Data Sciences and Artificial Intelligence

Our research in artificial intelligence and big data explores how we can understand intelligence by constructing computational models of intelligent behavior and how we can apply the insights gleaned from the vast amount of information available in our world. Specifically, we study and develop algorithmic theories to model aspects of behavior through machine learning, predictive modeling, data mining, and causal discovery. We explore its applications in health and bioinformatics, social and organizational informatics, learning analytics, text analytics, image analytics, and computational discovery, among others.

Research-Active Faculty
Photo of C. Lee Giles

David Reese Professor of Information Sciences and Technology

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Photo of Vasant Honavar

Professor and Edward Frymoyer Chair of Information Sciences and Technology

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Photo of Hadi Hosseini

Assistant Professor

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Photo of Sharon Huang

Associate Professor

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Photo of Fenglong Ma

Assistant Professor

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Photo of Sarah Rajtmajer

Assistant Professor

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Photo of Justin Silverman

Assistant Professor

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Photo of James Wang

Professor

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Suhang Wang

Assistant Professor

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Photo of Shomir Wilson

Assistant Professor

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Assistant Professor

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Photo of Amulya Yadav

Assistant Professor

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Photo of John Yen

Professor

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Photo of Xiang Zhang

Associate Professor

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Recent News

Surprisingly popular voting algorithm developed to recover ranked choices

August 25, 2021

Imagine you are asked to rank the counties in Pennsylvania in terms of number of COVID-19 infections. Or you may be asked to rank cities in Pennsylvania based on their populations.

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Block by block: Researchers use Minecraft to advance artificial intelligence

August 10, 2021

Researchers received a $900,000 grant to create artificial intelligence that can plan for and solve future problems. They will test the new software on the video game Minecraft.

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Honeypot security technique can also stop attacks in natural language processing

July 28, 2021

As online fake news detectors and spam filters become more sophisticated, so do attackers’ methods to trick them — including attacks through the “universal trigger.” In this learning-based method, an attacker uses a phrase or set of words to fool an indefinite number of inputs, which could lead to more fake news appearing in your social media fe

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Essential workers' tweets show surprising positivity during pandemic

June 9, 2021

During the COVID-19 pandemic, essential workers tweeted less often than general users about COVID-19 but more about overall mental health issues, according to researchers at the Penn State College of Information Sciences and Technology.

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Office of Research

E339 Westgate Building
University Park, PA 16802

researchadmin@ist.psu.edu
(814) 863-6801