Crisis and Humanitarian Computing

With the proliferation of Web 2.0 Social Media platforms such as Facebook, Twitter, Instagram, and many others (~800 others), bystanders in and around some crisis event use social media to post situational information, get latest updates about their time-critical queries. Moreover, humanitarian organizations such as UN OCHA, Red Cross look for early insights about the disaster event to make timely decisions and launch relief operations accordginly.

Under such time-critical situations, relying on traditional information sources like TV, Radio could potentially deplay the situational awareness process ultimately leading to a much bigger damage in terms of human-lives and economy. However, under such circumstances, especially in the first few hours of a disaster social media can be considered as a vital information source. For instance, Twitter's 140-character message really is an amazing service that proved it to be anything to many events, disasters, people, celebrities, brands etc. It was the Twitter that triggered Arab upheaval, saved many lives during the Sandy hurricane in the US, and during a devastated earthquake in Nepal in 2015.

Of all information that is posted online, more importantly textual information could be useful to gain situational awareness as well as to extract actionable information. We believe that if analyzed timely and effectively, these small bits of information can collectively help humanitarian organizations in their decision-making processes and eventually can save hundreds of lives.

Having in mind this motivation, I am interested in developing computational methods and technologies using big data analysis techniques such as text classification, data mining, machine learning, and deep neural networks, which can help stakeholders gain situational awareness and actionable information to better improve their decision-making processes.

Research talks: Research talks related to my current research interests.

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