4 UB faculty receive NSF CAREER Awards

Two faculty members in the School of Engineering and Applied Sciences, one in the Graduate School of Education and one in the College of Arts and Sciences have received awards from the National Science Foundation’s Faculty Early Career Development Program, the prestigious program that supports early-career faculty who have the potential to serve as academic role models in research and education.

Receiving CAREER awards are Ian Bradley, assistant professor in the Department of Civil, Structural and Environmental Engineering, SEAS; Virginia J. Flood, assistant professor of learning sciences, Department of Learning and Instruction, GSE; and Kaiyi Ji, assistant professor, Department of Computer Science and Engineering, SEAS, and Kristin Poinar, associate professor in the Department of Earth Sciences, CAS.

Ian Bradley: Harmful algal blooms and their impact with the environment and critical processes

Harmful algal blooms (HABs) are responsible for countless public health concerns, with an estimated economic impact in the U.S. of between $10–$100 million, according to the National Centers for Coastal Ocean Science.

Not all algal blooms are hazardous, but HABS can produce toxins and cyanobacteria, a bacteria known as blue-green algae that could cause skin irritation, nausea and liver or neurological damage. Even harmless algal blooms can cause eutrophication in water, a process where algae grow and then die after depleting the water’s oxygen. Bacteria feed on the dead algae, causing fish to die off and dead zones in the water.

Bradley, who is also a core faculty member of UB’s RENEW Institute, will use his $576,433 grant to investigate how wastewater treatment and agricultural discharge interact with HABs.

“We’re concerned about organic nitrogen and phosphorus that aren’t easily removed in wastewater treatment,” Bradley says, noting both are nutrients for algal growth. “What I want to understand is both how we can improve removing those in wastewater, and then also how they interact with harmful algal blooms out in the environment.”

Much of Bradley’s research focuses on algae and wastewater treatment. His PhD was in algal wastewater treatment, and his research group investigates algae cultivation and polyculture farming for biomass harvesting. The work in this CAREER award is the next step in his research on algae.

“Algae are great for wastewater treatment because they take up nitrogen and phosphorus very efficiently,” Bradley explains. “Algae can take up even small amounts of nitrogen and phosphorus; that’s why we get algal blooms out in the environment. The idea behind all our previous work has been to take these blooms out of the environment and get them into an engineered system and use algae to treat waste so that we have very low levels of nitrogen and phosphorus coming out of wastewater.”

This project continues those efforts and features a community outreach component as well. Bradley will work in wastewater treatment plants throughout Erie County, as well as with local community and environmental groups, including the Buffalo Niagara Waterkeeper. Bradley and his research group will connect their work to Buffalo Niagara Waterkeeper’s work measuring HABs and educating the community.

Virginia J. Flood: Embodied responsive teaching in undergraduate physics

Flood will use her $666,146 award to  investigate instructional strategies in an active, collaborative introductory undergraduate physics course where students work in small groups at vertical whiteboards to solve puzzling, open-ended problems. 

Instructors in this course use an instructional approach called responsive teaching, where they elicit, attend and respond to the substance of students’ ideas and help students connect their ideas with the discipline. In this course, instructors act as facilitators while students take the lead on modeling and solving problems. The instructor’s role is to listen and build on the ideas that students are exploring together and help guide them toward productive solutions.

Previous research has shown that responsive teaching has profound impacts on students’ STEM learning, and that students learn best when instructors carefully listen to and provide feedback on the ideas students share during problem solving, Flood explains.

But, she notes, this research on responsive teaching has primarily focused on written and spoken communication, despite the well-established role that gesture and other nonverbal, embodied communicational resources play in how students convey their ideas about STEM phenomena. 

A lot of ideas and concepts in STEM disciplines like physics are visuodynamic — requiring illustrations and animation to explain — so it’s important that instructors pay attention to all the ways students communicate information, Flood says. 

“One resource we have to communicate visuodynamic information is gesture, where we make spontaneous illustrations and animations with the movement of our hands and arms when we talk to others,” she says. “When students are learning new ideas in STEM, they convey their understanding of these new ideas not just through speech, but also through embodied communicational resources like gesture.”

The goal of this project, Flood notes, is to understand how instructors are responsive to the ways students share their ideas about physics in ways beyond words and how that impacts students’ learning over time. 

The project will contribute to the improvement of undergraduate STEM education by generating a better understanding of instructional practices that can best support students’ STEM learning and participation, she says, adding that results of the research will be used to develop training materials to help new instructors learn how to be responsive to students’ STEM ideas in ways that help engage students with problems and better support their learning.  

