00:00 AM

Four Pitt Engineering Researchers Receive NSF CAREER Awards in 2020 Funding Cycle

Award is NSF's most prestigious for early-career faculty

PITTSBURGH (September 28, 2020) … The University of Pittsburgh’s Swanson School of Engineering closed out the 2020 fiscal year with four faculty winning CAREER awards from the National Science Foundation. This brings the total to 15 CAREER awards received by Swanson School faculty since 2016.

According to NSF, the Faculty Early Career Development (CAREER) Program is its most prestigious award in support of early-career faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization.

“I am incredibly proud of our young faculty for contributing to the Swanson School’s diverse research portfolio and achieving this important recognition in their early career,” said David Vorp, associate dean for research and the John A. Swanson Professor of Bioengineering. “Over the past few years, we have improved faculty resources for developing and applying for federal funding, and the number of CAREER recipients is a great indicator of our success.”

The 2020 recipients include:

Takashi D-Y Kozai, assistant professor of bioengineering
Uncovering the Impact of Traditional and Novel Chronic Stimulation Modalities on Neural Excitability and Native Neuronal Network Function

Dr. Kozai received a $437,144 CAREER award to improve the integration of the brain and technology in order to study long-standing questions in neurobiology and improve clinical applications of brain-computer interfaces. One of the challenges remaining with this technology is achieving long-term and precise stimulation of a specific group of neurons. Kozai has designed a wireless, light-activated electrodethat enables precise neural circuit probing while minimizing tissue damage. The funding will enable him to further improve this technology.

Sangyeop Lee, assistant professor of mechanical engineering and materials science
Machine Learning Enabled Study of Thermal Transport in Polycrystalline Materials from First Principles

Dr. Lee’s $500,000 CAREER award will utilize machine learning to model thermal transport in polycrystalline materials. Developing materials with ultrahigh or ultralow thermal conductivity along a certain direction can enable new energy storage and conversion devices. However, grain boundaries - two-dimensional defects in crystal structures - exist in polycrystalline material and significantly affect thermal transport. Addressing the defects is currently not efficient - observing and experimenting with grain boundaries when creating materials can prove to be a lengthy and costly process. Machine learning may provide a more sustainable alternative. His research seeks to create a computer model that can predict the conductive properties of a material in real life, providing guidance to engineer defects for desired thermal properties.

Jason Shoemaker, assistant professor of chemical and petroleum engineering
Enabling Immunomodulatory Treatment of Influenza Infection using Multiscale Modeling

When a person contracts a respiratory viral infection like COVID-19 or influenza, the immune system responds in a myriad of ways to eliminate the virus. Respiratory viral infections are so dangerous, however, because excessive immune responses may cause extreme lung inflammation. However, Dr. Shoemaker’s new modeling research may help doctors better predict and treat patients who are most at risk to that extreme response. His $547,494 CAREER Award will fund creation of computational models of the immune response to seasonal, deadly (avian) influenza viruses, which can help identify the best way to suppress immune activity and reduce tissue inflammation. Since this work targets the immune system and not the specific virus, the models are expected to impact many respiratory infections, including COVID-19.

Feng Xiong, PhD, assistant professor of electrical and computer engineering
Scalable Ionic Gated 2D Synapse (IG-2DS) with Programmable Spatio-Temporal Dynamics for Spiking Neural Networks

In science fiction stories from “I, Robot” to “Star Trek,” an android’s “positronic brain” enables it to function like a human, but with tremendously more processing power and speed. In reality, the opposite is true: a human brain - which today is still more proficient than CPUs at cognitive tasks like pattern recognition - needs only 20 watts of power to complete a task, while a supercomputer requires more than 50,000 times that amount of energy. Dr. Xiong’s $500,000 CAREER award will fund research in neuromorphic computer and artificial neural networks to replicate the spatio-temporal processes native to the brain, like short-term and long-term memory, in artificial spiking neural networks (SNN). This “dynamic synapse” that will dramatically improve energy efficiency, bandwidth and cognitive capabilities of SNNs.


Contact: Paul Kovach