The goal of this project is to conduct applied and core computer science research and to build innovative technologies that can be used by decision-makers, NGOs, affected communities, and scholars to improve the effectiveness of humanitarian strategies such as preparedness, mitigation, and response during humanitarian crises and emergencies. The core of this project will focus on developing a multimodal data processing system for understanding disaster scenes and situations from social media.
This project is focused on the development of a lifestyle recommendation system eventually intended to reduce the risk of obesity and type 2 diabetes. The project team will explore the use of reinforcement learning with a new healthy lifestyle and behavioral change representation initially focused to recommend activity patterns which maximize the user's quality of sleep. These recommendations will be used to create both a new model for behavioral change (which will be incorporated into a health coaching system to provide just-in-time recommendations to increase the user's quality of sleep), as well as a new analytics system to support coaching by healthcare professionals.
The main focus of this project is to discover causal relationships in (multivariate) sequence of states (e.g. in health data) and to uncover the complex dependency structures from high-dimensional time-series encoded as sequence of states. The project team will address, in particular, the following challenges, using optimal transport methodology: machine learning techniques to extract sequences of states from time-series data, causality analysis from sequence of states data, explanatory models for sequence of states data, supervised learning methods to predict categorical or continuous output from sequence of states input data, unsupervised learning methods for sequence of states data, and factor analysis for sequence of states data.
This project aims to develop accurate map-making techniques using crowd-sourced methods to overcome challenges related to creating and maintaining street maps, especially in a rapidly developing environment such as Doha, Qatar, leveraging data primarily from mobile phones and investigating current limitations due to sensor noise, outages and data sparsity.
This project aims to develop key speech and language processing technology enabling users to search for verified facts and claims, in both written and video repositories of English and Arabic, using questions posed in natural and spoken language. The research addresses four essential cross-cutting topic areas to achieve this objective. First, we will investigate methods that enable rich annotation of Arabic multimedia content. Second, we will investigate language processing methods to analyze open-ended user-generated content, e.g., dialogs, and perform veracity assessment and inference. Third, we will explore speech and language methods for processing low-resource Arabic dialects. Finally, we will explore interpretation and debugging techniques to improve machine translation between English and Arabic.