Artificial Intelligence and Machine Learning

Researching the models, methods, uses, and impact of intelligent systems design for processing data and information

Researchers Working in this Area

Related Research Projects

Advancing STEM Online Learning by Augmenting Accessibility with Interactive Companionship

Time frame
2021-2024
Investigator
Yun Huang
Total funding to date
$526,006.00
Funding agency
National Science Foundation

Full title: Collaborative Research: Advancing STEM Online Learning by Augmenting Accessibility with Interactive Companionship

Videos are a popular option for online learning, and captions are essential for accessibility. Two types of captions exist: typical closed captions and explanatory captions. Closed captions…

interactive companionship

AEOLIAN (Artificial Intelligence for Cultural Organizations)

Time frame
2021-2023
Investigator
Glen Layne-Worthey
Total funding to date
$49,820.00
Funding agency
National Endowment for the Humanities

Many digital archival collections are limited due to factors such as privacy concerns and copyright. AEOLIAN combines innovative AI methods and the knowledge of scholars from multiple cultural institutions to address the accessibility of these collections, ultimately making them more accessible. Additionally, the project aims to foster collaboration amongst scholars and practitioners from…

artificial intelligence

AI Institute Artificial Intelligence for Future Agricultural Resilience Management and Sustainability (AIFARMS)

Time frame
2020-2022
Investigator
Jingrui He
Total funding to date
$7,909.00
Funding agency
National Science Foundation

This project brings together researchers in AI and agriculture, combining their expertise to promote advances in agriculture through AI. AIFARMS mission is to use core AI research areas such as computer vision, machine learning, data science, soft object manipulation, and intuitive human-robot interaction to address major challenges in agriculture:

  • Sustainable intensification…
AI farms

Assessing Impact Patterns in Research Texts Applying Corpus Driven Methods (Text Transfer)

Time frame
2020-2023
Investigator
Jana Diesner
Total funding to date
$164,732.00
Funding agency
Federal Ministry of Education and Research (Germany)

A continuation of a previous pilot study, this project aims to detect transferrable knowledge across several domains. In the pilot study, the researchers demonstrated the feasibility of this idea by using inductive and deductive methods to identify relevant impact categories and patterns for the domain of electromobility. These methods included interviews, information extraction, natural…

text transfer

Collaborative Research: Accelerating Synthetic Biology Discovery & Exploration through Knowledge Integration

Time frame
2019-Present
Investigator
J. Stephen Downie
Total funding to date
$211,699.00
Funding agency
National Science Foundation

The scientific challenge for this project is to accelerate discovery and exploration of the synthetic biology design space. In particular, many parts used in synthetic biology come from or are initially tested in a simple bacteria, E. coli, but many potential applications in energy, agriculture, materials, and health require either different bacteria or higher level organisms (yeast for…

synthetic biology

DeepCrowd: A Crowd-assisted Deep Learning-based Disaster Scene Assessment System with Active Human-AI Interactions

Time frame
2021-2023
Investigator
Dong Wang
Total funding to date
$499,786.00
Funding agency
National Science Foundation

Full title: CHS: Small: DeepCrowd: A Crowd-assisted Deep Learning-based Disaster Scene Assessment System with Active Human-AI Interactions

This project addresses the application of AI to disaster scene assessment (DSA). AI currently has limited success with DSA; this project…

DeepCrowd

Harnessing Artificial Intelligence for Cartel Smuggling Study

Time frame
2019-2020
Investigator
Jingrui He
Total funding to date
$78,917.00
Funding agency
Arizona State University and the Department of Homeland Security

This joint effort with Arizona State University’s CAOE team aims to create a suite of effective and efficient AI tools for analyzing cartel smuggling activities, building upon the team’s expertise in machine learning, data mining, and visual analytics.

U.S. Coast Guard

Identifying False HPV-Vaccine Information and Modeling Its Impact on Risk Perceptions

Time frame
2020-Present
Investigator
Jessie Chin
Total funding to date
$389,810.00
Funding agency
National Institutes of Health

Human papillomavirus (HPV) is the most common sexually transmitted infection in the U.S., with over 34,000 new HPV-related cancers diagnosed annually, according to the Centers for Disease Control and Prevention. An HPV vaccine, which was approved by the Food and Drug Administration (FDA) in 2006, is recommended as part of routine vaccinations for school-aged children. However, the vaccine's…

network

Machine Learning Modeling for the Reactivity of Organic Contaminants in Engineered and Natural Environments

Time frame
2021-Present
Investigator
Dong Wang
Total funding to date
$150,001.00
Funding agency
National Science Foundation

With support from the Environmental Chemical Sciences Program of the NSF Division of Chemistry, the researchers will develop machine learning models to predict the reactivity of thousands of organic contaminants (OCs) in engineered (water) and natural (soil and sediment) environments. To assess and mitigate risks associated with this vast number of OCs, accurate predictive models are needed to…

topography map

Natural Language Processing to Assess and Improve Citation Integrity in Biomedical Publications

Time frame
2022-Present
Investigators
Halil Kilicoglu, Jodi Schneider
Total funding to date
$300,000.00
Funding agency
Office of Research Integrity, U.S. Department of Health and Human Services

This project will assist researchers and journals in evaluating citation behavior in biomedical publications. While citations play a fundamental role in the diffusion of scientific knowledge and assessment of research on a topic, they are often inaccurate (e.g., citation of nonexistent findings, inappropriate interpretation). This inaccuracy undermines the integrity of scientific literature…

journals spines

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