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Computer science faculty and student receive grants
Associate Teaching Professor Martin Allen, Professor Donna Slonim, Professor J.P. de Ruiter, and PhD student Charles Threlkeld were awarded DISC seed grants.
The Data Intensive Studies Center (DISC) at Tufts recently announced its picks for the Seed Funding Program, an initiative designed to advance innovative research at Tufts University. Through sponsorship by the Office of the Provost and the Office of the Vice Provost for Research, DISC funded 11 proposals. Among those chosen were two proposals by Professor J.P. de Ruiter with PhD student Charles Threlkeld, and Associate Teaching Professor Martin Allen and Professor Donna Slonim of the Department of Computer Science.
de Ruiter and Threlkeld will conduct a study on the identification of speech acts in natural conversation, merging the fields of linguistics, sociology, and machine learning. Their study, entitled “Predicting Dialogue Acts from Annotated Conversational Corpora,” looks at how speech acts in natural conversation can be identified by using cutting edge linguistic and sociological theory paired with machine learning. “The words we hear and say lend meaning to language, but they aren’t the whole picture,” says Threlkeld. “We also perform speech acts, the social dimension of conversation.”
Allen is part of a team led by Professor Donna Slonim of the Department of Computer Science and Professor Jonathan Runstadler of the Department of Infectious Disease and Global Health, along with biomedical sciences PhD candidate Laura Borkenhagen. Their study, titled “Systematic Inference of Viral Phenotype through AI Approaches,” addresses the topic of inferring viral phenotypes through artificial intelligence (AI) and will analyze major disease outbreaks. These outbreaks can occur when a novel influenza virus in an animal, such as a bird or a pig, adapts to infect humans.
“While virus surveillance efforts can help identify sources of novel viruses, it is difficult to tell whether a virus is going to be dangerous to humans without expensive, time consuming, and labor-intensive lab tests,” the team explains. Through this study, they seek to develop computational tools to help rapidly identify which influenza viruses may be a threat to humans based on the genome sequence of the virus, with the overarching goal of aiding future pandemic preparedness efforts.
The CS department congratulates these teams of researchers for their groundbreaking work.