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.

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Current Projects

Researcher:
Wu, Dinghao
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:
Wang, James
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:
Wilson, Shomir
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:
Chen, Jinghui
Sponsoring Agency: Cisco Systems, Inc.
This project proposes to understand this adversarial robustness in anomaly detection via devising algorithms that could exploit this weakness and develop principled ways to mitigate the potential risk. It also proposes solutions for handling discrete or categorical data which is common in real-world applications. The proposed research could lead to much more reliable anomaly detection algorithms for cybersecurity in practice. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Chen, Jinghui
Sponsoring Agency: Cisco Systems, Inc.
This project proposes to understand this adversarial robustness in anomaly detection via devising algorithms that could exploit this weakness and develop principled ways to mitigate the potential risk. It also proposes solutions for handling discrete or categorical data which is common in real-world applications. The proposed research could lead to much more reliable anomaly detection algorithms for cybersecurity in practice. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Hanrahan, Benjamin
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:
Mitra, Prasenjit
Sponsoring Agency: The REMADE Institute
This project will address a critical barrier to the financial and environmental viability of plastic chemical recycling: an inability to design and operate efficient processes that reliably achieve target material, energy, and emissions benefits despite geo-temporal variation of composition and quality of plastic streams. This barrier stems from a lack of mechanistic understanding of the process components. We will develop a mechanistic understanding of sequential fast pyrolysis and catalytic upgrading of a mixture of polyester, i.e. polyethylene terephthalate (PET), and polyolefin, i.e. polypropylene (PP), to valuable chemicals. The mechanistic understanding gained in this work will allow the development of microkinetic reaction models, which can be incorporated into reactor simulations for process optimization and translate the knowledge obtained in the laboratory to the manufacturing industry. Our team will utilize experiments, data science and theory to overcome fundamental science and engineering challenges in mixed plastic upcycling. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Mitra, Prasenjit
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:
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Carroll, John M.; Rosson, Mary Beth
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:
Carroll, John M.; Rosson, Mary Beth
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:
Hosseini, Hadi
Sponsoring Agency: College of IST
This project aims to address the shortcomings of voting in digital platforms by developing novel algorithmic solutions for aggregating the opinion of the crowd when information about preference data is incomplete, noisy, or contains misinformation. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Lee, Dongwon
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:
Lee, Dongwon; Wang, Suhang
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:
Lee, Dongwon; Wang, Suhang
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:
Ma, Fenglong
Sponsoring Agency: Sony Corporation of America
We propose a novel, adaptive, and general framework for federate semi-supervised learning, which is a new research paradigm aiming to exploit the power of unlabeled data ignored by existing federated supervised learning. Introducing unlabeled data to federated learning brings several new challenges, which limits the applicability of existing models. In this proposal, we define the federated semi-supervised learning problem from the insight of data regularization and analyze the new-raised difficulties. This project creates new fundamental knowledge in both federated learning and semisupervised learning fields and will significantly advance these two fields by developing novel methods and implementing analytic tools. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Squicciarini, Anna
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:
Honavar, Vasant
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:
Honavar, Vasant
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:
Kou, Yubo
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:
Squicciarini, Anna
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:
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Chen, Jinghui
Sponsoring Agency: College of IST
This project seeks to new understandings of the vulnerabilities of distillation based federated learning – a machine learning technique, where local clients collaboratively train a global model without sharing clients' original data – and help build more robust federated learning methods for security-critical applications. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Squicciarini, Anna; Lee, Dongwon
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:
Squicciarini, Anna; Lee, Dongwon
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:
Wilson, Shomir
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:
Squicciarini, Anna
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:
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Xiong, Aiping; Lee, Dongwon
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:
Xiong, Aiping; Lee, Dongwon
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:
Yadav, Amulya
Sponsoring Agency: Army Research Office
The goal of the proposed research is to develop proactive approaches for handling systemic biases in machine learning datasets by tackling the following question: Can we build better crowdsourcing systems which are robust to handling subjective biases of human crowd-workers such that datasets derived from such systems are bias-free (thereby leading to unbiased ML models)? In other words, we propose to build robust crowdsourcing systems which can ensure that they generate datasets which are bias-free at the time of creation. As a result, ML algorithms trained in the future (on such datasets) do not have to worry about bias in their training. More specifically, we propose to achieve this goal by designing optimal ways of incentivizing (or dissuading) human crowd-workers so that it is in their best interests (from a utilitarian perspective) to not let their personal biases affect their annotation process, thereby resulting in bias-free data. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Wang, James
Sponsoring Agency: College of IST
This project aims to develop state-of-the-art computer vision algorithms to analyze high-resolution images of Monet paintings that will shed new light on the works’ style and open new narratives about Impressionist painting. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Wang, Suhang
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:
Billah, Syed
Sponsoring Agency: College of IST
This project aims to design accessible segmentation algorithms – partition an image into meaningful regions, assign labels to each region, and are widely used in downstream computer vision tasks – to unlock their potential for people with disabilities. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Human-Computer Interaction
Term: -
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Social and Organizational Informatics
Term: -
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence, Privacy and Security
Term: -
Researcher:
Song, Linhai
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:
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. Learn more...
Research Areas: Data Sciences and Artificial Intelligence
Term: -
Researcher:
Hanrahan, Benjamin
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:
Honavar, Vasant
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: -