TU Berlin

Quality and Usability LabBabak Naderi

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Dr. -Ing. Babak Naderi

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Research Interests:

  • Subjective quality assessment
  • Speech Quality Assessment in Crowdsourcing
  • Motivation, Workload, and Performance in Crowdsourcing
  • Statistical Modeling, field data and applied statistics
  • Speech Enhancement
  • Text Complexity and Simplification

Biography:

Babak Naderi has obtain his Dr.-Ing degree (PhD) on the basis of his thesis with a title of Motivation of Workers on Microtask Crowdsourcing Platforms in September 2017. Babak has Master's degree in Geodesy and Geoinformation Science form the Technical University Berlin with a thesis on "Monte Carlo Localization for Pedestrian Indoor Navigation Using a Map Aided Movement Model". He has also a Bachelor's degree in Software Engineering.

Since August 2012, Babak Naderi is working as a research scientist at the Quality and Usability Lab of  TU-Berlin.

2013-2015 Babak was awarded with an BMBF funded Education program for future IT and Development Leadership involving Bosch, Datev, Deutsche Telekom AG, Holtzbrinck, SAP, Scheer Group, Siemens, and Software AG  amongst highly ranked academic institution (Softwarecampus). He was taking part by leading CrowdMAQA project.

Within dissertation, Babak studies the motivation of crowdworkers in details. He has developed the Crowdwork Motivation Scale for measuring general motivation based on the Self-Determination Theory of Motivation. The scale has been validated within several studies. In addition, he has studied factors influencing the motivation, and influence of different motivation type on the quality of outcomes. Models for predicting task selection strategy of workers are developed, including models for automatically predicting expected workload associated to a task from its design, task acceptance and performance. 

Beside others research activities, Babak is actively working on the standardization of methods for speech quality assessment in crowdsourcing environment in the P.CROWD work program of Study Group 12 in ITU-T Standardization Sector.

Reviewed for WWW, CHI, ICASSP, CSCW, MMSys, PQS, HCOMP, ICWE, QoMEX, International Journal of Human-Computer Studies, Computer Networks, Behaviour & Information Technology, Quality and User Experience.

 

Selected talks:

  • "Motivation of Crowd Workers, does it matter?",Schloss Dagstuhl, Evaluation in the Crowd: Crowdsourcing and Human-Centred Experiments, November 2015.
  • "Motivation and Quality Assessment in Online Paid Crowdsourcing Micro-task Platforms",Schloss Dagstuhl, Crowdsourcing: From Theory to Practice and Long-Term Perspectives, September 2013.

 

Office Hours: On Appointment

 

Adresse:

Quality and Usability Lab

Technische Universität Berlin
Ernst-Reuter-Platz 7
D-10587 Berlin

Tel.:+49 (30) 8353-54221
Fax: +49 (30) 8353-58409

babak.naderi[at]tu-berlin.de

Publications

Background Environment Characteristics of Crowd-Workers from German Speaking Countries Experimental Survey on User Environment Characteristics
Zitatschlüssel zequeirajimenez2019b
Autor Zequeira Jiménez, Rafael and Naderi, Babak and Möller, Sebastian
Buchtitel 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)
Seiten 1–3
Jahr 2019
ISSN 2372-7179
DOI 10.1109/QoMEX.2019.8743208
Monat jun
Verlag IEEE
Serie QoMEX 2019
Wie herausgegeben Poster
Zusammenfassung Crowdsourcing has been used extensively for gathering and annotating data cost efficiently. Nowadays, there are multiple platforms offering crowd-sourced workforce, still most of these users are from Asia or English speaking countries, and not so many native German speakers. Thus, there is a lack of information regarding the conditions in which German users execute tasks, neither about their habits when taking part in crowdsourcing campaigns. Which is of main importance to address properly user studies to German crowd-workers. This paper reports on a survey that investigated the environments' characteristics of users from German speaking countries. To this end, a study has been conducted in which users were asked to provide details about the surroundings in which they normally execute crowdsourcing tasks. Audio and visual data was collected per user which contributed to aggregate even more information on the users' input. We provide insights aimed at easing the decision making process when designing subjective user studies.
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