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

It's Not Magic, It's Science: Predicting the Future

It could be argued that scientists create superpowers in their labs. If Aram Galstyan, director of the Artificial Intelligence Division at the USC Viterbi Information Sciences Institute (ISI) had to pick just one superpower, it would be the ability to predict the future. What will be the daily closing price of Japan's Nikkei 225 index at the end of next week? How many 6.0 or stronger earthquakes will occur worldwide next month? Galstyan and a team of researchers at USC ISI are building a system to answer such questions.

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In Memoriam: Bill Manning, Distinguished Computer Scientist at ISI and Internet Visionary

Renowned computer scientist Dr. Bill Manning, whose work was fundamental to developing the internet as we know it today, passed away January 25, 2020.

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How To Reduce Bias in AI? Selective Amnesia.

Imagine if the next time you apply for a loan, a computer algorithm determines you need to pay a higher rate based primarily on your race, gender or zip code.

Now, imagine it was possible to train an AI deep learning model to analyze that underlying data by inducing amnesia: it forgets certain data and only focuses on others.

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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.

Feb 28
Andrea Bertozzi, UCLAAI Seminar

Graphical Models in Machine Learning, Networks, and Uncertainty Quantification

11:00am - 12:00pm PST
Mar 02
Loic PottierScientific Computing Seminar

Scheduling and memory management for large-scale applications: from caches to burst buffers

11:00am - 12:00pm PST
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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.

Feb 28
Andrea Bertozzi, UCLAAI Seminar

Graphical Models in Machine Learning, Networks, and Uncertainty Quantification

11:00am - 12:00pm PST
Mar 02
Loic PottierScientific Computing Seminar

Scheduling and memory management for large-scale applications: from caches to burst buffers

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