Sensors, satellites, scanners, other devices and the internet provide a deluge of data for researchers in multiple disciplines. Artificial intelligence is key to helping navigate it all. This evolving technology, in essence, helps connect the dots.
At the University of Maine, AI research is providing innovation, leadership and inroads needed to efficiently and effectively use the constant stream of data, and to help scholars solve long-lasting and emerging problems.
Penny Rheingans, director of the UMaine School of Computing and Information Sciences, says the growth of AI development has responded to an explosion of data, and coincides with the overall expansion of the data science field. Conducting research and serving as engaged citizens now require copious amounts of data and tools to harness it. Stakeholders in medicine, manufacturing, finance, environmental stewardship, public health, commerce, education and other areas also need to access extensive datasets.
“One of the core game changers for machine learning was that there was so much data that it was overwhelming and almost became a barrier to understanding,” says Rheingans, a professor of computer science. “It’s no longer really possible to understand what’s going on through manual approaches.”
UMaine computer scientists and engineers code software on their computers and build hardware at their benches to create AI that will perceive, reason, communicate and predict more like humans. With greater cognition, coupled with enhanced efficiency and accuracy, these technological neural networks will be able to collate copious amounts of historic and new data and use it in novel ways.
Rheingans says the abundance, interconnection and diversity of data has prompted researchers to not only develop new AI, but also find novel applications for existing software and hardware.
“It’s hard to think of an application in the world right now that doesn’t require a large amount of information,” Rheingans says. “There is also this greater potential to do what we could never do before.”
To help Maine capitalize on the social and economic benefits of this emerging field of technology, UMaine launched an initiative to make the state a hub for AI research, education and use. The endeavor, known as the University of Maine Artificial Intelligence Initiative (UMaine AI), seeks to achieve this goal by uniting experts in academia, government, industry and the community.
UMaine possesses the expertise and resources needed to develop new applications for AI that improve how researchers conduct studies and tackle problems affecting the quality of life for Mainers, Rheingans says.
The university also is poised to foster strong partnerships needed to advance AI development, and train students to excel and secure employment in this burgeoning field.
“We have the best potential to work on problems that are important to us here,” Rheingans says. “That’s why it’s important to have the expertise, to build that in-house expertise and to have partnerships.”
Research led by UMaine computer scientists and engineers is underway. Studies are creating AI technology and applications to combat disease, protect natural resources, defend against natural disasters and find new solutions for energizing communities.
Highlights of AI research at UMaine follow.
Collecting data to protect, preserve forests
New technology to enhance scientists’ understanding of the complex yet highly dynamic Northern New England forests began its first trial in a flowerpot. Ali Abedi, a professor of electrical and computer engineering at UMaine, tasked his new wireless sensor with gathering soil moisture data and sending it to a computer in his lab in April 2020, one of multiple tests for the prototype. The device, with rubber-coated wires connecting a red converter and two metal prongs, and its development will lay the groundwork for a multi-institutional effort to assess and forecast changes in 26 million acres of New England forestland.
Researchers from UMaine, the University of New Hampshire and the University of Vermont are collaborating to create a digital framework capable of gathering near real-time data about the forests spanning the northern portions of their respective states and New York — the Northern Forest Region.
The digital framework will consist of many small networks of wireless sensors spread across the region. Governed by AI, the sensors will collect data about soil moisture, soil temperature, ambient temperature, carbon dioxide, sunlight exposure and other characteristics; and communicate with each other to create a cohesive, regulated and self-monitoring network.
Abedi, who also serves as associate vice president for research and director of UMaine’s Center for Undergraduate Research, leads development of the new sensor technology that serves as the data collection part of the first phase of the multiyear interuniversity project, Leveraging Intelligent Informatics and Smart Data for Improved Understanding of Northern Forest Ecosystem Resilience (INSPIRES), which was awarded a $6 million NSF grant in 2019. This collected data will be combined with remote sensing data within a new AI tool — the “Digital Forest” — that is developed with INSPIRES funding by another team of researchers, led by Kate Beard-Tisdale, UMaine professor of spatial informatics, and Hahmann.
