Eight SEAS faculty earn CAREER Awards

By Peter Murphy and Elizabeth Egan 

The recipients include Courtney Faber, an assistant professor in the Department of Engineering Education, Luis Herrera, an assistant professor in the Department of Electrical Engineering, Yinyin Ye, an assistant professor in the Department of Civil, Structural and Environmental Engineering, Craig Snoeyink, an assistant professor in the Department of Mechanical and Aerospace Engineering, Shaofeng Zou, an assistant professor in the Department of Electrical Engineering, Kang Sun, an assistant professor in the Department of Civil, Structural and Environmental Engineering, Zhuoyue Zhao, an assistant professor in the Department of Computer Science and Engineering, and Andrew Olewnik, an assistant professor in the Department of Engineering Education.

Thirteen SEAS faculty have received the prestigious honor since 2023, with five awards presented in the previous year.

Jason Sprowl, an assistant professor of pharmaceutical sciences in The University at Buffalo's School of Pharmacy and Pharmaceutical Sciences, also received a CAREER Award. 

"We take great pride in our eight faculty members who have been honored with this prestigious NSF award,” said Venu Govindaraju, UB vice president for research and economic development. “Their exceptional research is integral to UB’s mission of fostering a better world for all.”

Among the support that awardees receive is guidance from UB’s Office of Research Advancement, which Chitra Rajan, associate vice president for research advancement, oversees. The office is managed by three co-directors – Joanna Tate, Maggie Shea and Menna Mbah – and provides a comprehensive suite of services, including proposal management, scientific editing, graphics, and help with non-technical parts of the proposal. 

These services, Rajan says, play a critical role in assisting faculty members submit high-quality proposals.

When a research team is made up of people with various engineering and education backgrounds, different ideas of what knowledge is and how it is acquired can hinder their ability to work as a cohesive team.

Having firsthand experience with this issue, Faber’s goal is to support engineering education researchers who find themselves in a similar situation. 

With a $591,000 grant, Faber aims to facilitate interdisciplinary work by identifying the barriers that research teams might face related to differences in thinking and creating ways to bring them to the surface for discussion before they create a problem.

“It is important for the field of engineering education to be able to do this type of interdisciplinary work,” said Faber. “The problems we are trying to solve are very complex and require an interdisciplinary approach to make space for diversity of thinking.”

The project will involve observing research teams and conducting interviews to see how they function together as well as how the individual members think independently of the group.

With the information she gathers, Faber plans to develop trainings that new and established engineering education researchers can freely access.

She also hopes to create a tool that assists research groups in integrating the varying approaches and goals that might otherwise become problematic for a group. The tool could be as simple as a one-page guide that provides questions to be considered throughout the research process to help identify where a team’s ideas might differ across various aspects of their research, leading to more open discussions. 

Herrera’s research lies at the intersection of power electronics, power systems and control theory.

With a $500,000 grant, he is developing different control methods to promote the wider adoption of DC microgrids, which run more efficiently than the more commonly used AC (alternating current) microgrids.

“Currently, DC electrical systems are primarily used in applications such as electric aircrafts, including the Boeing 787 Dreamliner, navy ships and data centers,” said Herrera. “However, most renewable energy sources are interfaced to the AC power grid through an intermediate DC stage.”

More networks operated through DC grids could significantly increase energy efficiency, reduce losses and improve the overall operation of current electrical systems, he says.

This potential creates motivation for DC systems to be implemented in commonly used structures such as residential and office buildings.

Beyond the practical applications of his research, Herrera’s project will also provide educational opportunities for students from graduate to elementary school.

Graduate students on the project will have the opportunity to participate in a summer internship at the Air Force Research Laboratory through a partnership with the University of Dayton Research Institute.

Herrera also plans to create demonstrations regarding the research and present them to elementary, middle school and high school students, aiming to get students excited about STEM early in their academic careers.  

Extracellular vesicles (EV) are a mechanism for bacterial pathogens to release their virulence proteins into surrounding environments. These harmful materials move from the human body through feces into the sewer systems, where their fate is not fully understood.

With a $583,000 grant, Ye will monitor EV persistence and stability in wastewater and throughout the wastewater treatment process, and analyze functions of environmental EV and what contents are packed in them. The project will develop a novel environmental analysis method that integrates genome sequencing and proteomic analysis.

“If the vesicles preserve the function of virulence proteins in wastewater, we need to better understand the fate of the vesicles when they go through the treatment chain,” said Ye. “How are we able to minimize the health risks of vesicles after the treatment at the wastewater treatment plants? If they escape the treatment process and are still active, that can have certain health impacts.”

The principles and analysis in Ye’s project will focus on wastewater samples, however, these approaches can be applied to analyzing vesicles and their potential health risks in air dust, drinking water and rainwater, she says. Ultimately, this work will help determine what harmful materials—if any—are still present after the wastewater treatment process and how to remove them most effectively through disinfection.

The project will also create hands-on activities to engage K-12 and undergraduate students in learning wastewater microbiome analysis and microbial risk mitigation for public health and potentially build their interest in environmental engineering.

Reinforcement learning (RL) is a type of machine learning that trains autonomous robots, self-driving cars and other intelligent agents to make sequential decisions while interacting with an environment.

