Our former colleague Shuo Liu successfully defended his doctoral thesis ‘Deep learning approaches for adaptive audio processing and binary classification in digital health’ on the 16th of August 2023. The examiners were Shuo's supervisor Prof. Dr.-Ing. habil. Björn Schuller, Prof. Dr. Matthias Schlesner (University of Augsburg), and Prof. Dr. Frank Kramer (University of Augsburg). Congratulations!

The universities of Augsburg, Bamberg, Erlangen-Nürnberg, Munich (LMU), and Würzburg have spent four years researching the healthy use of digital technologies and media. ForDigitHealth has presented its findings in academic publications as well as in an online guide accessible to the public.

Das Unternehmen recoro, das aus einem Forschungsprojekt der Universität Augsburg am Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing (EIHW) hervorgegangen ist, hat im Münchener 7. Digital Innovation Award den 1. Platz gewonnen. Das IT-Startup hat einen intelligenten Assistenten für Vertriebsteams entwickelt, um den Verkaufserfolg in virtuellen Gesprächen zu erhöhen und Mitarbeitende datenbasiert zu schulen. 

 

 

On the 20th of October 2022, our former colleague Alice Baird successfully defended her PhD thesis ‘Computer Audition for Emotional Wellbeing’. The examiners were Prof. Dr.-Ing. habil. Björn Schuller (Alice’s supervisor), Prof. Dr. Elisabeth André, Prof. Dr. Frank Kramer (both University of Augsburg) and PD. Dr.-Ing. habil. Ronald Böck (Genie Enterprise Inc.). Congratulations, Alice!

Our former colleague Lukas Stappen successfully defended his doctoral thesis ‘Multimodal Sentiment Analysis in Real-life Videos’ on the 19th of October 2022. The examiners were Lukas's supervisor Prof. Dr.-Ing. habil. Björn Schuller, Prof. Dr. Elisabeth André (University of Augsburg), Prof. Dr. Frank Kramer (University of Augsburg) and PD Dr.-Ing. habil. Ronald Böck (Genie Enterprise Inc.). Congratulations!

Forscher der Universität Augsburg und internationale Partner*innen sprechen
über die Anwendung von Robotern und automatischer Emotionserkennung als
Hilfsmittel in der Therapie autistischer Kinder.

Essen und Getränke werden kostenfrei gestellt

At the Faculty of Applied Computer Science at the University of Augsburg there is now a position at the Chair of Embedded Intelligence for Health Care and Wellbeing (Prof. Dr. Björn Schuller) as

Research Associate (m / f / d)

Das junge Unternehmen Recoro wird durch das Bundesministerium für Wirtschaft und Klimaschutz mit einem EXIST-Stipendium gefördert. Es ist aus einem Forschungsprojekt an der Universität Augsburg entstanden und wird von ihr sowohl wissenschaftlich als auch bei der Unternehmensgründung beraten. Rocoro nutzt künstliche Intelligenz, um Vertriebsprozesse zu optimieren.

On the 18^th of March 2022, our former colleague Max Schmitt successfully defended his doctoral thesis ‘Bag-of-Words Representations for Computer Audition’. The examiners were Prof. Dr.-Ing. habil. Björn Schuller (Max’s supervisor), Prof. Dr. Elisabeth André (Universität Augsburg), Prof. Dr. Frank Kramer (Universität Augsburg) and Prof. Dr.-Ing. Dorothea Kolossa (Ruhr-Universität Bochum). Congratulations, Max!

Our former colleague Zhao Ren successfully defended her PhD thesis „Deep Learning Techniques for Computer Audition“ on the 22^nd of February. The examiners were Zhao’s supervisor Prof. Dr.-Ing. habil. Björn Schuller, Prof. Dr. Elisabeth André and Prof. Dr. Frank Kramer (both University of Augsburg) as well as Prof. Dr.-Ing. habil. Andreas Maier (FAU Erlangen). Congratulations!

Congratulations to former EIHW researcher Vedhas Pandit on the successful defence of his doctoral thesis! Vedhas defended his PhD, ‚Time- and Value-continuous Explainable Affect Estimation In-the-wild‘ on the 23th November. The examiners were Prof. Dr.-Ing. habil. Björn Schuller — Vedhas’ supervisor, Prof. Dr. Elisabeth André (University of Augsburg) and Prof. Dr. Rainer Lienhart (University of Augsburg).

The chair for Embedded Intelligence in Healthcare and Wellbeing is looking for two students to support our research.

 

Topic: emotion recognition from facial and physiological information.

 

Qualification: skills in Machine Learning and Data Analysis

 

Workload: 40h/month (no degree yet) bzw. 36h/Monat (bachelor degree)

 

Duration: June 1 2021 to Dec 31 2021