Current Research Projects

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.

|||||||||

Current Projects

Researcher: Dinghao Wu
Sponsoring Agency: Office of Naval Research
We are considering a radically different approach to binary reverse engineering tools by placing the recompilability as the first and topmost goal. We will further develop our preliminary study on Reassembleable Disassembling, with the similar design goal to preserve the recompilability while lifting the code to higher level languages or intermediate representations.   Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Dinghao Wu
Sponsoring Agency: National Science Foundation
This project tackles the challenge of binary code analysis by proposing several advanced methods that combine techniques from behavior and semantics perspectives. The proposed methods leverage formal program semantics, symbolic execution, automated constraint solving, and algorithmic memorization of code semantics that form solid foundations with rigorous resilience properties to latest attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: James Wang
Sponsoring Agency: Amazon Research Awards – Robotics Program
In this project, the team will conduct fundamental research to advance bodily expressed emotion understanding using an interdisciplinary approach crossing computing, statistical learning, and movement analysis. Breakthroughs in emotion understanding technologies have the potential to enable many innovative applications. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction
Term: -
Researcher: Daniel Susser
Sponsoring Agency: Microsoft Corporation
Important decisions are increasingly automated, delegated to algorithms susceptible to bias and other flaws. In order to guard against problems associated with automated decisions, it is often suggested that there should be a “human-in-the-loop”—i.e., some form of human review—at least in the case of high stakes decisions. But human decision-making is susceptible to its own biases and flaws, as well as to external influence. Today this includes a range of automated influences, such as targeted advertising, recommender systems, AI assistants, and digital nudges. While existing discussions tend to frame questions about these decision processes in binary terms—automated or not—this project aims to understand the normative implications of increasingly blended forms of human-machine decision-making. What ethics and policy questions are raised by decision-making systems that benefit from the strengths of both human and machine deciders, but also are subject to the weaknesses of each? How can ethics and policy guide a world in which individually and socially important decisions are reached by blended human-machine deciders? Learn more...
Research Areas: Ethics, Social and Organizational Informatics
Term: -
Researcher: Shomir Wilson
Sponsoring Agency: National Science Foundation
This multi-disciplinary project aims to develop novel technology that will enable people to regain a sense of control by enabling them to simply ask questions about the privacy issues that matter to them rather than requiring them to read long, one-size-fits all privacy policies. This multi-disciplinary project aims to re-invent notice and choice, moving from long and hard-to-understand notices to interactive privacy dialogues with users. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Benjamin Hanrahan
Sponsoring Agency: National Science Foundation
This project studies the ways that algorithmic management, using digital tools to automate and remotely manage workers, may negatively impact workers and their rights. The research will look specifically at ride-hailing platforms, which are rapidly replacing traditional taxi services. Researchers will develop an experimental ride-hailing platform that gives drivers and passengers control over parameters that impact algorithmic outcomes, as a means to understand and interact with the platform. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction, Social and Organizational Informatics
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: National Science Foundation
The goal of this project is first to learn an adversarial agent informed by explainable AI. Using this learned agent, we then unveil the weakness of target agents and adversarially train them accordingly. Through a robustness check, we evaluate the enhanced agents. If a strengthened agent fails the adversary-resistance check, we fall back on formal verification and program synthesis techniques. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Prasenjit Mitra
Sponsoring Agency:
This project investigates the cognitive and motivational factors that support deep engagement with teacher’s data and which drive change. Specifically, we are investigating three outcomes: change in teachers’ beliefs regarding classroom discussion, change in teachers’ knowledge of effective strategies, and change in teachers’ behavior regarding the implementation of these strategies. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Social and Organizational Informatics
Term: -
Researcher: Dongwon Lee
Sponsoring Agency: National Science Foundation
This collaborative project between Penn State and Tuskegee University proposes to improve the solutions for the intrusion detection tasks by means of the Anomaly Detection framework in cyber systems by incorporating recent advancements in big data and machine learning techniques. In this project, we explore how to advance existing Anomaly Detection Systems (ADSs) to prevent more diverse and challenging types of network intrusions with higher detection accuracies. Recent advances in big data and machine learning, especially deep learning, provide an unprecedented opportunity for building highly effective ADSs. Therefore, the team will investigate methods in various data science and machine learning fields, and seek to exploit them in the context of network intrusion detection. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Ting Wang
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. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Vasant Honavar
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. Learn more...
