Research/About
Hello. I'm a PhD student in computer science at The University of Oregon. I study the design of parallel algorithms with particular care towards large-scale scientific computing applications.
Applications such as direct numerical simulation, tensor decomposition, uncertainty quantification, and statistical machine learning catch my interest because they are among a narrow number of activities that require vast quantities of data and appropriately accommodating computing resources.
My work seeks to both optimize existing applications and to find new & novel algorithms that more effectively utilize our available resources.
Email josephmcl « at » protonmail « dot » ch
Technical Publications
- McLaughlin J., Choi J., SMOOTH: Shared Memory Optimal Orthogonal Tiling for Hybridized PDEs. In submission.
- McLaughlin J., Choi J., Durairajan R., ×Grid: A Location-oriented Topology Design for LEO Satellites. LEO-NET 2023—1st International Workshop on LEO Networking and Communication at ACM MobiCom 2023, Madrid, Spain, 2023 October 2—3. PDF DOI
- McLaughlin J., Young M., Rockcastle S., Fine-grained measurement of the indoor built environment with robotic vacuum cleaners. BS2021—17th International Conference of the International Building Performance Simulation Association, Bruges, 2021 September 1–3. PDF DOI
- Flores J., Fuentes R., McLaughlin J., Novitzky K., Schofield S., Springel A., Tal S., Pipeline Trees - An Auxiliary Tool in the Creation of Time Series Pipelines, ITISE2021—International Conference on Time Series and Forecasting, Gran Canaria, 2021 July 19–21. PDF
Reports
- McLaughlin J., A high-performance profile of hybridized PDEs. Technical report. University of Oregon, Eugene, Oregon, 2023 November 28. PDF TALK
- McLaughlin J., Scoping study: high-efficiency X-ray sources for STARBRIGHT. Lawrence Livermore National Laboratory (LLNL) internal audience, publicized. 2023 September 12. PDF