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Associate Professor Hsun-Ping Hsieh Named Recipient of the "2022 AI 2000 Most Influential Scholar Honorable Mention in Data Mining"

publish date : 2022-03-21

 

Associate Professor Hsun-Ping Hsieh Named Recipient of the “2022 AI 2000 Most Influential Scholar Honorable Mention in Data Mining”

 

Hsun-Ping Hsieh, Associate Professor of the Department of Electrical Engineering, has been recognized as the 2022 AI 2000 Most Influential Scholar Award Honorable Mention by AMiner for his outstanding and vibrant contributions to the field of Data Mining. Dr. Hsun-Ping Hsieh is the only young scholar in Taiwan who has received this honorable mention award.

 

Hsun-Ping Hsieh, Associate Professor of the Department of Electrical Engineering, was named the 2022 AI 2000 Most Influential Scholar Honorable Mention in Data Mining.

EE Associate Professor Hsun-Ping Hsieh was named the 2022 AI 2000 Most Influential Scholar Honorable Mention in Data Mining.

 

According to AMiner, the AI 2000 Most Influential Scholar Annual List aims to name 2,000 of the world’s top research scholars from the fields of artificial intelligence over this decade. The list is conferred in recognition of outstanding technical achievements with lasting contribution and impact. The top 11–100 scholars will be awarded as ‘AI 2000 Most Influential Scholar Honorable Mention’. The 2022 winners are among the most-impactful scholars from the top venues of their respective subject fields between 2012 and 2021.

 

Dr. Hsun-Ping Hsieh is currently executing the Einstein Program funded by the Ministry of Science and Technology (MOST) involving the research topics of Big Data Analysis and Mass Transit Systems. Since 2010, he has authored 46 journal papers in the field of data mining, with a total of 1,584 citations, and has gotten an H-index of 11. His research fields include big data analysis and exploration, urban science and computing, smart city and IoT applications, temporal information calculation, social network analysis, wireless sensor network computing, machine learning and financial information technology.

 

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