Research Areas: Biomedical and Health Informatics, Data Sciences and Artificial Intelligence
Term: -
Researcher: Hadi Hosseini
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: National Science Foundation
In this proposal, we ask one critical question --- assuming we will never have representative labels, what can we do to significantly improve the adaptability and resilience of learning-based defenses with extremely limited labeling capacity? While the problem looks challenging, recent progress in self-supervised learning has shown great promise to perform complex learning tasks with limited labels. Self-supervision is about designing pretext learning tasks to better utilize unlabeled data and obtaining supervision from the data itself. While most existing efforts focus on computer vision and natural language process tasks, we believe some of the fundamental ideas can significantly benefit the security community to address the concept drift problem. In this proposal, we want to combine the idea of self-supervision with the domain-specific insights in malware detection to build new solutions to combat concept drift. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: John M. Carroll, Zihan Zhou, Mary Beth Rosson
Sponsoring Agency: National Library of Medicine
The project will investigate prosthetic support for people with visual impairment (PVI) that integrates computer vision-based prosthetics with video-mediated human-in-the-loop prosthetics. We will employ a human-centered design approach, identifying a set of key assistive interaction scenarios that represent authentic needs and concerns of PVIs. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction, Social and Organizational Informatics
Term: -
Researcher: Zhenhui (Jessie) Li
Sponsoring Agency: National Science Foundation
This project develops novel data mining techniques to help people uncover the complicated correlations in the big urban data. Ultimately, this project strives to advance the techniques in urban computing, a nascent interdisciplinary research field that addresses the challenges and opportunities in the fast-evolving urban environments. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Social and Organizational Informatics
Term: -
Researcher: Nicklaus Giacobe, Mike Hills
Sponsoring Agency: National Security Agency
The CYSP program provides scholarship opportunities to College of IST students interested in pursuing employment with the Department of Defense. It also provides faculty with the opportunity to compete for capacity building project funds to develop educational products of general use to the wider Center for Academic Excellence community. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Andrea Tapia
Sponsoring Agency: National Science Foundation
The project investigates the use of big data analysis techniques for classifying crisis-related data in social media with respect to situational awareness categories, such as caution, advice, fatality, injury, and support, with the goal of helping emergency response teams identify useful information. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction, Social and Organizational Informatics
Term: -
Researcher: Dongwon Lee
Sponsoring Agency: National Science Foundation
In this project, we propose a flexible framework, named as SAGA, where scholars can easily create cybersecurity case studies (similar to business case studies) that have AI components. Further, by adopting the notion of “citation” in academic world and implementing it using public platforms (e.g., arXiv, Github, Kaggle), SAGA enables the developed case studies to be easily found and shared in the community, and the authors of case studies to be rightfully attributed for their efforts, thereby encouraging more participation from scholars in creating and sharing case studies. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Suhang Wang, Dongwon Lee
Sponsoring Agency: National Science Foundation
This project explores effective labeled data generation via generative adversarial learning and proposes novel approaches based on generative adversarial learning for effective labeled data generation to facilitate deep learning with limited label information, investigates associated fundamental research issues and develops effective algorithms. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher: Anna Squicciarini
Sponsoring Agency: National Science Foundation
This project investigates the complex chaotic behaviors that can emerge as a result of evolutionary dynamics on networks, develops techniques for dynamic control, and studies the problems associated with privacy and fairness among agents in these systems. The work focuses on semi-autonomous agents that interact with each other in a network and alter their instantaneous mixed strategies through evolutionary dynamics, with an emphasis on flocking and consensus dynamics. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Linhai Song
Sponsoring Agency: National Science Foundation
This project aims to facilitate informed vaccination decisions. The research aims to illuminate how people’s vaccination decisions evolve in response to their social context. The project’s novelty is to follow participants over time to evaluate their real-world decision-making about vaccination. The project will advance the state-of-the-art on risk communication during crises and decision-making under uncertainty. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Biomedical and Health Informatics, Privacy and Security
Term: -
Researcher: Vasant Honavar
Sponsoring Agency: National Science Foundation
