I am an Assistant Professor in the Computer Science Department at Carnegie Mellon University. My research interest is in database management systems, specifically main memory systems, non-relational systems (NoSQL), transaction processing systems (NewSQL), and large-scale data analytics. At CMU, I am a member of the Database Group and the Parallel Data Laboratory. My work is also in collaboration with the Intel Science and Technology Center for Big Data.
I am looking for one new Ph.D. student to start in Fall 2015. Applications from students that are proficient at turntablism will be given preferential consideration. All admitted Ph.D. students receive graduate fellowships from the department. Please see the SCS admissions page for information on how to apply.
The onset of non-volatile memory (NVM) devices will require us to rethink the dichotomy between memory and durable storage. As part of the Big Data ISTC, we are studying NVMs to understand their performance characteristics in the context of big data systems and build the groundwork for new DBMS architectures. [More Info]
We are building a distributed framework for supporting fast and efficient incremental computation of materialized views for large-scale data sets. The system is designed to be highly parallelizable and fault-tolerant. [More Info]
We are exploring new database system designs for future many-core CPU architectures. On the software side, are pursuing a bottom-up approach where the architecture is designed to target many-core systems from inception. On the hardware side, we are designing new hardware components that can unburden the software system from computationally critical tasks. [More Info]
OLTP-Bench is an extensible "batteries included" DBMS benchmarking testbed with over a dozen workloads that supports all major DBMSs. that all differ in complexity and system demands. The key contributions of the project are its ease of use and extensibility, support for tight control of transaction mixtures, request rates, and access distributions over time. [More Info]
We are developing an in-memory, distributed stream processing engine that is integrated with a front-end, OLTP database system. The goal of the S-Store project is to deploy continous query workloads in the front-end system without degrading the performance of the regular OLTP workload.