The 2022 IEEE International Symposium on Information Theory (ISIT) was held from June 26th to July 1st at Aalto University in Espoo, Finland. ISIT is the annual flagship conference hosted by the IEEE Information Theory Society dedicated to advancing research in information theory and related fields. Wu Zhuangfei, Diao Jun, and Zhou Lin published papers at the conference, and participated in the conference online, presented papers and discussed with scholars at home and abroad.

        On June 27th, Mr. Wu gave a report entitled "Excess-Distortion Exponents for Successive Refinement Using Gaussian Codebooks" at the seminar "Source Coding and Data Compression II", introducing the mismatched codes used to encode any source for the Successive Refinement problem, and it is proved that the probability of excess-distortion decays exponentially as the code length increases.

        On June 28th, In the seminar "Error Exponents and Reliability I", Diao Jun gave a report entitled "Achievable Error Exponents for Almost Fixed-Length Binary Classification", which introduced the almost fixed-length test algorithm that can achieve sequential test performance with fixed length test complexity in the binary classification in the case of asymptotic, and analyzed its achievable error exponents.

On July 1st, Prof. Zhou Lin gave a report entitled "Asymptotics for Outlier Hypothesis Testing" in the seminar "Hypothesis Testing", which introduced the anomaly detection algorithm with the optimal performance under the extended Neyman-Pearson criterion, and analyzed the equilibrium relationship of this algorithm among the false alarm probability, the misexamine probability and the missed detection probability under the condition of using limited length samples.

In addition, Prof. Zhou Lin, Wu Zhuangfei and Diao Jun participated in Professor Raymond W. Yeung's Shannon Award lectures online, as well as invited lectures by renowned scholars such as Yona Eldar, Michael Jordan, Bengt Holmström and Tsachy Weissman.