Many RL approaches assume the learned policy will be deployed in the same—or similar—environment as the one it was trained in. In most cases, however, the simulated environment is vastly different from the real world—such as when a real-world environment is mobile while a simulated environment is stationary. These differences often lead to major disruptions in industries using RL, including health care, critical infrastructure, transportations systems, education and more.

Zou’s $520,000 award will fund his work to develop RL algorithms that do not require excessive resources, and that will perform effectively under the most challenging conditions they may encounter including those outside of the training environment. According to Zou, the project could have a significant impact on both the theory and practice of sequential decision making associated with RL in special education, intelligent transportation systems, wireless communication networks, power systems and drone networks.

“The activities in this project will provide concrete principles and design guidelines to achieve robustness in the face of model uncertainty,” Zou says. “Advances in machine learning and data science will transform modern humanity across nearly every industry. They are already the main driver of emerging technologies. The overarching goal of my research is to make machine learning and data science provably competent.”

Interested in astronomy from a young age, Sun is fascinated by the idea of pointing a space telescope towards the Earth and imaging emission sources like celestial objects.

With a nearly $644,000 grant, Sun will map global emission sources of gaseous air pollutants and greenhouse gasses. Such gasses are invisible to the human eye. While they can be detected by satellites, their images are naturally smeared due to wind dispersion.

“This research removes the smearing effect using a simple and elegant equation that originates from mass balance,” said Sun. “The results are timely and precise estimates of emissions that can inform policy and scientific studies.”

Currently, the two mainstream emission-estimating methods are bottom-up, accounting for activities on the ground and how they emit, and top down, inferring emissions with observations, numerical models and complicated frameworks that are usually region-specific.

Sun’s method will fall within the scope of the later but will work faster, be globally applicable and provide the high spatial resolutions that are more commonly achieved by the bottom-up method.

The results will resemble a space-telescope image, with significant emission sources standing out like galaxies and smaller sources, such as towns and power plants, sprinkled about like star clusters.

By the end of the five-year study, Sun hopes that students and educators may use his open-source algorithms to generate satellite-based concentration and emission maps on their personal computers. 

Water filtration, whisky distillation and blood-based diagnostics are just a few of the real-world potential applications of DMT, a process that uses strong electric fields to push solutes out of water, even those such as sugar and alcohol that do not have an electrical charge.

DMT is not used, however, due to the inaccuracy of current mathematical models.

With a $581,000 grant, Snoeyink will develop and validate models for DMT for use in these applications. With one of the first accurate models of DMT, the process could be used, for example, to clean water as effectively as a water filter, that never needs to be changed.

Snoeyink adds that point-of-care diagnostics are another significant application for DMT. 

“Down the line, we could use this technology to separate blood into components we want to test and stuff we don’t, making medical diagnostics cheaper and more sensitive,” said Snoeyink.

To help with testing and to offer students research opportunities that could propel them into graduate school, Snoeyink will teach a course for students to do research for the project as part of their curriculum. With Snoeyink’s guidance, students will run tests and even create their own hypothesis. He hopes that by the end of the class, the students will have papers based on their research that will bolster their graduate school applications.

Today’s internet databases hold large volumes of data that are processed at higher speeds than ever before.

A new type of database system, hybrid transactional/analytical processing (HTAP), allows real-time data analytics to be performed on databases that undergo constant updates.

“While real-time data analytics can provide valuable insights for applications such as marketing, fraud detection, and supply chain analytics, it is increasingly hard to ensure a sufficiently low response time of query answering in existing HTAP systems,” said Zhao.

Approximate query processing (AQP) is a faster alternative that uses random sampling. However, many AQP prototypes and adopted systems sacrifice query efficiency or the ability to handle rapid updates correctly.

With a $600,000 grant, Zhao aims to support real-time data analytics on large and rapidly growing databases by enabling reliable AQP capabilities in HTAP systems, leading to increasingly demanding, real-time analytics applications.

“If this problem is solved, it will potentially make it possible to finally adopt AQP in many existing database systems and create sizable impacts on real-world data analytics applications,” said Zhao.

Zhao is also committed to broadening the participation in data system research to all levels of education and plans to incorporate new material into existing UB undergraduate and graduate level courses, as well as offer tutorials and projects in various K-12 outreach and undergraduate experiential learning programs. 

When Olewnik finished his bachelor’s degree in mechanical engineering, he recalled thinking, “Am I an engineer now?” Even after spending a year in an engineering internship, he felt unsure about what it meant to be a practicing engineer.  

Engineering students have rigorous curriculums with more credit hours than many other degrees, leaving little time for the students to reflect on what their professional engineering identity (PEI) will look like after graduating.

With a $530,000 grant, Olewnik will work to advance the understanding of how people come to identify themselves as engineers. Through this improved understanding, he plans to develop and implement reflection interventions that will support students in their engineering education journeys.

Olewnik will conduct narrative inquiries and survey early-career engineers to analyze PEI reflection themes and explore the generalizability of these themes towards developing a valid conceptual model of PEI reflection constructs. He will then study the PEI reflection themes of undergraduate engineering students through reflection intervention and surveys that will be analyzed to identify common PEI themes among students.  

By the end of his project, Olewnik hopes to establish evidence-based reflection intervention and integration plans that will be made publicly accessible to serve as a resource for students, helping to make their educational experience transformative and rooted in reflection.