Dr. Sana Hassan Imam
Kontakt
Leitung
Sekretariat
Anschrift
Anfahrt und Lageplan
Dr. Sana Hassan Imam
Research Interests
- Large Language Models (LLMs) for systematic reviews and meta-analyses
- Generative AI, including LLMs in online collaboration
- Machine learning and explainable AI (XAI) and applications to imaging neuroscience
- Human-Computer Interaction and creativity support systems
- User engagement and personalization in online collaborative communities
Academic positions
| Since June 2025 | Postdoctoral Researcher, Carl von Ossietzky Universität Oldenburg, Germany |
01/2023 – 08/2025 | Research Associate, Diginomics Research Group, University of Bremen, Germany |
01/2021 – 12/2024 | PhD Researcher, University of Bremen, Germany |
11/2013 – 12/2021 | Assistant Professor, NUCES-FAST (National University of Computer and Emerging Sciences), Pakistan |
12/2012 – 10/2013 | Lecturer, NUCES-FAST, Pakistan |
02/2009 – 08/2012 | Lab Engineer, NUCES-FAST, Pakistan |
Education
01/2021 – 12/2024 | PhD-Ing in Informatics, University of Bremen, Germany |
2011 – 2013 | Master of Engineering in Electrical Engineering, NUCES-FAST, Islamabad, Pakistan |
2007 – 2011 | Bachelor of Engineering in Electrical (Telecom), NUCES-FAST, Islamabad, Pakistan |
Publications
Imam, S. H. (2025). Enhancing User Engagement in Online Collaborative Communities using AI. DOI: 10.26092/ELIB/4063 (PhD Thesis)
Imam, S. H., Metz, C. A., Hornuf, L., & Drechsler, R. (2024). Determining the Effect of Feedback Quality on User Engagement on Online Idea Crowdsourcing Platforms Using an AI Model. Proceedings of the ACM on Human-Computer Interaction 8 (CSCW2), Article 376. DOI: 10.1145/3686915
Imam, S. H., Metz, C. A., & Drechsler, R. (2024). How Can Generative AI Curate the User Creativity on an Idea Crowdsourcing Platform? ACM CHI 2024
Imam, S. H. (2024). Let’s Brainstorm: Personalized Chatbot Prototype as Creativity Partner in Idea Crowdsourcing Platforms. Diginomics Conference 2024
Imam, S. H., Metz, C. A., Hornuf, L., & Drechsler, R. (2023). Classifying Crowdsourcing Platform Users' Engagement Behaviour using Machine Learning and XAI. Mensch und Computer 2023 - Workshopband. DOI: 10.18420/muc2023-mci-ws16-385
Imam, S. H., Huhn, S., Hornuf, L., & Drechsler, R. (2023). A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning. Credit and Capital Markets–Kredit und Kapital, 27–62. DOI: 10.3790/ccm.56.1.27
Imam, S. H., Huhn, S., & Hornuf, L. (2022). Feature Importance and Extensibility for Predicting Loan Defaults in Marketplace Lending using BiLSTM. Frontiers of Factor Investing Conference (FoFi)
Priberny, C., Imam, S. H., Huhn, S., Hornuf, L., Drechsler, R., et al. (Year TBD). Scientific Papers. (Further details available on Google Scholar)