Research in IST cuts across traditional boundaries to drive interdisciplinary discovery and innovation. Our research is sponsored by a variety of national and international agencies, and we collaborate with diverse groups of scholars within and beyond Penn State. Explore our funded projects to see how IST's transformative research is addressing the world's most complex problems at the intersection of information, technology, and society.
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Researcher:
Ma, Fenglong
Sponsoring Agency: National Science Foundation
This project is aimed at developing and testing new algorithms and techniques for analyzing and integrating different types of health care data, such as electronic health records, medical imaging, and patient-generated data.
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Term:
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Researcher:
Wilson, Shomir
Sponsoring Agency: National Science Foundation
This project will examine the full spectrum of consumer-oriented legal documents (COLDs), with the goal of bridging the understanding gap between
consumers and these documents.
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Term:
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Researcher:
Hosseini, Hadi
Sponsoring Agency: National Science Foundation
CAREER: Robust Fairness in Matching MarketsLearn more...
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Researcher:
Wang, James
Sponsoring Agency: National Science Foundation
Collaborative Research: CCRI: New: An Open Data Infrastructure for Bodily Expressed Emotion UnderstandingLearn more...
Term:
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Researcher:
Giles, C. Lee; Wilson, Shomir
Sponsoring Agency: National Science Foundation
We propose to build a large-scale, longitudinal, annotated, and searchable resource of privacy policies, terms of service agreements, cookie policies, and other related documents for the privacy research community. This resource, which we name PrivaSeer, will serve three simultaneous roles: (1) a search engine for privacy documents (i.e., privacy policies plus other species of relevant text); (2) a source of corpora for use by the research community; and (3) an API for privacy-enhancing technologies to draw privacy information from on demand.
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Term:
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Researcher:
Maulik, Romit
Sponsoring Agency: U.S. Department of Energy
DeepFusion Accelerator for Fusion Energy Sciences in Disruption MitigationsLearn more...
Term:
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Researcher:
Hills, Michael; Giacobe, Nicklaus A.
Sponsoring Agency: National Security Agency
DoD NSA CySP 2023-24Learn more...
Term:
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Researcher:
Wang, Suhang; Honavar, Vasant
Sponsoring Agency: U.S. Department of Homeland Security
Exploring Graph Neural Networks for Attributed Multilayer Criminal Network Analysis(Previous Agreement No. E205949D)Learn more...
Term:
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Researcher:
Squicciarini, Anna
Sponsoring Agency: National Science Foundation
Intergovernmental Personnel Act (IPA) for Anna SquicciariniLearn more...
Term:
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Researcher:
Lee, Dongwon; Giacobe, Nicklaus A.
Sponsoring Agency: National Science Foundation
Penn State CyberCorps; Scholarship for Service ProgramLearn more...
Term:
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Researcher:
Wang, Ting
Sponsoring Agency: National Science Foundation
We propose a new disciplinary concept of computational Screening and Surveillance (CSS) that utilizes edge learning to collect, analyze and interpret both physical and physiologic data of human subjects, to detect early indicators of diseases, and monitor health changes in both individuals and populations. Real-time information, knowledge, and insights from extreme-scale CSS will revolutionize our understanding, prediction, intervention, treatment, and management of acute/infectious (e.g. flu, COVID), chronic physical and psychological/psychiatric diseases, resulting in huge savings for numerous diseases each costing hundreds of billion dollars every year.
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Term:
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Researcher:
Liu, Peng
Sponsoring Agency: New York University
This project will develop approaches for real-time measurements of auxiliary signals that can be used to detect the existence of malicious/anomalous
elements such as kernel rootkits, ransomware, and keyloggers.
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Term:
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Researcher:
Hu, Hong
Sponsoring Agency: National Science Foundation
As cyber attackers are always exploring novel, low-cost hacking vectors to bypass current defenses, security researchers should examine the remaining threats comprehensively in order to develop effective defenses in advance. Within program memory, attackers are shifting their attentions from control hijacking to more stealthy, pure data manipulation: they aim to modify security-critical variables to bypass security checks, like authentication and authorization. Researchers must understand which variables determine application security before developing efficient defenses to prevent so-called data-only attacks. This project proposes three thrusts to comprehensively understand the practicality of automatically constructing data-only attacks. Learn more...
Term:
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Researcher:
Wang, Ting; Ma, Fenglong; Gui, Xinning
Sponsoring Agency: National Science Foundation
The goals of this project are to thoroughly investigate the potential security risks of automated machine learning and to develop rigorous yet easy-to-use mitigation to curb such risks without compromising the benefits of AutoML.
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Term:
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Researcher:
Laszka, Aron
Sponsoring Agency: National Science Foundation
SCC-IRG Track 1: Mobility for all - Harnessing Emerging Transit Solutions for Underserved CommunitiesLearn more...
Term:
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Researcher:
Wang, Suhang
Sponsoring Agency: Army Research Office
Toward Interpretable and Stable Graph Neural NetworksLearn more...
Term:
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Researcher:
Rajtmajer, Sarah; Wilson, Shomir
Sponsoring Agency: National Science Foundation
This project will study how SES affects the challenges of privacy self-management and will develop technologies to support privacy equity.
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Term:
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Researcher:
Liu, Peng
Sponsoring Agency: National Science Foundation
To bridge the gap in cyber-defenses for servers located in enterprises, this project will develop a first-of-its-kind co-design framework, which involves three intertwined components: newly synthesized mathematical models, online learning-based defense algorithms and server retrofitting. The developed defenses will present adversaries optimized dynamically changing attack surfaces, thereby significantly increasing uncertainty and complexity for the adversaries to succeed. They will significantly improve adaptive and autonomous defense capabilities of real-world servers against zero-day attacks during vulnerability windows.
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Term:
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Researcher:
Wang, Ting
Sponsoring Agency: National Science Foundation
This project aims at understanding the security threats incurred by reusing third-party models as building blocks of machine learning (ML) systems and developing tools to help developers mitigate such threats throughout the lifecycle of ML systems. Outcomes from the project will improve ML security in applications from self-driving cars to authentication in the short term while promoting more principled practices of building and operating ML systems in the long run.
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