Texas A&M at Qatar’s Huang named highly cited researcherPublished Dec 29, 2019
Dr. Tingwen Huang, a mathematics professor in the Science Program at Texas A&M University at Qatar, has been named a Highly Cited Researcher by Clarivate Analytics and is one of six Texas A&M faculty members named to the list for 2019.
Huang applies mathematics to solve engineering problems, especially those originating in artificial computational intelligence, neural networks and computer science. His research focuses on the dynamics of neural networks, including memristor (memory resistor), complex networks including multi-agent and smart grid, and automation.
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.
His research group is working toward developing new memristor-based neural networks that can dynamically store multiple information to increase storage density, and aim to resolve several fundamental challenges in the research and development of memristor-based neural networks. Improving storage capacity and density will have significant impact on IT industries in this era of big data computing, which could then have a transformative impact on society, Huang said.
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.
Huang earned a Ph.D. 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 170 in various Institute of Electrical and Electronics Engineers (IEEE) Transactions journals, the flagship journals of IEEE, and more than 40 in Neural Networks, the archival journal of the world’s oldest neural modeling societies: the International Neural Network Society, the European Neural Network Society and the Japanese Neural Network Society. His research work has received more than 14,000 citations. In 2015, Qatar National Research Fund awarded him the Best Research Project. In 2018, he was elected an IEEE Fellow, the highest grade of membership, and a prestigious honor and distinctive career achievement. Most recently, he was presented the Changjiang Scholar award (this honor goes to only 50 overseas scholars each year), the most prestigious honor awarded by Ministry of Education of China.