PITTSBURGH (July 20, 2020) … The University of
Pittsburgh Department of Electrical and Computer Engineering (ECE) will welcome
Drs. Kara Bocan, Azime Can-Cimino, and Peipei Zhou as assistant professors,
starting September 1, 2020.
“My ECE colleagues and I are thrilled to have these
outstanding new professors join our faculty,” said Alan George, Department
Chair and R&H Mickle Endowed Chair of Electrical and Computer Engineering.
“Azime’s experience in industry and gift for working with students will be a
major asset, and Kara is an innovative teacher with a wealth of ideas to
improve our courses and programs. Peipei will boost our research efforts with
her expertise in field-programmable gate arrays, which is a field of growing
prominence and a growing need in our department.”
The appointment of these three faculty members
will also help narrow the gender gap in a field that struggles to hold a female
presence.
Though the number of women earning engineering
degrees has increased in the past decade, there are still
proportionally far fewer women than men studying engineering and an even lower
proportion of female engineering faculty. According to a 2018 study from the American
Society for Engineering Education, on average, women only make up about 17
percent of tenured and tenure-track engineering faculty.
“These three appointments will double the number
of full-time female faculty in our department, help us to more strongly support
our present undergraduate and graduate students, and help us to attract in
future an even larger and broader range of promising students,” George added. “Peipei,
Azime, and Kara are outstanding engineers, and they will significantly enhance both
teaching and research in the Swanson School of Engineering.”
Kara
Bocan, PhD
Bocan received her PhD in electrical
engineering from the University of Pittsburgh in 2017, where she also received
her BSE in electrical engineering and bioengineering with a minor in
neuroscience in 2012. She performed her dissertation research on wireless
implantable medical devices with the RFID Center of Excellence, where her use
of computer-aided design was an entry point to the field of computational
modeling. More recently, her research has focused on the use of computational
modeling to enhance understanding of complex systems, and on the development of
effective and usable modeling software.
She has taught courses part-time as a visiting
research assistant professor for the Swanson School’s Department of Electrical
and Computer Engineering since Fall 2018, focusing on active learning and
student engagement through interactive examples and open-ended engineering
questions. Her teaching interests include blended learning, flipped classrooms,
gameful design, technology ethics, and accessibility.
Azime
Can-Cimino, PhD
Can-Cimino received a BS and MS degree in
electrical and electronics engineering from the University of Istanbul,
Istanbul, Turkey, and a PhD degree in electrical engineering from the
University of Pittsburgh. Prior to Pitt, she worked as a senior software
engineer at Emerson Automations Solutions development team, where among other
things, she developed AI algorithms for the power and water industry. Her
research interests are in machine learning, optimization, and statistics. She
has also contributed to other areas including sampling (signal processing),
wavelets and compressive sensing.
Peipei Zhou,
PhD
Zhou received her PhD in computer science in
August 2019 from the University of California, Los Angeles, where she also
received an MS in electrical and computer engineering in June 2014. Her
undergraduate studies were in electrical engineering at Chien-Shiung Wu Honors
College, Southeast University, China. She is currently a research scientist at
Shanghai Enflame Technology, an AI chip start-up with a research focus on
domain-specific language and compiler for AI ASIC Accelerator and computer
architecture modeling and system optimization with autotuning.
Zhou's research interests lie in design
automation and compilers as well as modeling and optimization for customized,
parallel and distributed computing at multiple levels, including chip-level,
node-level and cluster-level. Her research advances field-programmable gate
array-based reconfigurable architecture from a performance, energy and cost
perspective for deep learning, precision medicine and other big data and
machine learning applications.
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7/20/2020
Contact: Leah Russell