Women in Data Science (WiDS) 2024

Women in Data Science (WiDS) 2024

From Edge AI to rehabilitation and automated transport

Forschungsgruppe Energienetze (FENES)

  • „Investigating the Interpretability of the Temporal Fusion Transformer for a Small Real World Data Set: A Case Study Concerning Reactive Power Forecasting“. Ariane Franke.

Institut für Sozialforschung und Technikfolgenabschätzung (IST)

  • „Acceptance and Concerns about Smart Meters and web-based Apps: Results of the Project EVEKT“. Miriam Vetter, Sonja Haug, Caroline Dotter, Karsten Weber.
  • „Determinants of Electricity Expenditure by Private Housholds“. Miriam Vetter, Sonja Haug, Caroline Dotter, Karsten Weber.
  • „Digitalisation in Home Care and Therapy“. Sonja Haug. Edda Currle.
  • „Perspectives of AI in non-governmental organizations“. Franziska Hauer, Anna Scharf, Sonja Haug.
  • „A quantitative study on the heterogeneity of female couples in Germany“. Anna Scharf.
  • „Diversity: Important, Fair and Sustainable – but often overlooked in AI Applications“. Kendra Pöhlmann.

Laboratory for Biomechanics

  • „Development of a Deep Learning Framework for Automated Gait Assessment“. Mareike Barthel, Franz Süß, Sebastian Dendorfer.

Impressions of WiDS 2024 (Photos: OTH Regensburg)

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