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…

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…

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…

Researchers at NewHaptics Corporation and the University of Illinois at Urbana-Champaign are developing and testing software and hardware tools for faculty, staff, postdoctoral research fellows, and graduate students who are blind or have low vision, and who are working in college and university Chemistry, Mathematics, and Computer Science and Engineering and Information Sciences employment…

This project aims improve upon a tool clinicians, researchers, and systematic reviewers use to retrieve biomedical articles from bibliographic databases. Associate Professors Halil Kilicoglu and Jodi Schneider will work with Affiliate Professor Neil Smalheiser, professor of psychiatry at the University of Illinois Chicago, on the project, which has received funding from the National Institutes…

The Midwest Big Data Hub (MBDH) is a network of regional institutions created to facilitate collection, management, and use of complex information, creating active partnerships of experts and resources to address issues relevant to life in the Midwest.

MBDH aims to help member organizations working in Big Data coordinate…

Catastrophic events such as Fukushima and Katrina have made it clear that integrating physical and social causes of failure into a cohesive modeling framework is critical in order to prevent complex technological accidents and to maintain public safety and health. In this research, experts in Probabilistic Risk Assessment (PRA), Organizational Behavior and Information Science and Data…

Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of real world applications, ranging from security to manufacturing, from healthcare to crowdsourcing. Many high-impact data mining applications exhibit the co-existence of multiple types of heterogeneity, such as different classification tasks, different data sources, and different labeling oracles.…

Changes of a galaxy's properties over time are driven by the quantity of its cold gas, the raw material from which stars form. Thus, understanding the properties of a galaxy's cold gas component will tell us both how the star formation process changes over time and how this affects galaxies. Using a recently completed survey of carbon monoxide gas in a sample of nearby galaxies, the project…

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…

A great challenge in astrophysics is to understand in detail how the initial smooth distribution of matter in the early Universe formed the first galaxies. Complementing observations of real galaxies, researchers use computational simulations to model the early Universe and study the results. This process allows one to learn how these first galaxies might have formed. However, the sheer size…

Scientific discovery across the physical sciences is increasingly dependent on the analysis of volumetric - or three-dimensional - data, that may come from a supercomputer simulation, direct measurement, or mathematical models. Researchers typically seek to extract meaningful insights from this data by visualizing and analyzing it in various ways. The ways in which scientists process…

COMBINI (connecting Complementary Medicine and Biological kNowledge to support Integrative Health) is a collaboration between the National Center for Supercomputing Applications (NCSA) at the University of Illinois, the University of Minnesota, and Mayo Clinic in Florida. Illinois serves as the primary site.

The goal of the project is to develop informatics resources and scientific…

This project investigates the problem of information integrity, that is, identifying faulty or ungrounded information online. It focuses on a specific domain, that of information produced during the COVID-19 pandemic, and processes both text and image data. While significant efforts in artificial intelligence (AI) and machine learning (ML) have addressed information integrity in this type of…

The University of Illinois, in collaboration with multiple community college and public libraries, will develop a data storytelling toolkit for libraries (DSTL) to support meaningful and effective data communication, empowering libraries to utilize the rapidly growing data landscape. DSTL will connect real-world examples of data use with data stories (including narrative strategies and data…

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…

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.

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…

This project will study diabetes dedicated social networks. It aims to harness diabetes patients' online social behaviors from multiple networks to predict their biomarker measurements such as glycated hemoglobin and fasting blood glucose. This project will provide a paradigm shift from exploration to prediction compared with state-of-the-art research on diabetes dedicated social networks,…

It is difficult to understand the effectiveness of various treatment options when a huge number of external factors such as lifestyle, diet, and environment affect the burden of a disease. A major barrier to understanding is the challenge of scale—sampling enough patients to separate the major, minor, and negligible factors. With access to a database of more than one trillion public social…

This project is a collaboration with University of North Carolina (main site) and University of Colorado. Halil Kilicoglu serves as a co-PI and the lead at the University of Illinois. 

