Dr. Tim Polzehl
- 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
- 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.
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 .
Quality and Usability Labs
Technische Universität Berlin
Tel.:+49 (30) 8353-58227Fax: +49 (30) 8353-58409mailto:firstname.lastname@example.org 
|Author||Iskender, Neslihan and Polzehl, Tim|
|Title of Book||Internal Crowdsourcing in Companies: Theoretical Foundations and Practical Applications|
|Editor||Ulbrich, Hannah and Wedel, Marco and Dienel, Hans-Liudger|
|Publisher||Springer International Publishing|
|Abstract||Crowdsourcing has become one of the main resources for working on so-called microtasks that require human intelligence to solve tasks that computers cannot yet solve and to connect to external knowledge and expertise. Instead of using external crowds, several organizations have increasingly been using their employees as a crowd, with the aim of exploiting employee's potentials, mobilizing unused technical and personal experience and including personal skills for innovation or product enhancement. However, understanding the dynamics of this new way of digital co-working from the technical point of view plays a vital role in the success of internal crowdsourcing, and, to our knowledge, no study has yet empirically investigated the relationship between the technical features and participation in internal crowdsourcing. Therefore, this chapter aims to provide a guideline for organizations and employers from the perspective of the technical design of internal crowdsourcing, specifically regarding issues of data protection privacy and security concerns as well as task type, design, duration and participation time based on the empirical findings of an internal crowdsourcing platform.|