Six Pitt Faculty Receive NSF Awards

Issue Date: 
July 21, 2008

Awards fund junior faculty members’ emerging research


Top, from left: Tracy Cui, Di Gao, and Rebecca Hwa. Bottom,

from left: Alexandros Labrinidis, Lisa Weiland, and Jun Yong.

Communities powered by clean, local-source energy. Faster, more reliable technologies and computers with a better grasp of human language. Medical care tailored to your DNA, or neural stem cells readily available for treating neurological diseases and injuries.

Six University of Pittsburgh faculty members will advance the futures of energy, health, and technology as part of Faculty Early Career Development (CAREER) awards they received this year from the National Science Foundation. The awards fund junior faculty members’ emerging careers and include an education component that encourages outreach to women and underrepresented populations.

Four recipients teach in Pitt’s Swanson School of Engineering: Tracy Cui, an assistant professor in the Department of Bioengineering; Di Gao, an assistant professor in the Department of Chemical and Petroleum Engineering; Lisa Weiland, an assistant professor in the Department of Mechanical Engineering and Materials Science; and Jun Yang, an assistant professor in the Department of Electrical and Computer Engineering.
Assistant professors Rebecca Hwa and Alexandros Labrinidis, both in the Department of Computer Science in Pitt’s School of Arts and Sciences, also received awards.

As of July 7, Pitt was among only 19 universities to receive six or more of the 420 CAREER awards granted since October 2007. Matching Pitt with six awards are Penn State, Texas A&M University System, the University of Massachusetts, the University of Missouri, the University of Washington, the University of Delaware, and Virginia Tech. The University of Illinois at Urbana-Champaign tops the list with 18 awards.
A description of each Pitt recipient’s research follows.

Tracy Cui is developing a platform for better understanding how to harvest neural stem cells for therapeutic use for neurological diseases and injuries. Her research involves creating a surface of electroactive polymers on which neural stem cells can be directed to become functional neurons. This technology would allow scientists to answer the predominant questions regarding neural stem cell growth and neural tissue regeneration, namely, whether stem cells can become functional cells on an engineered surface and, if so, under what circumstances.

Di Gao’s research could help usher in the much-heralded future of personalized medical care based on an individual’s DNA, with his effort to revamp the technique for screening and separating DNA molecules. Gao’s approach would stretch DNA strands tethered to a solid surface via an electric field, allowing them to be pulled from the surface and analyzed based on their viscoelasticity. This method would overcome the limitations of the predominant method of electrophoreses—submerging the strands in a matrix and applying an electric field. By stretching the DNA, chromosome-size DNA molecules can be separated and studied, large fragments can be screened for mutations, and longer sequence fragments can be extracted. The technique might also be applied to RNA. The education component of Gao’s project includes outreach to underrepresented high school students through a related course and workshop at Baldwin and Westinghouse high schools in Pittsburgh, both of which have large African American student populations, and collaboration with Tsinghua University in China on an international field study module for Pitt undergraduates that focuses on international views of the ethical and social issues of genetic research.

Rebecca Hwa aims to improve the ability of computers to process and translate human language. She will address the difficulty many systems have in processing texts from such specialized domains as business e-mails or scientific literature as well as texts that are automatically translated from foreign languages. Specifically, Hwa will create machine-learning algorithms that find correspondences between “standard English” and texts from specialized domains.

The project focuses on three types of correspondences: direct translations, such as bilingual documents; loose translations, e.g., paraphrased articles; and indirectly related texts without an explicit translation. From these correspondences, a standard system will be adapted to translate texts in specialized domains. Better language processing for a wide range of texts could allow for such computer applications as intelligent tutoring programs and data mining for medical documents.

The need for personalization is expected to increase dramatically as the Internet becomes more widespread and its users—and content—more diverse. Accordingly, Alexandros Labrinidis aims to create a user-centric Web portal wherein people can tailor their search results. Labrinidis will first identify quality information from Web data sources; then—through a framework called Quality Agreements (QA)—a person would specify preferences in three categories of quality: Quality of Service, Quality of Data, and Quality of Information.

The user-centric Web portal would then display the Web pages most in keeping with the person’s preferences. Labrinidis’ project re-examines traditional query processing techniques and introduces a new tier of interaction wherein the processor adapts to the user’s changing preferences over time. Labrinidis will conduct user studies to validate the QA framework, evaluate the proposed algorithms analytically and experimentally, and develop prototypes. Results of this research—including software, data, and publications—will be made publicly available via the project Web site Labrinidis is codirector of Pitt’s Advanced Data Management Technologies Laboratory (ADMT Lab), which encompasses a range of projects from data management for sensor networks to data-stream management systems and from scientific data management to Web databases. The ADMT Lab was established in 1995 through an NSF CAREER award presented to ADMT codirector Panos Chrysanthis, a Pitt professor of computer science.

Lisa Weiland will undertake a twofold effort to help sustainable energy gain a foothold in Western Pennsylvania by implementing self-powered materials into an ongoing project to power the town of Vandergrift in Westmoreland County with hydrokinetic power. The Vandergrift project, based in the Swanson School’s Mascaro Center for Sustainable Innovation, will harness the Kiskiminetas River and help power the town’s main business district with free, clean-source electricity using microhydro generators. Because the river—and thus the generator—is small, Weiland will investigate a potential power-harvesting method based on electromechanical materials that would generate power as the river’s current moves over them. Weiland will focus on, among other things, materials known as ionomers, which have been tested for such uses as self-powered sensors in bridges and for monitoring blood flow in patients at risk for arterial blockage; as the sensors move from vibrations or fluid flow, they would simultaneously send out an electric data signal and recharge themselves. But ionomers have not yet been applied to such high-power devices as generators because of a concern that electrical output and fragility increase in tandem. As part of her CAREER project, Weiland will work on constructing more robust ionomers that can produce more power without becoming too delicate. The education component of her project includes working with civic and business leaders in Vandergrift—and eventually other cities—to develop tailored plans for becoming more efficient producers and consumers of energy and goods.

As technologies become more compact and powerful, the microprocessors within them become more prone to overheating, leading to poor performance, reduced reliability, and shorter lifetimes. Jun Yang will investigate ways of controlling temperature by proactively scheduling workloads among different processing cores—which perform specific tasks within a processor—of today’s multicore processors. Current processors adopt a reactive temperature control by decreasing power flow within the entire processor—even if only one core overheats. Yang’s technique instead prevents overheating by swapping a high-stress task in an overheating core with a low-stress task from a cooler core. This approach would diminish the occurrence of hotspots and maintain a temperature at which the processor can function with maximum performance and reliability. Yang focuses her research on computer architecture particularly power and thermal-aware design, energy efficiency, and chip multiprocessor design.