Computer Science Students Unleash Power of Social Web Data

More than 100 students presented their final project posters as part of a new data science course taught by ISI’s Emilio Ferrara and Fred Morstatter.Read More

ISI News

In Memoriam: Danny Cohen, Distinguished Computer Scientist and Long-time ISI Staff Member, Dies at 81

Distinguished computer scientist Danny Cohen, a long-time ISI researcher whose work paved the way for voice over IP (VOIP) technology, has died aged 81.

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ISI Spotlight: Mayank Kejriwal, Computer Scientist and Research Assistant Professor

ISI computer scientist Mayank Kejriwal, a 2019 Forbes 30 Under 30 finalist, uses his computer science skills to solve high- impact social problems like fighting human trafficking and responding to natural disasters. We sat down with him to learn more about his work and his experiences at ISI, where he is part of the Center on Knowledge Graphs.

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USC researchers receive $12.5M grant for craniofacial research data-sharing endeavor

Nearly half of all birth defects involve the face and skull and, for the most part, scientists remain unclear as to why most occur. To better serve families at risk for these conditions, scientists need a comprehensive and systematic understanding of how the faces of healthy children form and what goes wrong to cause common malformations, such as cleft lip and palate.

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Feature Story

ISI Researcher's Machine Learning Method Unearths Early Signs of Alzheimer’s

January 28, 2019

Nearly 50 million people worldwide have Alzheimer’s disease or another form of dementia. While age is the greatest risk factor for developing the disease, researchers believe most Alzheimer’s cases occur as a result of complex interactions among genes and other factors. But those factors and the role they play are not known—yet.

In a new study, USC researchers used machine learning to identify potential blood-based markers of Alzheimer’s disease that could help with earlier diagnosis and lead to non-invasive ways of tracking the progress of the disease in patients.

The method was developed by USC computer science research assistant professor Greg Ver Steeg, a senior research lead at the USC Information Sciences Institute (ISI). Machine learning is a subset of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed.

“This type of analysis is a novel way of discovering patterns of data to identify key diagnostic markers of disease,” said team member Paul Thompson, the associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and professor in the Keck School of Medicine at USC. “In a very large database of health measures, it helped us discover predictive features of Alzheimer’s disease that nobody suspected were there.”

The study, “Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer’s Disease in the Aging Brain,” appeared in Frontiers in Aging Neuroscience, Nov. 28. The study authors are from the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and the USC Information Sciences Institute.  

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Events

Unless otherwise noted, seminars are open to the public.

Aug 23James Foulds, UMBCAI Seminar

Bayesian Modeling of Intersectional Fairness: The Variance of Bias

11:00am - 12:00pm PDT
Aug 29Filip Ilievski, Vrije Universiteit (VU) Amsterdam,AI Seminar

Identity of long-tail entities in text: a knowledge perspective

11:00am - 12:00pm PDT
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Feature Story

ISI Researcher's Machine Learning Method Unearths Early Signs of Alzheimer’s

January 28, 2019

Nearly 50 million people worldwide have Alzheimer’s disease or another form of dementia. While age is the greatest risk factor for developing the disease, researchers believe most Alzheimer’s cases occur as a result of complex interactions among genes and other factors. But those factors and the role they play are not known—yet.

In a new study, USC researchers used machine learning to identify potential blood-based markers of Alzheimer’s disease that could help with earlier diagnosis and lead to non-invasive ways of tracking the progress of the disease in patients.

The method was developed by USC computer science research assistant professor Greg Ver Steeg, a senior research lead at the USC Information Sciences Institute (ISI). Machine learning is a subset of artificial intelligence (AI) that gives computers the ability to learn without being explicitly programmed.

“This type of analysis is a novel way of discovering patterns of data to identify key diagnostic markers of disease,” said team member Paul Thompson, the associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and professor in the Keck School of Medicine at USC. “In a very large database of health measures, it helped us discover predictive features of Alzheimer’s disease that nobody suspected were there.”

The study, “Uncovering Biologically Coherent Peripheral Signatures of Health and Risk for Alzheimer’s Disease in the Aging Brain,” appeared in Frontiers in Aging Neuroscience, Nov. 28. The study authors are from the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and the USC Information Sciences Institute.  

Read More