Dr. Hina Tabassum (Senior Member, IEEE) received the Ph.D. degree from the King Abdullah University of Science and Technology (KAUST). She is currently an Associate Professor with the Lassonde School of Engineering, York University, Canada, where she joined as an Assistant Professor, in 2018. She is also appointed as a Visiting Faculty at University of Toronto in 2024 and the York Research Chair of 5G/6G-enabled mobility and sensing applications in 2023, for five years. Prior to that, she was a postdoctoral research associate at University of Manitoba, Canada. She is listed in the Stanford's list of the World’s Top Two-Percent Researchers in 2021-2024. She received the Lassonde Innovation Early-Career Researcher Award in 2023 and the N2Women: Rising Stars in Computer Networking and Communications in 2022. She has published over 100 refereed articles in well-reputed IEEE journals, magazines, and conferences. Her publications thus far have garnered 6400+ citations with an h-index of 38 (according to Google Scholar). She has been recognized as an Exemplary Editor by the IEEE Communications Letters (2020), IEEE Open Journal of the Communications Society (IEEE OJCOMS) (2023 - 2024), and IEEE Transactions on Green Communications and Networking (2023). She was recognized as an Exemplary Reviewer (Top 2% of all reviewers) by IEEE Transactions on Communications in 2015, 2016, 2017, 2019, and 2020. She is the Founding Chair of the Special Interest Group on THz communications in IEEE Communications Society (ComSoc)-Radio Communications Committee (RCC). She served as an Associate Editor for IEEE Communications Letters (2019–2023), IEEE OJCOMS (2019–2023), and IEEE Transactions on Green Communications and Networking (2020–2023). Currently, she is also serving as an Area Editor for IEEE OJCOMS and an Associate Editor for IEEE Transactions on Communications, IEEE Transactions on Wireless Communications, and IEEE Communications Surveys and Tutorials.
Lecture Topics
- Multiband Wireless Networks for 6G and Beyond – Modeling, Analysis, and Resource Allocation
- Machine Learning for 6G Network Resource Management and Semantic Communications
- Machine Learning empowered Wi-Fi Sensing