About Me

I am a sixth year PhD graduate student in the Database Group at the Paul G Allen School for Computer Science and Engineering at the University of Washington in Seattle. I am currently advised by Dan Suciu and Magdalena Balazinska. I will graduate in August of 2019 and take a postdoc at a university TBA.

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.

Publications

  • EntropyDB: A Probabilistic Approach to Approximate Query Processing. Laurel Orr, Magdalena Balazinska, and Dan Suciu. VLDB Journal. 2019. (under submission)
  • 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.

Projects

These projects are past or present projects or collaborations.

EntropyDB

This is the prototype database system I built for my AQP research using the Principle of Maximum Entropy. If you want to know more, head to the project page here.

Myria

MyriaWeb

This is the Database Group's big data management system. I mainly just help with debugging, but if you want to know more, head to their website.

MyMergerTree

MyMergerTree

This work is in collaboration with the Astronomy Department at the University of Washington and aims at helping the astronomers build and visualize galactic merger trees. I and another member of the Database Group, Jennifer Ortiz, worked with the astronomers to build a tool to take raw particle simulation data and generate visualizations of how the particles formed galaxies over time. For more information, go here and scroll down to the MyMergerTree Service section.