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
A Knowledge Graph Warehouse for Nonfatal Firearm InjuriesLearn more...
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Researcher:
Honavar, Vasant
Sponsoring Agency: National Center for Complementary and Integrative Health
AI-based Mapping of Complex Cannabis Extracts in Pain PathwaysLearn more...
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Researcher:
Hosseini, Hadi
Sponsoring Agency: National Science Foundation
The broad objective of this proposal is to develop a theoretically grounded approach for robust fairness in practical and large-scale allocation markets through the integration of Artificial Intelligence (AI), economics, and computation.
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Researcher:
Wang, James
Sponsoring Agency: National Science Foundation
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Researcher:
Wang, James
Sponsoring Agency: National Science Foundation
Collaborative Research: Cellular and Biomechanical Mechanisms of Rapid Stomatal Dynamics in GrassesLearn more...
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Researcher:
Honavar, Vasant; Billah, Syed
Sponsoring Agency: National Science Foundation
Collaborative Research: FW-HTF-RL: Future of Digital Facility Management (Future of DFM)Learn more...
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Researcher:
Abdullah, Saeed
Sponsoring Agency: National Science Foundation
Collaborative Research: FW-HTF-RL: Understanding the Ethics, Development, Design, and Integration of Artificial Intelligence Teammates in Future Mental Health WorkLearn more...
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Researcher:
Honavar, Vasant
Sponsoring Agency: National Science Foundation
The research involves the development of new experimental technologies to investigate RNA structures one molecule at a time and new computational technologies of artificial intelligence wherein a computer learns patterns that can predict RNA structure and its variation. Using rice (Oryza sativa) as the primary model system, the proposed research will develop new wet bench and computational approaches that will allow categorization of the mRNA “pan-structurome,” its consequent impacts on gene expression, and its functional association with respect to local climate conditions in rice landraces. Training will be provided to postdoctoral fellows, graduate students, undergraduates, and high school students and teachers. Broader Impacts will include development of the Oryza CLIMtools webtool to relate rice genotypes with climate variables and to identify beneficial structural haplotypes for use in development of elite rice cultivars. Impact will be broadened through technology including enhanced browser-based RNA structure-reactivity visualization and publicly available instructional screencasts.
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Researcher:
Honavar, Vasant
Sponsoring Agency: National Science Foundation
This project brings together a team of researchers with complementary expertise in formal methods, artificial intelligence, and preference reasoning to develop methods and tools for representing and reasoning with multi-stakeholder preferences.
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Researcher:
Hosseini, Hadi
Sponsoring Agency: National Science Foundation
Fair division deals with the distribution of welfare among a population of agents with the goal of achieving fairness. We propose a new framework based on epistemic fairness through information withholding. The broad goal of this proposal is to provide axiomatic and algorithmic solutions for fair division in practical, large-scale, settings, as a broad contribution to the grand scheme of AI and economics for social good. In addition, this proposal plans to investigate the strategic behavior of agents under information withholding, develop mechanisms that prevent such strategic manipulations, and experimentally study the perception of fairness among humans.
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Researcher:
Wang, James
Sponsoring Agency: National Science Foundation
Our research attempts to develop new algorithms that accomplish the first step of such interactive learning. While moving through an environment, an agent senses the passage of time and spatial position, which provide metrics of similarity that can be used as a self-supervisory training signal. Such a learning mechanism, could be available to a child prior to their ability to understand verbal or social cues and would also be effective for artificial agents attempting to learn the visual statistics of a new environment.
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Researcher:
Wang, James
Sponsoring Agency: National Institutes of Health
This project aims to leverage the simplicity and low cost of digital photographs and the computational and decision power of recent advances in artificial intelligence (AI) to create software for comprehensive placental assessment from images of gross placentas. The software could address the need for widespread, simple placenta assessment, particularly when information is needed urgently, pathologists are not highly trained for placental pathology, or where resources only allow a small fraction of placentas to be reviewed. This software has the ability to strengthen pathology exams by standardizing and enhancing the data collected at the gross level, providing better information to pathologists for diagnoses. The immediate information could impact clinical care before hospital discharge, and ease-of-use will allow inclusion in pregnancy research.
