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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Atlanta, GA | Posted: August 19, 2019
Jackie Nemeth
School of Electrical and Computer Engineering
404-894-2906
Summary Sentence:
ECE Ph.D. student Bahar Asgari has been selected to participate in Rising Stars 2019, hosted by the University of Illinois at Urbana-Champaign.
Full Summary:
ECE Ph.D. student Bahar Asgari has been selected to participate in Rising Stars 2019, hosted by the University of Illinois at Urbana-Champaign.
Bahar AsgariBahar Asgari has been selected to participate in Rising Stars 2019, hosted by the University of Illinois at Urbana-Champaign (UIUC). Asgari is a Ph.D. student in the Georgia Tech School of Electrical and Computer Engineering (ECE).
Rising Stars is an intensive workshop for women graduate students and postdocs who are interested in pursuing academic careers in computer science, computer engineering, and electrical engineering. The workshop will be held from October 29-November 1 at the UIUC campus.
Asgari joined the Georgia Tech School of ECE in fall 2015. She has worked in the Computer Architecture and Systems Lab under the supervision of ECE Regents’ Professor Sudhakar Yalamanchili and Hyesoon Kim, an associate professor in the School of Computer Science.
Asgari earned both her bachelor’s and master’s degrees in Computer Engineering from Iran University of Science and Technology (IUST) in Tehran, Iran. Her Ph.D. thesis research includes, but is not limited to, designing efficient specialized hardware for compute- and memory-intensive algorithms that comprise a high level of parallelism and/or specific patterns of data reuse.
Such algorithms with a wide range of applications in on-demand fields–such as machine learning, autonomous technologies, modeling physical-world phenomena, and graph analytics–encounter performance limitations when running on the current compute platforms such as GPUs and CPUs. Specialized hardware, designed to accelerate the bottleneck-prone parts of an algorithm, continue delivering high performance for target memory- and compute-intensive applications.
