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

Pegasus Research Team Discovers Errors for a Win

At the PEARC19 conference this past summer, researchers at the USC Viterbi School of Engineering won a transformative research award in addition to the best technical paper. The work focused on providing scientists extra assurances that their data is not inadvertently corrupted when executing their analysis on multiple computers in a distributed computing environment.

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USC Earth Scientists and AI Researchers Join Forces on New Semantic Web Technology

Sometimes the most unrelated things can produce the most innovative results. Take, for instance, aikido—a Japanese martial art that can be translated as the "way of unifying energy"—and paleoclimatology, a scientific field examining climate evolution.

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Research Experiences for Undergraduates (REU) Interns Showcase Results at ISI

Ten students from ISI's Research Experiences for Undergraduates (REU) program recently concluded their 10-week internship with a poster showcase.

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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.

Jan 24
Bharat Bhargava (Purdue University)AI Seminar

Real Application of Machine Learning (REALM): Situation Knowledge on Demand (SKOD)

11:00am - 12:00pm PST
Jan 30
Sarah Wiegreffe (Georgia Tech)NL Seminar

BlackBox NLP: What are we looking for, and where do we stand?

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ISI Annual Report

View the 2018 ISI Annual Report.

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Events

Unless otherwise noted, seminars are open to the public.

Jan 24
Bharat Bhargava (Purdue University)AI Seminar

Real Application of Machine Learning (REALM): Situation Knowledge on Demand (SKOD)

11:00am - 12:00pm PST
Jan 30
Sarah Wiegreffe (Georgia Tech)NL Seminar

BlackBox NLP: What are we looking for, and where do we stand?

11:00am - 12:00pm PST
See More Events »

ISI Seminar Series

Keep up-to-date with the ISI seminars by subscribing below. You will have the option of subscribing to individual seminar topics.

Subscribe to seminar notifications

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