My researches focus on using the collective intelligence of distributed human computation systems. The general assumption in collective intelligence is that aggregating the results of small and precisely defined task solved by independent people can produce results and behaviors that seems to be intelligent. Therefore, I am especially interested in the design of complex processes within the crowd.
I am a fast-track student at the DDIS group at the Institute of Informatics. Before that I studied Information Systems and Computer Science (Bachelor of Science) at the University of Zurich and worked for Schneider Software AG as Software Engineer and worked during a summer internship for the Swiss National Bank (SAP SRM customizing and business process analysis).
, CrowdLang - First Steps Towards Programmable Human Computers for General Computation, In Proceedings of the 3rd Human Computation Workshop (HCOMP 2011), AAAI-Press 2011. (inproceedings)
Crowdsourcing markets such as Amazon?s Mechanical Turk provide an enormous potential for accomplishing work by combining human and machine computation. Today crowdsourcing is mostly used for massive parallel information processing for a variety of tasks such as image labeling. However, as we move to more sophisticated problem-solving there is little knowledge about managing dependencies between steps and a lack of tools for doing so. As the contribution of this paper, we present a concept of an executable, model-based programming language and a general purpose framework for accomplishing more sophisticated problems. Our approach is inspired by coordination theory and an analysis of emergent collective intelligence. We illustrate the applicability of our proposed language by combining machine and human computation based on exist- ing interaction patterns for several general computation problems.
MOCCA - A System That Learns and Recommends Visual Preferences Based on Cultural Similarity, International Conference on Intelligent User Interfaces (IUI) 2011. (inproceedings/Demo)
, Aggregating social networks - entity resolution with face recognition, 08 2010. (bachelorsthesis)
The Internet, especially social network sites have become an integral part of our daily lives. Personal data, stored in Internet resources, build a huge data set for social network analyis. This bachelor thesis evaluates the feasibility of an entity resolution system based on face recogntion with the goal to integrate several social networks in an aggregated one.