Archaeoinformatics - Data Science

Short Bio: Prof. Dr. Matthias Renz (Dr. rer. nat., habil)

Dr. Renz is Professor at the Department of Computer Science at Christian-Albrechts-Universität zu Kiel (CAU, University of Kiel). Before he joined CAU in summer 2018, Dr. Renz was Associate Professor in the Computational and Data Science Department at George Mason University (GMU), Fairfax, VA, USA. He received his Ph.D. in Computer Science at Ludwig-Maximilians-Universität München in 2006, where he served as lecturer after finishing his habilitation (venia legendi) 2011. Before Dr. Renz moved to GMU 2016, he was acting chair of the database systems group at the LMU Munich. Dr. Renz was co-founder and co-director of the Data Science Lab at LMU Munich which has been founded in cooperation with Siemens AG. Dr. Renz also co-founded and co-directed the Mason’s DataLab at the College of Science at George Mason University, where he headed the DataLab's DataScience division.

Dr. Renz’s main research interest is Data Science with focus on scalable methods for searching and mining in very large, heterogeneous, dynamic and potentially uncertain data. He has published more than 120 papers on peer-reviewed international conferences and in international journals. His work has received considerable attention by the corresponding community with more than 2500 citations achieving an H-index of 23 and an i10-index of 51. He gave several invited tutorials, seminars, and keynotes on international conferences and in 2016 his work received the 10-Year Best Paper Award at the International Conference on Database Systems for Advanced Applications. His work has been supported by governmental research grants and industrial funding including Siemens AG, Volkswagen Group, Audi AG, and BMW AG. He has been a program committee member for several international conferences and workshops, including SIGMOD, VLDB, ICDE, and KDD. Furthermore, Dr. Renz co-organized and chaired several international conferences and workshops, including SSTD, ACM SIGSPATIAL, DASFAA, ACM SIGSPATIAL QUeST, ACM SIGMOD GeoRich, and ACM SIGSPATIAL LocalRec.