in this area.

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.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:
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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