Prof. Huang was chosen for demonstrating “a highly prolific spirit of innovation in creating or facilitating outstanding inventions that have made a tangible impact on the quality of life, economic development and welfare of society.”
The research proposes "novel SRAM technology aimed at decoupling the conflicting low leakage power and low supply voltage design requirement using emerging nano-devices such as memristor and selector switches."
Students are selected based on a strong academic record, exemplifying leadership, and demonstrating a passion for technology.
Jeff Andrews, François Baccelli, Alan Bovik, and Robert Heath, professors at Texas ECE, have been recognized as Highly Cited Researchers for 2018.
Prof. Jaydeep Kulkarni and Prof. Sanjay Banerjee are co-principal investigators on a new National Science Foundation (NSF) grant to explore the advancement of energy efficient design in integrated circuits.
The title of his research for the competition was "UTPlaceF: A High-Performance Placement Engine For Modern FPGAs."
Together with collaborators at the University of California, Riverside they have been awarded a $1M grant to study application of machine learning techniques for performance and power prediction in early design stages of future computer systems.
Texas ECE student Otitoaleke Akinola won the Best Poster Award at the Inaugural TACC Symposium for Texas Researchers. Akinola, a nanoelectronics researcher and Ph.D. student at UT Austin, works in the Integrated Nano Computing Lab with Dr. Jean Anne Incorvia.
Dr. Wasserman was elected “for contributions to the development of novel sources, detectors, and optical materials operating in the mid-infrared wavelength range.”
Texas ECE alumna Jette Henderson, who completed her PhD in August, received the Best Student Paper award for the paper "PIVETed-Granite: Computational Phenotypes through Constrained Tensor Factorization" at the KDD MLMH Workshop on Machine Learning for Healthcare in London in August. Jette worked under the supervision of Texas ECE professor Joydeep Ghosh. The paper uses a special kind of tensor factorization that is guided by supporting evidence from PubMed, a huge repository of medical literature, to extract meaningful insights from electronic health records.
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