“By better understanding what kinds of communication best supports learning in STEM classrooms, we can help design more effective instruction and better train instructors to help more students succeed in STEM courses,” Flood says.

Kaiyi Ji: Helping AI find balance and efficiency

Artificial intelligence, particularly large language models (LLM) like ChatGPT and big data applications — think 5G networks, and health care and finance models — must often fulfil multiple completing objectives at once. Multi-objective optimization (MOO) provides a framework for these models to identify the best trade-offs among competing objectives.

Ji’s $549,999 CAREER award aims to advance MOO theory, enhancing many systems that society interacts with daily. Ji is collaborating with UB faculty to implement the new theoretical framework into LLMs and robotics, and is working with Amazon on ad recommendations using this theory.

“The research outcomes could have some impact on large foundation models, robotics and recommendation. In recommendation, users often have multiple requirements or requests,” Ji says. “For example, the user could request items that are cheap, high-quality and latest. These objectives are often conflicting, making the final decision difficult. My research wishes to strike a balanced solution that can satisfy users’ requirements as much as possible, instead of finding some items that focus only on one property like low price.”

MOO has been studied for decades, but in the era of ubiquitous large foundational models, it now faces new challenges in scalability, stability and accuracy. Ji’s work aims to address these challenges and make MOO more effective in modern, practical, AI-related applications. The broad impact his work could have on larger fields of artificial intelligence is significant, specifically with open AI platforms where users make multiple requests.

“How to find a balanced solution is important. Currently, multi-objective reinforcement learning from human feedback — reward soup — are very popular for multi-objective LLM modeling,” Ji says. “All of these tools involve dealing with multiple objectives at the same time. I believe my research will be useful there as well.”

Research efforts on this project are broken out into three complimentary thrusts, where Ji and his research team will develop new theoretical and algorithmic foundations for stochastic MOO, optimizing multiple objectives when the solution is difficult to measure; propose efficient and scalable multi-objective bilevel optimization, flexible and adaptive systems that works with MOO; and substantially advance MOO frameworks by incorporating innovative fairness concepts that are different than current approaches.

The project includes outreach activities to communicate research outcomes in educational and extracurricular settings to K-12, undergraduate and graduate students. Ji will work to develop experiential learning and undergraduate research opportunities, and a course related to this work.

Kristin Poinar: Predicting ice sheet loss more accurately with AI

Ice sheet models are complex pieces of code that simulate the behavior of the ice sheets, but they can underpredict the amount of ice these glaciers are actually losing. 

That’s because current models simply can’t simulate all the smaller-scale processes that help make a glacier’s ice flow into the sea, like the many crevasses that collect and drain surface meltwater.

Poinar’s $782,557 project will use AI to produce representations of these smaller and more complex glacier processes and slot them into existing ice sheet models, thus improving predictions of both ice loss and sea level rise.

“Ice sheet models are sophisticated and accurate. They are one of the best tools we have for projecting futures,” Poinar says. “This project will make them even better.” 

Glaciologists have a robust understanding of the smaller processes that contribute to ice loss. The problem has been fitting this knowledge into their models.

Ice sheet models typically use a resolution on the scale of 10x10 kilometers, which is about the size of Buffalo’s city limits. This means they would represent everything in Buffalo as a constant, neglecting neighborhoods, topography and the difference between streets and skyscrapers.  

Poinar’s goal is to use AI, including deep learning techniques, to represent the glacial equivalent of neighborhoods and skyscrapers. Specifically, she and her team will produce representations of crevassing, hydrologic systems and other structural features of glaciers.

Crucially, these AI methods will not substantially increase the computational costs of the models, which are already severely resource constrained. Poinar says the AI will help condense the glacier processes into efficient modules that can be readily applied within any of the 15-plus active ice sheet models. 

Having an accurate idea of ice sheet loss is critical. The melting of the Greenland Ice Sheet is already contributing 0.75 millimeters to global sea levels, on average, each year.  

“We need to know how much coastal erosion to expect so that society can plan around it,” Poinar says. “In the years to come, we will have to decide where and whether to build protective coastal infrastructure like seawalls.”

In addition to supporting graduate and undergraduate students in UB’s Glacier Modeling Lab, the project will establish a “WNY AI in the Geosciences Symposium.” This on-campus conference will bring together geoscience researchers, educators and students from area community colleges and regional public universities that serve less-resourced populations.