Topic:
Mathematical Modeling and Heuristic Optimization
Presenter:
Dr. May Itani, Assistant Professor of Computer Science
Date and Time:
Friday, Jan 26, 2024 at 1:00 PM
Location of event:
Debbieh Campus, Meeting Room A3 Building
Zoom:
https://zoom.us/j/96417417775?pwd=bEd3WmFsNlZyWGh2WXpBTVd6MUhDZz09
Biography:
May Itani received her B.E. degree in electrical engineering and M.E. degree in computer & communications engineering from the American University of Beirut (AUB), Lebanon, in 1999 and 2004, respectively, and her Ph.D. degree in computer science from Beirut Arab University, in 2017. She was a recipient of the Charli Korban Award for Outstanding Graduate Student at AUB. She worked as a teaching assistant at Beirut Arab University from 1999-2011 and as a part-time Instructor at LAU & AUB, from 2012 to 2019 respectively. She joined Beirut Arab University as a Full Timer, in September 2020. She is currently an Assistant Professor in the Mathematics & Computer Science Department at BAU. Her research interests are in the field of heuristic optimization using artificial intelligence, future wireless networks, parallel processing, and cryptography.
Abstract:
This talk focuses on modeling real life problems mathematically and solving them using heuristic techniques aspired from artificial intelligence. In this context, we will present three case studies that are modeled mathematically and solved heuristically. The first case study is failure recovery of distributed storage systems using erasure coding. The second case study proposes a high-fidelity digital twin via optimizing accuracy and synchronization in an industrial IoT network. The third case study presents a cooperative approach for content caching and delivery in the context of internet of connected vehicles, where a roadside unit, having access to a library of contents but with limited communication coverage, collaborates with a UAV to deliver contents to vehicles on a road segment. The three problems are modeled mathematically as mixed integer non-linear programming (MINLP) problems with different objectives. The problems are solved heuristically, and a range of results is presented in each case to assess the effectiveness of the solution approaches that are shown to generate results close to optimal in the three different scenarios.
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