His 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 techniques for automated reconstruction of structure and dynamics of neural circuits, a crucial step toward understanding the functionality of the brain.
Prior to joining Northeastern University, Riedewald was a research associate at Cornell University. He also held visiting research positions at Microsoft Research in Redmond, Washington and at the Max Planck Institute for Informatics in Germany. His work has been 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.
His 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 techniques for automated reconstruction of structure and dynamics of neural circuits, a crucial step toward understanding the functionality of the brain.
Prior to joining Northeastern University, Riedewald was a research associate at Cornell University. He also held visiting research positions at Microsoft Research in Redmond, Washington and at the Max Planck Institute for Informatics in Germany. His work has been 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.