Systematic reviews are used to develop clinical guidelines, to set research priorities, and to help patients and clinicians make healthcare decisions. Systematic reviews of drugs should identify harms…

INDICATOR is a novel information system for collecting, integrating, and analyzing data from multiple sources to provide public health decision makers real-time data on the health of their community. Data comes from sources as varied as emergency department visits, school attendance, veterinary clinics, and social media postings and together have been used to change public policy in outbreak…

The U.S. scientific workforce is aging - the average age of both US academics and medical school faculty increased to the late 40s, from the early 40s in 1970. This aging is troubling because people are seen to make important scientific contributions early in their careers. Moreover, the U.S. is turning to innovation as an economic driver, and the aging of the population will both increase and…

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…

This project will research the needs of professional data curators and blind patrons. In partnership with the National Center for Supercomputing Applications, Posit Public Benefit Corporation, the Chart2Music open-source project team, the Data Curation Network, and the National Federation of the Blind, JooYoung Seo will develop a multimodal data representation system. Through needs assessments…

Copyright law and resource licensing complicate the application of text data mining for research. This project convened a National Forum on Text Data Mining with Use-Limited Data in April 2018 that brought together 25 leading stakeholders selected among researchers, librarians, content providers, legal experts, and representatives of scholarly societies to articulate an agenda that provides…

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…

This project will develop a mobile sensor technology for performing detection and identification of viral and bacterial pathogens. By means of a smartphone-based detection instrument, the results are shared with a cloud-based data management service that will enable physicians to rapidly visualize the geographical and temporal spread of infectious disease. When deployed by a community of…

Infectious airborne pathogens and their spread have caused increased incidences of outbreaks and pandemics, which challenge our society with severe impacts on public health, economies, and social well-being. Controlling airborne pathogens is challenging because they are transmitted easily via respiratory aerosols, spread quickly over large distances, and can mutate to become more infectious…

This project will focus on real-world problems where underrepresented, rare (abnormal) examples play critical roles, such as defective silicon wafers resulting from a new semiconductor manufacturing process and rare but severe complications (e.g., kidney failure) among diabetes patients.

"This problem of explainable rare category analysis was motivated by my collaboration with IBM…

Recent research has demonstrated that investigations of contemporary societal problems can benefit from the use of long-term environmental data and from comparisons with cases in which the interactions of human societies with their environments is well-documented over centuries. By providing easy access to time- and place-specific long-term environmental data, this project seeks to facilitate…

Music prints and manuscripts created over the past thousand years sit on the shelves of libraries and museums around the globe. As these organizations digitize their collections, images of these scores are increasingly accessible online. However, the musical content remains difficult to search.

Google Books and HathiTrust have already made it possible to search the content of text…

Monitoring drinking water contamination is vitally important to inform consumers about water safety, identify source water problems, and facilitate discussion of public health and the environment of our drinking water. The overall goal of this project is to develop a framework for reliable and timely detection of drinking water contamination to build sustainable and connected communities. It…

This project will create both a master’s and doctoral-level specialization in Socio-technical Data Analytics (SODA). Partnerships with local researchers and businesses who already work with large data-sets will enable master's graduates to receive first-hand experience with both the social and technical implications of large digital data collections, and thus be well-prepared for leadership…

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…

Scholarly publications today are still mostly disconnected from the underlying data and code used to produce the published results and findings, despite an increasing recognition of the need to share all aspects of the research process. As data become more open and transportable, a second layer of research output has emerged, linking research publications to the associated data, possibly along…

This project will study foreign influence via the lens of disinformation on news media from a computational perspective. The researchers will use Explainable Heterogeneous Adversarial Machine Learning (EXHALE) to address the limitations of current techniques in terms of comprehension, characterization, and explainability.

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This NIH-funded project focuses on machine learning approaches for translating transdermal alcohol content (i.e., alcohol measured from a person’s skin) into blood alcohol content (“BAC”). Modern transdermal sensors are small, easy to use, and measure transdermal alcohol content frequently, but lag behind typical measures of BAC (especially breathalyzers) in terms of accuracy. This project…

Graph Neural Networks have proven to be a powerful tool for harnessing graph data, which is widely used for representing rich relational information in multiple areas. However, the performance of graph neural networks largely depends on the amount of labeled data, which is subject to an expensive and time-consuming annotation process. This creates data without labels, or a label scarcity.…

The yt project aims to produce an integrated science environment for collaboratively asking and answering astrophysical questions. To do so, it will encompass the creation of initial conditions, the execution of simulations, and the detailed exploration and visualization of the resultant data. It will also provide a standard framework based on physical quantities interoperability between codes…