Mirek Riedewald
Professor
Research Interests
- Databases
- Data mining
Education
- PhD in Computer Science, University of California, Santa Barbara
- BS in Computer Science, Saarland University — Germany
Biography
Mirek Riedewald is a professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.
Riedewald's research interests are in databases and data science, with an emphasis on designing novel data management and analysis techniques that scale in size, velocity, and dimensionality of data. He has collaborated with scientists from various domains, including ornithology, physics, mechanical and aerospace engineering, and astronomy. This work produced novel approaches for data warehousing, data stream processing, prediction, and parallel data processing using computer clusters. Riedewald now focuses on distributed big-data analytics, ranked enumeration, exploratory analysis of massive observational datasets, query visualization, 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, Riedewald was a research associate at Cornell University. He also held visiting research positions at Microsoft Research in Washington and at the Max Planck Institute for Informatics in Germany. His work has been published in premier data management research venues and journals, and he has received awards for Best Student Paper at ECML, Best Poster at ICDE, and Best Paper at EDBT, as well as "best-of-conference" mentions at ICDE, DaWaK, and ICDE.
Labs and groups
Recent publications
-
Optimal Algorithms for Ranked Enumeration of Answers to Full Conjunctive Queries
Citation: Nikolaos Tziavelis, Deepak Ajwani, Wolfgang Gatterbauer, Mirek Riedewald, Xiaofeng Yang. PVLDB 13(9):1582-1597, 2020 -
STRATISFIMAL LAYOUT: A modular optimization model for laying out layered node-link network visualizations
Citation: S. di Bartolomeo, M. Riedewald, W. Gatterbauer and C. Dunne, "STRATISFIMAL LAYOUT: A modular optimization model for laying out layered node-link network visualizations," in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 324-334, Jan. 2022, DOI: 10.1109/TVCG.2021.3114756. -
QueryVis: Logic-based diagrams help users understand complicated SQL queries faster
Citation: Aristotelis Leventidis, Jiahui Zhang, Cody Dunne, Wolfgang Gatterbauer, H. V. Jagadish, and Mirek Ridewald. “QueryVis: Logic-based diagrams help users understand complicated SQL queries faster”. In: Proc. 2020 ACM SIGMOD International Conference on Management of Data. SIGMOD. Preprint & supplemental material: osf.io/btszh. SIGMOD 2021 Most Reproducible Paper Award. 2020, pp. 2303–2318. doi: 10.1145/3318464.3389767.