2014 – 2019: PhD of Psychology, Humboldt University of Berlin Research fellowship at the Social Cognition Group of the Berlin School of Mind and Brain Thesis: "On Facial Mimicry and Empathy: Insights from individuals with autism spectrum conditions, electromyography and machine learning”

2013 – 2017: Master of Computational Science, University of Potsdam Computer science master focused on data analysis Thesis: "Using Neural Networks to Detect Autism from Video Recordings of Virtual Interactions"

2008 – 2013: Bachelor & Master of Psychology (Cognitive Neuroscience), Ruhr University Bochum Research-oriented study program with a focus on cognitive neuroscience Thesis: "The Influence of Acute Stress on Trust Learning"

Aktuelle Forschungsthemen

Automatic affect recognition: Previous solutions for affect recognition are based on non-representative and unrealistic data sets. The first step towards better emotion recognition is therefore the collection of a balanced video data set in a situation as natural as possible using standardized test procedures. With this data material we want to develop algorithms of machine learning for affect recognition. Since the data material is video data, innovative approaches to the integration of different modalities (voice, facial expression, gaze behavior) can be used .

Computer-based stress measurement: The measurement of stress has so far focused mainly on self-report or individual parameters of the physiological response. In different stress paradigms we want to record the non-verbal behavior of test persons together with physiological markers. Based on these multimodal data, we develop algorithms for automatic stress detection and validate them in natural environments.

Analysis of social embedding: To assess the social integration of a person, clinicians and researchers often use questionnaires. In order to capture the social integration of a person more sensitively and objectively, we want to develop an automatic analysis of online social interaction data. In a large online study, we compare the sensitivity of this approach to classical questionnaires. Furthermore, we identify characteristic and helpful interaction patterns using machine learning to predict the social embedding and resilience of a respondent.

Frau Prof'in Dr. Hanna Drimalla

Kontakt

1. Technische Fakultät / Arbeitsgruppen / AG Human-centered Artificial Intelligence

2. Technische Fakultät / Kommissionen und Ausschüsse / Fakultätskonferenz / Hochschullehrerinnen und Hochschullehrer

3. Technische Fakultät / Kommissionen und Ausschüsse / Studienbeirat Informatik

4. SFB / Transregio 318 “Constructing Explainability“ / TRR 318 Projektbereich A: Erklären / TRR 318 Teilprojekt A06: Inklusive Ko-Konstruktion sozialer Signale des Verstehens

5. Joint Research Center on Cooperative and Cognition-enabled AI

6. Technische Fakultät / Kommissionen und Ausschüsse / Habilitationsausschuss

7. Center for Cognitive Interaction Technology CITEC

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