Mirek Riedewald’s research interests are in databases and data mining, with an emphasis on designing scalable analysis techniques for data-driven science. He has collaborated successfully with scientists from different domains, including ornithology, physics, mechanical and aerospace engineering, and astronomy. This work resulted in novel approaches for data warehousing, data stream processing, prediction, and parallel data processing using computer clusters. He is now focusing on exploratory analysis of massive observational data and on techniques for automated reconstruction of structure and dynamics of neural circuits, a crucial step toward understanding the functionality of the brain. Prof. Riedewald’s work was published in the premier peer-reviewed data management research venues like ACM SIGMOD, VLDB, IEEE ICDE, and IEEE TKDE, as well as in domain science journals.
