About Me

I am a fifth year PhD graduate student in Computer Science and Engineering at the University of Washington in Seattle. As I started this program right after receiving my undergraduate degree, my expected graduation date is no time soon (which is nice because I really enjoy Seattle). 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. Being a true nerd at heart, when my senior year at Carleton started, I realized I was I was in love enough with Computer Science (or just plain crazy enough) to enroll in a PhD program.

If you're curious about my life before undergraduate, I grew up in Denver, Colorado with my parents, older sister, and cats. As a side note, I am a huge cat person - not in a crazy, old lady way but in a "cats are man's best friend" way. When I went to college, my parents moved to Albuquerque, New Mexico where food is served Christmas-Style (smothered in half red chili and half green chili). Being that I don't like chili, Albuquerque is not my favorite place to live, but one of its redeeming qualities is The Albuquerque International Balloon Fiesta. It is a sight to see, and I highly recommend it.

Research and Work Experience

I am currently advised by Dan Suciu and Magdalena Balazinska and am part of the University of Washington's Database Group. I am also one of the 2015 winners of the NSF GRFP in Computer Science. My research is currently focused on automatically generating a query-able summary of data that can the be used for running ad-hoc, exploratory, and approximate queries. Departing from traditional summarization techniques, I use the Principle of Maximum Entropy to generate a probabilistic representation of the data that can be used to give approximate, highly probable solutions. My other broad research interests include Machine Learning and Data Visualization

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 2008 to the spring of 2015, I interned at Sandia National Laboratories working on high performance computing and image reconstruction.


  • Probabilistic Database Summarization for Interactive Data Exploration. Laurel J. Orr, Magdalena Balazinska, and Dan Suciu. VLDB. 2017. (paper)
  • 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.


These projects are past or present projects or collaborations.


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.



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.



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.