This project brings together an interdisciplinary team of researchers with complementary expertise in AI and Material Science to launch a planning effort to lay the groundwork for an AI-Enabled Materials Discovery, Design, and Synthesis (AIMS) Institute. AIMS will produce AI advances and technologies that yield not only transformative advances in materials design, discovery and synthesis, but also provide organizing frameworks, infrastructure, collaborative human-AI systems and tools, and best practices to dramatically accelerate scientific discovery, but also enable new modes of discovery across diverse scientific domains. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Vasant Honavar
Sponsoring Agency: National Science Foundation
In high stakes applications of machine learning, the ability to explain the machine learned model is a prerequisite for establishing trust in the model’s predictions. Satisfactory explanations have to provide answers to questions such as: "What features of the input are responsible for the predictions?"; "Why are the model’s outputs different for two individuals?" (e.g., Why did John’s loan application get approved when Sarah’s was not?). Hence, satisfactory explanations have to be fundamentally causal in nature. This project will develop a theoretically sound, yet practical approach to causal attribution, that is, apportioning the responsibility for a black-box predictive model’s outputs among the model’s inputs. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Yubo Kou
Sponsoring Agency: National Science Foundation
This is a study of human implications of online moderation systems that deal with disruptive online behaviors, such as offensive language and hate speech, by issuing penalties such as content removal or account suspension to users they determine to be disruptive. The study site is a high-population online community, where the research will document and describe human-punishment interaction in terms of how users experience punishment, what are users' post-penalty actions, and what support resources users use for a better understanding of community behavioral standards and behavioral improvement. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction
Term: -
Researcher: Anna Squicciarini, Peter Forster
Sponsoring Agency: National Science Foundation
This project aims to build mathematical and data-driven models to understand the dynamics of extremist groups at scale, the patterns of their influence, and integrated micro (individual-level) and macro (group-level or system-level) data-driven models that can guide future interventions. This project provides a greater understanding of users' behavioral patterns and social dynamics related to online extremism. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher: C. Lee Giles
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Ting Wang
Sponsoring Agency: International Business Machine Corporation
The goal of Hardening and Orchestrating Responses Under Stress (HORUS) project is to enable dynamic response and adaptive protection for cyber hunting scenarios, leveraging (1) a cognitive threat analysis process to choose from effective protective responses and actions, (2) a game-theoretic model for action selection and attack anticipation, and (3) adversarial analysis of threats and hardening of detectors. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Dongwon Lee, Anna Squicciarini, Peter Forster
Sponsoring Agency: National Science Foundation
This project will expand the capability and involvement of Penn State students State in cyber-relevant disciplines. To support student needs, we have implemented a flexible and strong Scholarship for Service (SFS) program, based on customized mentoring for each student. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Dongwon Lee
Sponsoring Agency: Sungkyunkwan University Foundation for Corporate Collaboration
The project is to perform collaborative research to develop (1) novel AI-based knowledge tracing methods that can predict students’ probabilities to get next questions right based on their previous history of performance, and (2) AI-based solutions to auto-answer arithmetic math questions. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Shomir Wilson
Sponsoring Agency: University of Chicago
While researchers continue to study the effects of disproportionate minority contact with law enforcement on a range of health-related outcomes, a recent review of this work questions the methodological validity of most studies on this topic. Many of these concerns focus on (a) unrealistic assumptions about police behavior and (b) poor quality data. This project addresses both by introducing a human development based model of law enforcement officer (LEO) behavior and applying this model to study how LEOs identify with male minority youth (MMY) using a novel publicly available data source: broadcast police communications (BPC). Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Anna Squicciarini
Sponsoring Agency: National Science Foundation
This research project seeks to address privacy issues by developing a new location privacy framework for workers in vehicle-based spatial crowdsourcing. The project will start with developing new adversarial models to capture the network-constrained mobility features of multiple vehicles operating over roads. As a countermeasure of the adversarial models, the project will develop a new location obfuscation paradigm that can effectively protect vehicles' location privacy even assuming that adversaries use vehicles' mobility features for inference attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Shomir Wilson, C. Lee Giles
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Shaowen Bardzell, Jeffrey Bardzell
Sponsoring Agency: National Science Foundation
This research will provide rich insights into a new line of regional experiments with computer-based economic development in the American Midwest, rooted in collaborations between government, industry, and universities to drive their traditions of manufacturing excellence into the next generation. The research will inform the development of sociotechnical interventions supporting bottom-up innovation procedures and emergent outcomes. Learn more...
