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DOHA --

Dr. Tingwen Huang, a mathematics professor in the Science Program at Texas A&M University at Qatar, has been elevated to Fellow grade of the Institute of Electrical and Electronics Engineers (IEEE).

IEEE Fellow is the highest grade of membership and is recognized as a prestigious honor and a distinctive career achievement. Huang was recognized "for contributions to dynamical analysis of neural networks."

"I apply mathematics to solve engineering problems, especially those originating in artificial computational intelligence, neural networks and computer science," Huang said. "My research areas focus on dynamics of neural networks including memristor (memory resistor), complex networks including multi-agent and smart grid. Memristor and smart grid both are important to Qatar's advancement."

A neural network is a framework for many different machine learning systems to work together and process complex data. For example, a simple neural network could learn to identify cats and dogs in photos after learning and deciphering characteristics of them from other photos.

Memristor-based neural networks are advantageous over transistor-based ones because of their non-volatile properties and nanoscale geometry, together with the compatibility with CMOS process technology to increase the packing density of the memory cells and reduce power dissipation. More memory can be packed in a smaller space, heat can be dissipated better and power can be managed better without the loss of already stored data. Therefore, memristor-based neural networks have been actively investigated as a promising approach to achieve ultra-high computing capacity and performance in artificial intelligence, Huang said.

"My research group is working toward developing new memristor-based neural networks that can dynamically store multiple information to increase storage density," he said. "We aim to resolve several fundamental challenges in the research and development of memristor-based neural networks. The developed theories and design techniques will offer a solid technical foundation for memristor-based neuromorphic computing and benefit both the computer system and VLSI circuit communities. The research outcome can also benefit solid-state electronics societies by guiding the device and circuit development toward long-term technology scaling and system design promises. Improving storage capacity and density will have significant impact on IT industries in this era of big data computing and this will have a transformative impact on society. The targeted area is timely and important as storage systems are becoming more critical and it is urgent to solve the 'I/O Wall' challenges."

Huang has also developed a framework of the smart grid system management via neural networks-based approaches, which play a significant role in improving the energy efficiency. In fact, Huang said, Qatar needs a smart grid that is intelligent to customers and utility companies, self-healing and resilient to natural disasters, which could accommodate existing infrastructure and deliver reliable supply through deep learning algorithms.

The outcomes of this research will improve the energy efficiency and enhance the reliability of Qatar's power network. Huang said this will be very useful to the country in training the next generation of electrical engineers, developing cutting-edge deep learning technology and cultivating modern power system operational consultancy.

Huang earned a Ph.D. in mathematics from Texas A&M University in December 2002. Since joining the Texas A&M at Qatar faculty in 2003, he has published more than 400 papers in prestigious journals, including more than 130 in IEEE Transactions journals (the top engineering journals) and 36 in Neural Networks (the flagship journal of International Neural Networks Society). His research work has received more than 10,000 citations. He was a Dean's Fellow in recognition of his excellence and achievements in 2014. Qatar National Research Fund awarded him the Best Research Project, also in 2015. Most recently, he's been named to Clarivate Analytics' Highly Cited Researcher 2018 list.

IEEE is a non-profit international association advancing innovation and technological excellence in the field of electrical and electronics engineering. Today, there are more than 423,000 IEEE members in more than 160 countries around the world. The grade of Fellow is awarded to distinguished researchers in recognition of their breakthrough and high-impact studies in any of IEEE's designated fields of interest, and is conferred by the IEEE Board of Directors only by nomination and references of a Fellow nominator. Less than 0.1 percent of voting IEEE members are selected annually for this member grade elevation.

View the complete list of newly elevated fellows for 2019.

-aTm-

Contact: Shana K. Hutchins, (979) 862-1237 or shutchins@science.tamu.edu

Lesley Kriewald

  • Dr. Tingwen Huang '02

    (Credit: Texas A&M University at Qatar.)

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