The Qatar Computing Research Institute (QCRI) - MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) research collaboration is a medium for knowledge joint-creation, transfer, and exchange of expertise between QCRI and MIT CSAIL scientists. Scientists from both organizations are undertaking a variety of core computer science research projects -- database management, Arabic language technology, new paradigms for social computing, and data visualization, etc., with the goal of developing innovative solutions that can have a broad and meaningful impact. The agreement also offers CSAIL researchers and students exposure to the unique challenges in the Gulf region. Scientists at QCRI are benefiting from the expertise of MIT’s eminent faculty and researchers through joint research projects that will enable QCRI to realize its vision to become a premier center of computing research regionally and internationally.
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.
This project focuses on the generation of video facial expression given an audio signal.
Information technologies today can inform each of us about the best alternatives for shortest paths from origins to destinations, but they do not contain incentives or alternatives that manage the
This project falls into three categories: 1) the use of machine learning and other advanced analytical techniques to discover new information related to on-field performance, and 2) the developme
This project focuses on how data management can be used to facilitate social computing.
The research challenge we address is that of securing computing infrastructure against a broad class of cyberattacks.
We propose a new study type to understand the basis of complex genetic traits, a functional genome-wide association study (fGWAS). Most current experimental designs, relying solely on linear model
Research Objectives and Milestones Summary
Problem: How is memory implemented in the human brain?
The goal of the project is to design a high-throughput and low-power FPGA implementation of the newly proposed sparse FFT algorithm. For the purposes of guiding the implementation
MAQSA is a system for social analytics on news. MAQSA provides an interactive topic-centric dashboard that summarizes news articles and social activity (e.g., comments and tweets) around them.
The major goal of the project is to understand the food habits from social media images.
This research seeks to develop motion magnification and comparison techniques for sports applications, and to develop motion magnification techniques for laparoscopic surgery.
We are exploiting big data for image and video manipulation.
Our objective is to answer the question: How can users get the full benefits of multi-user software even when their friends and colleagues use different software vendors, platforms, and service pro
We aim to assess the current tactics used by Qataris and other GCC nationals to express identity through the use of virtual identity technologies (e.g., social media profiles and avatars), which ar
Current shared computing platforms, from small clusters to large datacenters, suffer from low utilization, wasting billions of dollars in energy and infrastructure every year.