Hina Firdaus M.Sc.
Weblinks
Office address
Weblinks
Vita
Hiring Date: 1 June 2025
Hina Firdaus is a researcher in the INF project at the Collaborative Research Center 1187 “Media of Cooperation” at the University of Siegen. Her research focuses on the integration and negotiation of Artificial Intelligence (AI) tools in interdisciplinary research contexts, with particular interest in AI, machine learning, and human-centered design. She is currently pursuing her doctoral studies, building on a strong interdisciplinary foundation.
Hina holds an M.Sc. in Human-Computer Interaction from the University of Siegen and a Master’s in Technology in Computer Science and Engineering from Jamia Hamdard, New Delhi. She has previously worked in both academic and industry settings on projects involving fairness in AI, participatory design, Unstructured learning algorithm, and Education Technology.
Research Focus
- Machine Learning
- Artificial Intelligence for Infrastructure
- Human Computer Interaction
- Education Technology
- Computer Supported Cooperative learning
Publication:
- Firdaus, Hina, Matthias Laschke, and Claudia Müller. 2023. “Sink Twice: Research through Design to Promote Sustainable Usage of Water at Your Home.” In Nfrahealth 2023 - Proceedings of the 9th International Conference on Infrastructures in Healthcare 2023. European Society for Socially Embedded Technologies (EUSSET). https://dl.eusset.eu/items/327e9cf0-33ab-47db-a227-e2acb9b255bf
- Aditya, and Hina Firdaus. 2022. “Estimated Time of Arrival for Sustainable Transport Using Deep Neural Network.” In Emerging Technologies in Data Mining and Information Security, 490:75–84. Springer, Singapore. https://link.springer.com/chapter/10.1007/978-981-19-4052-1_9#Sec1
- Hina Firdaus, and Syed Imtiyaz Hassan. 2020. “Unsupervised Learning on Healthcare Survey Data with Particle Swarm Optimization.” Springer EBooks 13 (January): 57–89. https://doi.org/10.1007/978-3-030-40850-3_4
- Firdau, Hina, Syed Hassan, and Harleen Kaur. 2018. “A Comparative Survey of Machine Learning and Meta-Heuristic Optimization Algorithms for Sustainable and Smart Healthcare.” A African Journal of Computing & ICT Reference Format 11 (4): 1–17. https://afrjcict.net/wp-content/uploads/2017/08/vol11no4dec18paper1journalformat-with-numbers.pdf
Publications
Sustaining STEAM: Challenges and Strategies for Long-Term Engagement in FabLabs and Makerspaces
Sustaining STEAM: Challenges and Strategies for Long-Term Engagement in FabLabs and Makerspaces
Estimated Time of Arrival for Sustainable Transport Using Deep Neural Network
Estimated Time of Arrival for Sustainable Transport Using Deep Neural Network
Sink Twice – Research through Design to promote sustainable usage of water at your home
Sink Twice – Research through Design to promote sustainable usage of water at your home
Unsupervised Learning on Healthcare Survey Data with Particle Swarm Optimization
Unsupervised Learning on Healthcare Survey Data with Particle Swarm Optimization
A comparative survey of machine learning and meta-heuristic optimization algorithms for sustainable and smart healthcare
A comparative survey of machine learning and meta-heuristic optimization algorithms for sustainable and smart healthcare