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:
Wang, James
Sponsoring Agency: National Endowment for the Humanities
After Constable’s Clouds will use computer vision to enhance art historical understanding of 19th-century Realism. The emergence of Realism in French landscape painting is often linked to the 1824 exhibition in Paris of John Constable’s unidealized view of the English countryside, The Hay Wain. Viewers particularly noted the veracity of Constable’s clouds. Indeed, our computational research shows that Constable’s clouds are more closely modeled on the structure of actual clouds than those of his contemporaries, with French academician Pierre-Henri de Valenciennes a near rival. Valenciennes taught a generation of landscape artists, emphasizing the importance of plein-air sky studies, yet histories of French landscape tend to cast Constable as Realism’s catalyst. After Constable’s Clouds will test this historiography by using computer vision to classify and compare the clouds in paintings by Barbizon, Realist, and Impressionist painters with those of Constable and Valenciennes.
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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:
Laszka, Aron
Sponsoring Agency: U.S. Department of Energy
AI-Engine for Optimizing Integrated Service in Mixed Fleet Transit OperationsLearn more...
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Researcher:
Kim, Taegyu
Sponsoring Agency: National Security Research Institute
Automated Vulnerability Discovery and Crash Investigation System Guided by Unmanned Aerial Vehicle Control ModelLearn more...
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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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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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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...
Term:
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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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Term:
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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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Term:
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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:
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:
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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Term:
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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:
Huang, Sharon
Sponsoring Agency: National Science Foundation
The goal of the research team is to develop a convergent device platform that can rapidly capture, sense, and identify viruses and predict new antigenic strains against which the human population has limited or no immunity. The project pursues a solution to a grand challenge in the surveillance and characterization of circulating epidemic and pandemic influenza virus strains by addressing deep scientific questions in enhanced Raman spectroscopy for virus detection and evolution prediction. The proposed platform is based on controlled virology experiments that propagate by culture and mutate viruses for specific research tasks, a novel virus enrichment platform for effectively capturing viruses without labels, biosensing of virus surface proteins with enhanced signal through a novel 2D/metal enhanced Raman spectroscopy technique, and rapid and sensitive virus identification and evolution prediction through machine learning analysis of enhanced Raman data.
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Term:
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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:
Maulik, Romit
Sponsoring Agency: U.S. Department of Energy
Inertial neural surrogates for stable dynamical predictionLearn more...
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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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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:
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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Term:
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Researcher:
Rajtmajer, Sarah
Sponsoring Agency: Air Force Office of Scientific Research
The project will develop a unifying mathematical foundation by which to represent psychological elements in behavioral game theory. Proposed models will allow artificial agents engaged in strategic social planning to use representations of people, places and things that vary in their level of abstraction. This process is posited by Construal Level Theory and thought to support memory consolidation and planning.
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Term:
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Researcher:
Wang, Suhang
Sponsoring Agency: Army Research Office
As new generalizations of traditional deep neural networks to graph structured data, Graph Neural Networks (or GNNs) have demonstrated the power in graph representation learning and have permeated numerous areas of science and technology. However, GNNs also inherited drawbacks of traditional deep neural networks including lack of interpretability and vulnerable and unstable to adversarial attacks.
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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:
Wang, Suhang
Sponsoring Agency: National Science Foundation
This project proposes novel principles and mechanisms for scalable and interpretable graph neural networks to facilitate the adoption of GNNs on critical domains, investigates associated fundamental research issues and develops effective algorithms. The project offers the first comprehensive investigation on these directions, and the designed novel methodologies and tasks will deepen our understanding on the inner working mechanisms of GNNs and contribute to real-world applications.
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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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Term:
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Researcher:
Honavar, Vasant
Sponsoring Agency: National Center for Advancing Translational Sciences
UL1 - Penn State Clinical and Translational Science InstituteLearn more...
Term:
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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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