Being part of the Qatar Computing Research Institute (QCRI) provides me with a unique opportunity to contribute to building a new world-class data science institute; and to engage in high-impact data science projects of interest to QCRI and its partners, which could have positive transformative effects for Qatar and the region.
At QCRI, Dr. Abdelkader focuses on the development of machine learning and linear algebra techniques in a multi-layer framework. He is working on the following projects of relevance to Qatar.
(1) Multiplex Networks for Multi-Modal Transportation – FIFA 2022 in Doha: Multilayer networks have been the subject of intense research in the recent years in different applications. However, in urban mobility, the multi‐layer nature of transportation systems has been generally ignored, even though most large cities are spanned by more than one transportation system. These different modes of transport have usually been studied separately. It is however important to understand the interplay between different transport modes. In this project, we consider the multimodal transportation system, represented as a multiplex network, and we address the problem of urban mobility in the transportation system, in addition to its robustness and resilience under random and targeted failures. Multiplex networks are formed by a set of nodes connected by links having different relationships forming the different layers of the multiplex. We study, in particular, how random and targeted failures to the transportation multiplex network affect the way people travel in the city. More specifically, we are interested in assessing the portion of the city covered by a random walker under various scenarios. The overall project is to develop a computational framework to better understand and predict mobility patterns in the city of Doha once its ambitious metro system is deployed in 2019. The computational framework will help the city to efficiently manage the flow of people and intelligently handle capacity through different transportation modes, in particular during mega events such as Soccer Wold cup FIFA 2022.
(2) City Resilience (the metro of Doha): We are interested in the study and quantification of the city resilience, i.e., the capacity of a urban area to confront uncertainty and/or risk, and its ability to recover from it. In this particular project, we are mainly interested in the study of the robustness of the metro network against random and/or targeted failure of its segments and/or nodes to identify its reduced exposure and fragility. The metro network is prone to fail anywhere, anytime, as opposed to targeted attacks on critical parts of the infrastructure which is the second goal of the study. We use well-established methods and techniques from complexity science, such as percolation theory, to get valuable information on the vulnerability of the rail network, and to uncover the critical nodes and segments, under different scenarios. This will help in assessing the infrastructural fragility of the metro network.
(3) Traffic Prediction: Real-time traffic prediction from high-fidelity spatio-temporal traffic sensor datasets in an important problem for intelligent transportation systems and sustainability. However it is challenging due to the complex topological dependencies and high-dynamism associated with changing road conditions, especially for a fast growing city like Doha. In this project, we are interested in addressing these challenges holistically, via a tensor formulation and a temporal regularized matrix factorization for high-dimensional time series prediction.
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