News

Suicide, the 10th leading cause of death for adults in the United States and the third leading cause of death among kids ages 10 to 14 and young adults ages 15 to 24, is often the result of an underlying mental health condition such as depression, anxiety, or bipolar disorder. 

Motivated by a suicide mortality by state map released by the Centers for Disease Control and Prevention (CDC) on the increasing severity of mental health crisis — further exacerbated by the COVID-19 pandemic — three Ph.D. students and their advisor at the Sanghani Center for Artificial Intelligence and Data Analytics are analyzing social media in a way that can help social workers and other professionals better understand and tackle different aspects of mental health issues to help prevent suicide. Read the full story here.

Abdulaziz Alhamadani’s path to computer science is somewhat atypical.

Having already earned a bachelor of arts degree in English language from Umm Al-Qura University and a master of arts in English literature from King AbdulAziz University, Alhamadani made a decision to combine his knowledge of linguistics with computer science. That resolve led him to the University of New Hampshire, where he earned a master of science degree in computer science.

Now, as a Ph.D. student in computer science at the Discovery Analytics Center,  Alhamadani is focusing on Arabic natural language processing, especially text summarization and text classification. Advised by Chang-Tien Lu, his work involves automatic archiving of news without human annotation and summarizing daily news articles to headlines.

“Being a DAC student offers an eclectic array of trending areas of research ranging from data analytics to natural language processing,” said Alhamadani. “I like the combination of research areas and how everyone — my advisor Dr. Lu, other distinguished professors at the center, and fellow students — motivate me. They are always willing to share their views and offer their help.”

“Collect Ethically: Reduce Bias in Twitter Datasets,” his collaborative work with Lu and two other students, was presented at SIMBig2019 last summer. In the study, the research team addresses factors that lead to sampling bias, presents case studies it encountered, proposes an approach that will reduce sampling bias and flaws in datasets collected from Twitter, and then follows the proposed guidelines to conduct two case studies to achieve a larger dataset.

“The results show that using multiple Twitter application programming interfaces for data collection is the best way to obtain a randomly sampled dataset,” Alhamadani said.

Another study, “Batman or the Joker? The Powerful Urban Computing and its Ethics Issues,” was published in December by ACM SIGSPACIAL.

Alhamadani said he is currently working to create the largest database for Arabic news articles for text summarization.

He serves as vice president of the Graduate Student Assembly in northern Virginia and has helped organize graduate student activities. In December he participated in the annual Scottish Walk Parade in Old Town Alexandria, where President Sands was Grand Marshal.

Projected to graduate in 2022, Alhamadani’s career goal is to be a professor at a Saudi Arabia university.