Abstract:

This seminar presents a generalized algorithm for closeness testing in sequential data, incorporating techniques derived from Markov chain analysis. The method is designed to assess the similarity or divergence of data sequences across varied domains. To illustrate the application and efficacy of this approach, we will discuss its deployment in analyzing COVID-19 case sequences over different time intervals, such as weekly and monthly. This approach not only facilitates a deeper understanding of data evolution but also demonstrates the versatility of closeness testing in handling complex sequential datasets in practical scenarios.



Short Bio:

Tomoko Matsui (Senior Member, IEEE) received a Ph.D. degree in computer science from the Tokyo Institute of Technology, Tokyo, Japan, in 1997. From 1988 to 2002, she was a researcher in several NTT laboratories, focusing on speaker and speech recognition. From 1998 to 2002, she was a senior researcher in the Spoken Language Translation Research Laboratory, ATR, Kyoto, focusing on speech recognition. In 2001, she was an invited researcher in the Acoustic and Speech Research Department, Bell Laboratories, Murray Hill, NJ, working on identifying effective confidence measures for verifying speech recognition results. She is currently a professor at The Institute of Statistical Mathematics, Tokyo, Japan, working on statistical spatial-temporal modeling for various applications, including speech and image recognition. She received the Best Paper Award from the Institute of Electronics, Information, and Communication Engineers of Japan, in 1993.