Research Areas: Human-Computer Interaction
Term: -
Researcher: Dongwon Lee, Aiping Xiong
Sponsoring Agency: National Science Foundation
This funding establishes a new Research Experiences for Undergraduates (REU) Site at Pennsylvania State University. An interdisciplinary team of experienced faculty mentors will guide undergraduate students in summer research projects focused on applying machine learning methods to solve cybersecurity problems, particularly cyber-attacks. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security, Social and Organizational Informatics
Term: -
Researcher: Shaowen Bardzell, Jeffrey Bardzell
Sponsoring Agency: National Science Foundation
This research integrates two goals: to survey, document, and disseminate knowledge about innovation in alternative agriculture to the human-centered computing community, and to support alternative-agricultural innovation practices using system design approaches to develop novel technologies that build on and extend what is already happening on a diversity of experimental farms. Learn more...
Research Areas: Human-Computer Interaction
Term: -
Researcher: Sarah Rajtmajer
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Suhang Wang
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Suhang Wang
Sponsoring Agency: Army Research Office
We systematically investigate the primary directions of Graph Neural Networks (GNNs) including new mechanisms to interpret GNNs, and ingenious strategies to attack and secure GNNs. Each direction will dramatically extend the frontier through not only studying original problems, but also developing innovative solutions. The significance of the project lies in the fact that the project offers the first comprehensive investigation on these new frontiers and the designed novel methodologies and tasks will deepen our understanding on the inner working mechanisms of GNNs and contribute to real-world applications. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: National Science Foundation
This project will provide much-needed automation for reproducing kernel bugs and vulnerabilities. If successful, the project will significantly advance computer security (for kernel vulnerability analysis) and contribute to the field of software engineering (for bug diagnosis and assessment). By improving the reproduction rate of kernel bugs, this project will also help with other parallel efforts for vulnerability patching and remediation. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Suhang Wang
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Social and Organizational Informatics
Term: -
Researcher: Xinyu Xing
Sponsoring Agency: Office of Naval Research
This research aims to explore, design and develop a series of technical approaches to ease the development of working exploits and escalate the exploitability for vulnerabilities. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Ting Wang
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Linhai Song
Sponsoring Agency: National Science Foundation
This project aims to better understand Rust’s memory bugs and build novel static/dynamic tools to combat Rust’s memory bugs. This project contains three components: (1) a comprehensive taxonomy of Rust’s memory bugs, (2) novel static techniques to identify memory bugs in interior unsafe functions, and (3) novel fuzzing techniques enhanced by the safe/unsafe information in Rust. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher: Benjamin Hanrahan
Sponsoring Agency: National Science Foundation
This project aims to uplift workers and improve the marketplace for online work so that digital work may help with the economic recovery of regions whose traditional industries have left. This research aims to develop sustainable methods for transitioning workers to high-skilled and creative digital jobs that are unlikely to be automated in the near to medium term future. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction
Term: -
Researcher: Ting Wang
Sponsoring Agency: National Science Foundation
The transformative nature of this project is to completely rethink how to define and implement the interpretation of DNNs and how to exploit this interpretability as a bridge to understand and control the DNN behaviors. This project aims to develop RIDDLE, a new interpretable deep learning framework that is reliable, interactive, and debuggable. Learn more...
Research Areas: Privacy and Security
Term: -
Researcher: Vasant Honavar
Sponsoring Agency: Volvo Technology North America
This project will develop and evaluate a suite of machine learning algorithms for predictive maintenance of vehicles using on-board and IoT sensor data as well as weather, road conditions, driver behavior, etc. The project team consisting of the PI and a PhD student will work closely with Volvo engineers and data scientists. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher: Sarah Rajtmajer
Sponsoring Agency: Quantitative Scientific Solutions LLC
VESPID: Visualizing and Enlarging the Statistics of Publication Information under Dynamics will support the NSF’s National Center for Science and Engineering Statistics (NCSES). VESPID will develop tools to integrate the NSF’s Survey of Doctorate Recipients with bibliometric and patent data to provide insights into US doctoral recipients’ career trajectories and innovation. Work will include new approaches to dynamic network analysis and policy-relevant research studies around equity and scientific investment. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -