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

PC Mag: Is That Your Fingerprint or a Fake? This AI Can Tell

As high-level security systems transition to gathering biometric data via facial recognition, iris scans, and fingerprints, researchers such as USC ISI's  Wael Abd-Almageed are creating AI neural nets to spot fakes.

Read More

ISI Student Battles Bias in AI

AI systems are increasingly being used for everything from predicting crime to determining insurance rates—but what happens when human bias creeps into AI? Bias in machine learning algorithms can have serious consequences: people can be denied health services, wrongly targeted for crimes, or turned away for a job because of a discriminatory algorithmic decision.

Read More

USC ISI and Intel Custom Foundry Collaborate to Spur Microelectronics Innovation

USC Viterbi’s Information Sciences Institute (ISI) and the Intel Corporation’s custom foundry organization today announced a collaboration to design, fabricate and package integrated circuits (ICs) through USC ISI’s MOSIS unit.

Read More
See More Stories »

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

Follow ISI

ISI Annual Report

View the 2017 ISI Annual Report.

Events

Unless otherwise noted, seminars are open to the public.

Jun 17Mohamed Elhoseiny, KAUSTAI Seminar

Imagination Inspired Vision

11:00am - 12:00pm PDT
Jun 18Esha ShahOceanside Chat

Orgs/Teams/People with Esha Shah

11:30am - 1:30pm PDT
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