TU Berlin

Quality and Usability LabTim Polzehl

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Dr. Tim Polzehl

Crowdsourcing Technology

  • High-quality data collection via crowdsourcing
  • Data management and data services via crowdsourcing (clean, index, verify, tag, label, translate, summarize, join, etc. )
  • Data synthesis und data generation via crowdsourcing
  • Subjective influences and bias normalization in crowdsourcing
  • Crowd-creation, crowd-voting, crowd-storming, crowd-testing applications
  • Crowdsourcing service for machine learning and BI
  • Crowdsourcing business and Business Logic
  • Complex automated workflows: combining human and artificial intelligence
  • Crowdsourcing with mobile devices
  • Real-time crowdsourcing
  • Skill-based crowdsourcing and verification of crowd-experts

 

Speech Technology

  • Automatic user classification
  • Automatic speaker characterization (age, gender, emotion, personality) 
  • Automatic speech recognition (ASR),
  • Prosody and voice gesture recognition
  • Prosodic voice print analysis, phonetic science
  • App development with speech functionalities (Android, iOS)

 

Text Classification, Natural Language Processing (NLP)  

  • Sentiment Analysis
  • Affective Analysis, Emotion
  • Personality und Lifestyle Detection from Social-Networks (Twitter, FB, G+, etc.)

 

Machine Learning and Artificial Intelligence  

  • Automated user modelling
  • Classification and prediction systems using linear and non-linear algorithms
  • Feature selection and reduction
  • Evaluation and verification methods

 

Running and Past Projects:

please click here.

 

 


Project Biography 

Tim Polzehl studied Science of Communication at Berlin's Technical University. Combining linguistic knowledge with signal processing skills he focused on speech interpretation and automatic data- and metadata extraction. He gathered experience within the field of machine learning as exercised when recognizing human speech utterances and classifying emotional expression subliminal in speech, the latter of which became his M.A. thesis. 

In 2008 Tim Polzehl started his position as PhD candidate in Telekom Innovation Laboratories (T-Labs) and the Quality and Usability Lab. He worked in both industrial and academic projects with focus on speech technology, App-Development, machine learning crowd sourcing solutions.

2011-2013 Tim was leading a R&D Project for Telekom Innovation Laboratories with Applications in the field of Intelligent Customer-Care Systems and Speech-Apps.

2012-2014 Tim was awarded with an BMBF funded Education program for future IT and Development Leadership involving SAP, Software AG, Scheer Group, Siemens, Holtzbrinck, Bosch, Datev and Deutsche Telekom AG, amongst highly ranked academic institution (Softwarecampus).       

2014 Tim was awarded the PhD for his work on automatic prediction of personality attributes from speech.

Since 2014 Tim has been working as a Postdoc at the Quality and Usability chair of TU-Berlin. At the same time Tim is driving the start-up activity applying the earlier  development of crowdsourcing solutions Crowdee.

 

Address:

Quality and Usability Labs

Technische Universität Berlin

Ernst-Reuter-Platz 7

D-10587 Berlin

Tel.:+49 (30) 8353-58227Fax: +49 (30) 8353-58409




Openings / Supervision

please refer to here.

Publications

Internes Crowdsourcing in Unternehmen
Citation key wedel2021a
Author Wedel, Marco and Ulbrich, Hannah and Pohlisch, Jakob and Göll, Edgar and Uhl, André and Iskender, Neslihan and Polzehl, Tim and Schröter, Welf and Porth, Florian
Title of Book Arbeit in der digitalisierten Welt: Praxisbeispiele und Gestaltungslösungen aus dem BMBF-Förderschwerpunkt
Pages 335–349
Year 2021
ISBN 978-3-662-62215-5
DOI 10.1007/978-3-662-62215-5_22
Address Berlin, Heidelberg
Month feb
Note electronic
Editor Bauer, Wilhelm and Mütze-Niewöhner, Susanne and Stowasser, Sascha and Zanker, Claus and Müller, Nadine
Publisher Springer Berlin Heidelberg
How Published Fullpaper
Abstract Die grundlegende Idee von internem Crowdsourcing (IC) ist, den innerbetrieblichen Wissensaustausch und die Interaktion im Unternehmen zu mobilisieren und zu stärken. Das Lösen von Problemstellungen durch bereichs- und fachübergreifendes Denken und kollaborative Handlungskompetenzen für die Zusammenarbeit sowohl zwischen den Beschäftigten untereinander als auch zwischen Unternehmensführung und Beschäftigten soll mit dem Verfahren auf direkte Weise gefördert werden. Vorhandenes explizites, aber vor allem auch personengebundenes implizites Fach- und Erfahrungswissen kann durch die Anwendung von internem Crowdsourcing schnell im Unternehmen abgerufen und für die Entwicklung von Lösungen, Prozessen und Entscheidungen genutzt werden. Insbesondere durch das niedrigschwellige Erproben neuer Kommunikations- und Kollaborationsmöglichkeiten kann internes Crowdsourcing einen wichtigen Beitrag zu einer veränderten, arbeitnehmerfreundlichen und agileren Unternehmenskultur für die digitalisierte Arbeitswelt leisten.
Link to publication Link to original publication Download Bibtex entry

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