“I think it’s exciting because it’s close to home here in Maine,” Abedi says. “We live in the forests. It’s important to understand what we have.”
Improving efficiency, accuracy of wildlife surveys
Biologists count and identify birds in thousands of aerial photos when conducting wildlife surveys, a laborious task that consumes many hours. To reduce time spent analyzing images and the margin for error, UMaine researchers endeavor to create artificial intelligence that will perform the task.
Faculty and graduate students from several UMaine departments will develop machine learning technology that can pinpoint colonial nesting birds in photos captured by cameras mounted in unmanned aerial vehicles (UAVs) or planes.
The AI developed by UMaine researchers will use object recognition and image segmentation to determine the number of birds, their species and behaviors in aerial photos captured on Maine’s offshore islands and over inland rookeries during the spring and summer months. Their program, known as a Convolutional Neural Network (CNN), a deep learning AI algorithm typically used for visual analysis, will find and classify the birds in an image by analyzing the pixels that form them.
The project received $43,000 from the UMaine AI Initiative seed grant funding program, and builds on previously funded grants and partnerships involving UMaine faculty and state and federal agency partners.
“Humans are prone to fatigue, error,” says project lead Roy Turner, an associate professor of computer science and director of the Maine Software Agents/Artificial Intelligence Laboratory (MaineSAIL). “It takes forever to do this by hand. Graduate students can take several hours identifying birds in one image.”
Developing novel materials for energy storage
Two UMaine researchers will use AI-aided design to develop new materials for improved batteries and supercapacitors.
The research initiative led by Liping Yu, assistant professor of physics, and Yingchao Yang, assistant professor of mechanical engineering, aims to predict, synthesize and characterize a new class of 2D materials for active electrodes in batteries and supercapacitors. These 2D materials will be comprised of four or more chemical elements in nearly equal concentrations; distinct from both traditional 2D materials, which consist only of two or three elements, and conventional alloys, which contain relatively small amounts of secondary elements added to a primary element.
The U.S. Department of Energy awarded the project $750,000 through the Established Program to Stimulate Competitive Research (EPSCoR).
Yu’s research focuses on the theoretical and computational prediction of new materials with properties suitable for sustainable clean energy and electronic applications, such as solar cells, supercapacitors and catalysts. Yang’s research encompasses fabrication-property-application of novel materials, which includes synthesizing 1D and 2D nanomaterials through chemical vapor deposition, hydrothermal reaction, and other means; mechanics of nanomaterials in situ and ex situ investigated with micromechanical devices; and application of nanomaterials in energy harvest, energy storage and water treatment.
Existing energy storage devices experience limitations such as inadequate power, capacity, efficiency, life span and cost effectiveness, Yu says. To overcome such limits, new electrode materials are critically needed.
Explaining the findings reached
AI helps scientists make discoveries, but not everyone can understand how it reaches its conclusions. UMaine computer scientist Chaofan Chen is developing deep neural networks that explain their findings in ways users can comprehend, and applying his work to biology, medicine and other fields.
Interpretable machine learning, or AI that creates explanations for the findings it reaches, defines the focus of Chen’s research. The assistant professor of computer science says interpretable machine learning also allows AI to make comparisons among images and predictions from data and, at the same time, elaborate on its reasoning.
Scientists can use interpretable machine learning for a variety of applications, from identifying birds in images for wildlife surveys to analyzing mammograms.
Before joining UMaine, Chen and research colleagues at Duke University developed machine learning architecture known as a prototypical part network (ProtoPNet) to pinpoint and categorize birds in photos, then explain its findings. The ProtoPNet would explain why the bird it identified was a bird and why it embodies a particular type of bird.
Chen has begun another AI study with colleagues and students from Duke exploring how they can apply ProtoPNet to review mammograms for signs of breast cancer. He also is investigating the possibility of integrating interpretable machine learning with environmental DNA (eDNA) applications in the hope of uncovering the connections between eDNA and environmental signals.
“I want to enhance the transparency for deep learning, and I want a deep neural network to explain why something is the way it thinks it is,” Chen says.