Professor honored with prestigious fellowship

Dr. Chao Tian, electrical and computer engineering professor at Texas A&M University, has been elected as an Institute of Electrical and Electronics Engineers (IEEE) Fellow for his contributions to information theory in machine-based converse proofs, coding for data storage, and joint source-channel coding.

Awarded to less than 0.1% of members each year, the IEEE Fellow grade holds an elite distinction for individuals with exceptional contributions in any of the institute’s fields of interest.

IEEE, the world’s largest technical professional organization, has over 460,000 members across more than 190 countries, making this grade a rare honor for members of this prestigious society. 

One of Tian’s cited contributions was his research on converse proofs — mathematical equations used to determine the fundamental limits of a system’s capacity. Traditionally, these proofs had been done manually, a process that requires lengthy thought and deep reasoning, which can be extremely time-consuming and prone to human error. Tian discovered that many of these proof steps can be automated, resulting in a more efficient computer-based converse proof capable of handling far more complex problem settings. 

Tian’s team also focused on the next step after establishing a converse proof: designing codes that are optimized to perform as efficiently as possible within a system’s limits. They developed coding strategies for distributed data storage systems, enabling a system to repair itself and recover data when a storage drive fails or reaches its limit. 

His third cited contribution was research in joint source-channel coding, which built on the ideas of renowned mathematician and engineer Claude Shannon, who founded the field. Tian identified the precise fundamental limits of several important joint source-channel scenarios. He also established a general set of conditions under which separate coding continues to be optimal, significantly extending Shannon’s original discovery.

Using Tian’s tools and theories, there is significant potential to improve communication and data storage systems that are fast, reliable and resilient to failure. While still theoretical in nature, his research has far-reaching implications. These include improved efficiency for large-scale data applications — such as artificial intelligence and machine learning systems — and future 6G communication systems, where semantic meanings appear to play an important role.

“AI and machine learning have made a large impact in the communications field, and I am looking forward to applying some of the new techniques to problems I’ve been exploring,” Tian said. 

He is particularly looking forward to applying these techniques to machine-based proofs, as well as designing faster and more reliable data storage and communication systems.