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Researcher:
Silverman, Justin
Sponsoring Agency: National Institute of General Medical Sciences
DMS/NIGMS 1: Addressing Measurement Limitations for Sequence Count DataLearn more...
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Researcher:
Abdullah, Saeed
Sponsoring Agency: National Institute of Mental Health
Financial Activity Data as an Objective Behavioral Marker in Bipolar Disorder: A Feasibility and Acceptance StudyLearn more...
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Researcher:
Honavar, Vasant
Sponsoring Agency: National Science Foundation
The project will develop a family of scalable deep kernel gaussian process regression algorithms for interpretable predictive modeling from high dimensional, sparsely and irregularly time sampled, longitudinal data with complex, a priori unknown correlation structure. The resulting methods will be able to discover the patterns of transitions between unobserved or hidden states, account for abrupt discontinuities in outcomes. They will be able to explain their predictions by learning the underlying complex correlation structure exhibited by the data and by identifying not only the variables that drive the predictions, but also the temporal context in which they do so. The project will rigorously empirically evaluate the resulting methods with simulated longitudinal data (with different correlation structures, different missingness mechanisms, different time-dependent variable importance), several benchmark longitudinal data sets, and, most importantly, deidentified longitudinal electronic health records data and socio-demographic data from real-world healthcare applications (in collaboration with clinical experts).
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Researcher:
Silverman, Justin
Sponsoring Agency: National Science Foundation
Investigation into effects of prepartum aspirin in modulating local inflammation and microbial populations in the bovine reproductive tractLearn more...
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Researcher:
Giles, C. Lee
Sponsoring Agency: National Science Foundation
The linguistic sophistication of technology has not kept pace with the growing linguistic diversity within the U.S., even though that technology is intended to improve the lives of humans and society at large, and people increasingly depend on technology for access to governmental, community, health and educational services. To address the discrepancy, this National Science Foundation Research Traineeship award to the Pennsylvania State University will educate a new generation of experts in human-technology interaction. The traineeship anticipates providing a unique and comprehensive two-year training to 48 graduate students, including 23 funded trainees, from graduate programs in Psychology, German, Spanish, Communication Science and Disorders, Computer Science and Engineering, Information Sciences and Technology, and Learning Design and Technology, to address key challenges in human-technology interaction to ensure the full participation of individuals with diverse language backgrounds, thereby fostering an equal, diverse, and inclusive society.
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Researcher:
Ma, Fenglong; Huang, Sharon
Sponsoring Agency: National Institute on Aging
We propose a new research paradigm aimed at addressing scientific questions in both biosensing and machine learning for the early prediction of Alzheimer’s disease (AD), and at solving a grand challenge in the identification of minimally-invasive AD biomarkers in tear, saliva, and blood. Our goal is to develop a novel and minimally-invasive system that integrates a multimodal biosensing platform and a machine learning framework, which synergistically work together to significantly enhance the detection accuracy.
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Researcher:
Honavar, Vasant
Sponsoring Agency: National Center for Advancing Translational Sciences
The overarching goals of Penn State Clinical and Translational Science Institute’s (CTSI) Informatics Core are to: 1) support a state-of-the-art, secure and user-friendly data infrastructure; 2) provide cutting-edge data science tools, methods and expertise; and 3) enhance our Information Commons’ capacity to advance informatics education and expertise through a collaborative culture and data-driven quality improvement. To date, the Core has substantially advanced standardization, integration and governance on disparate data sets, including electronic medical records, outcomes, environmental and social determinants, behavior, genetics, insurance claims, and public health surveillance information. The Core supports multiple common data models and institutional standard analyses files for clinical cohorts. To meet our growing clinical research data needs, our CTSI has secured additional resources to build rapid extract-transform-load (ETL) capability and to leverage informatics expertise across the University. These efforts are being tracked to assess whether and how they facilitate translational research across disciplines and domains. In addition, we monitor activities to optimize data quality, data governance, cybersecurity regulation compliance, privacy protection and research ethics. In data sciences, we develop and disseminate novel analytical tools and methodologies and track the success of our efforts to improve access to de-identified patient data for cohort query analyses.
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