Personal names are important and common information in many data sources, ranging from social networks and news articles to patient records and scientific documents. They are often used as queries for retrieving records and also as key information for linking documents from multiple sources. Matching personal names can be challenging due to variations in spelling and various formatting of names. While many approximated name matching techniques have been proposed, most are generic string-matching algorithms. Unlike other types of proper names, personal names are highly cultural. Many ethnicities have their own unique naming systems and identifiable characteristics. In this paper we explore such relationships between ethnicities and personal names to improve the name matching performance. First, we propose a name-ethnicity classifier based on the multinomial logistic regression. Our model can effectively identify name-ethnicity from personal names in Wikipedia, which we use to define name-ethnicity, to within 85% accuracy. Next, we propose a novel alignment-based name matching algorithm, based on Smith-Waterman algorithm and logistic regression. Different name matching models are then trained for different name-ethnicity groups. Our preliminary experimental result on DBLP's disambiguated author dataset yields a performance of 99% precision and 89% recall. Surprisingly, textual features carry more weight than phonetic ones in name-ethnicity classification.