I am currently a PostDoc at Stanford working with Christopher Re. In August of 2019, I graduated with a PhD from Paul G Allen School for Computer Science and Engineering at the University of Washington in Seattle. I was part of the Database Group and advised by Dan Suciu and Magdalena Balazinska.
For my undergraduate degree, I went to Carleton College in Northfield, MN, where the city's motto is "Cows, Colleges, and Contentment" and graduated in 2013 as a Computer Science and Mathematics double major.
Research and Work Experience
My research interests are primarily focused on building an Open World Database System that inherently treats relations as biased samples of some unknown population and by some population aggregate information, automatically corrects of sample selection bias. I am also interested in how to generate a query-able summary of data that can the be used for running ad-hoc, exploratory, and approximate queries. My other broad research interests include Machine Learning and Data Visualization
I am one of the 2015 winners of the NSF GRFP in Computer Science. In the summer of 2016 and 2017, I interned at Microsoft Research as a PhD research intern, and in the summer of 2015, I interned at Tableau as a software developer. From the summer of 2012 to the spring of 2015, I interned at Sandia National Laboratories working on high performance computing and image reconstruction.
- Mosaic: A Sample-Based Database System for Open World Query Processing. Laurel Orr, Samuel Ainsworth, Walter Cai, Kevin Jamieson, Magda Balazinska, Dan Suciu. CIDR 2020. (paper)
- Sample Debiasing in the Themis Open World Database System. Laurel Orr, Magdalena Balazinska, and Dan Suciu. SIGMOD 2020. (to appear)
- Pushing Data-Induced Predicates Through Joins in Big-Data Clusters. Srikanth Kandula, Laurel Orr, and Surajit Chaudhuri. VLDB 2019. (paper)
- EntropyDB: A Probabilistic Approach to Approximate Query Processing. Laurel Orr, Magdalena Balazinska, and Dan Suciu. VLDB Journal 2019. (paper)
- Probabilistic Database Summarization for Interactive Data Exploration. Laurel Orr, Magdalena Balazinska, and Dan Suciu. VLDB 2017. (paper)
- Explaining Query Answers with Explanation-Ready Databases. Sudeepa Roy, Laurel Orr, and Dan Suciu. VLDB 2015.
- Big-Data Management Use-Case: A Cloud Service for Creating and Analyzing Galactic Merger Trees. S. Loebman, J. Ortiz, L. Choo, L. Orr, L. Anderson, D. Halperin, M. Balazinska, T. Quinn, F. Governato. SIGMOD Workshop on Data Analytics in the Cloud (DanaC) 2014.
- Cluster-Based Approach to a Multi-GPU CT Reconstruction Algorithm. Laurel J. Orr, Edward S. Jimenez, Kyle R. Thompson. Conference Proceedings for the IEEE Nuclear Science Symposium and Medical Imaging Conference 2014.
- Rethinking the Union of Computed Tomography Reconstruction and GPGPU Computing for Industrial Applications. Edward S. Jimenez and Laurel J. Orr. Conference Proceedings for the Penetrating Radiation Systems and Applications XIV Workshop at the SPIE International Symposium on SPIE Optical Engineering+Applications 2013.
- Preparing for the 100-Megapixel Detector: Reconstruction a Multi-Terabyte Computed Tomography Dataset. Laurel J. Orr and Edward S. Jimenez. Conference Proceedings for the Penetrating Radiation Systems and Applications XIV Workshop at the SPIE International Symposium on SPIE Optical Engineering+Applications 2013.
- An Irregular Approach to Large-Scale Computed Tomography on Multiple Graphics Processors Improves Voxel Processing Throughput. Edward S. Jimenez, Laurel J. Orr, and Kyle R. Thompson. Conference Proceedings for the Conference on High Performance Computing Networking, Storage and Analysis, SC 2012, Workshop on Irregular Applications: Architectures and Algorithms (IA^3) 2012.
This is a prototype AQP database system using the Principle of Maximum Entropy. If you want to know more, head to the project page here.
This is a prototype open world database system of automatically debiasing sample data. If you want to know more, head to the project page here.