Professor Dan Olteanu
Professor of Computer Science
Fellow; Director of IT
I spend my time understanding hard computational challenges on data processing and designing simple and scalable solutions towards these challenges. I contributed so far to both theoretical foundations and systems aspects of data processing. My theoretical research frequently includes the development of novel data processing algorithms and of data processing languages along with the analysis of their complexities. My systems research is on building data systems in academia and industry based on well-understood theory. Here is a list of my current research projects:
- Factorised databases: FDB (data compression; query processing with low complexity); F (distributed & parallel machine learning over databases); F-IVM (incremental maintenance of machine learning models and database queries)
- Probabilistic databases: SPROUT (probabilistic query processing, lifted inference); MayBMS (probabilistic data models, probabilistic query languages); ENFrame (probabilistic programming); Pigora (probabilistic data integration)
- Datalog engines: With folks@LogicBlox on their database engine, their meta-engine, and a probabilistic extension of Datalog; with Oxford colleagues on RDFox.
Past projects to which I contributed as student/co-investigator/principal investigator:
- DIQOPT: Distributed Query Optimisation
- G-Store: A Storage Manager for Graph Data
- SKAR-DB: Managing Square-Kilometer-Array Data
- SPEX: Streamed and Progressive Evaluation of XPath
- Agora: Living with XML and Relational Data
Current research support: Google; EPSRC (DBOnto, Industrial PhD CASE, RealPDBs, VADA); ERC Consolidator Grant (FADAMS); Infor; Ordnance Survey.
Past research support: Amazon Cloud; Cornell; EPSRC (PROQAW, ADEPT); EU FP7 (FOX, HiperDNO); Fondation Wiener Anspach; LogicBlox; Microsoft Azure; Oxford (Astor Travel Fund, Lockey Fund).