Majid Saeedan

Software Engineer | PhD in Computer Science

Specializing in Data Systems, and Geospatial Computing.

Work Experience

Software Dev Engineer
Amazon Web Services – Redshift
East Palo Alto, CA
June 2025 – Present
  • Contributing to query optimization and performance improvements
  • Focus on table statistics and data summarization
Graduate Student Researcher
University of California, Riverside
Riverside, CA
July 2021 – June 2025
  • Design and implement novel data management solutions, leading to publications in top conferences like SIGMOD and SIGSPATIAL
  • Conduct weekly meetings for multiple research projects
  • Assist with research paper reviews at database conferences like VLDB, SIGMOD, and SIGSPATIAL
Software Development Engineering Intern
Amazon Web Services – Redshift
East Palo Alto, CA
June 2024 – September 2024
  • Worked with the query optimization team to evaluate a new feature that derives statistics using sketching algorithms for query planning
  • Built a tool for evaluation and visualization of statistics in Redshift, identifying error categories and suggested solutions to resolve 85%+ of errors
  • Conducted analysis on the effect of the new feature on query plans and identified causes of performance degradations and potential fixes
PhD Intern – Apple Maps Operations Team
Apple, Inc.
Cupertino, CA
June 2023 – September 2023
  • Developed a scalable address maintenance prototype for Apple Maps using spatial joins across large datasets
  • Proposed spatially aggregated metrics to identify bulk resolutions for address changes and pinpoint data issues by region
  • Provided recommendations for automating the prototype as a recurring data summary and visualization process
Research Assistant
King Fahd University of Petroleum and Minerals
Dhahran, Saudi Arabia
October 2018 – May 2020
  • Developed a predictive maintenance system for electrical pumps using deep learning models, improving accuracy in blind tests
  • Built an early prototype dashboard for visualizing pump signal data and testing predictive models
Other experiences
  • Served on SIGMOD’23 Reproducibility Committee.
  • Volunteered at database conferences: SIGSPATIAL’22, ICDE’23, and SIGMOD’23.
  • Teaching Assistant for graduate-level Big Data Management course at UCR.
  • Teaching Assistant for undergraduate-level Introduction to Big Data course at UCR.
  • Delivered a guest lecture on big spatial data and spatial joins to a class of 130 students.
  • Peer Mentor (volunteer) for the International Peer Mentor Program at UCR.
  • Teaching Assistant for the Data Science Summer School for two years at KFUPM.
  • Grader for undergraduate Computer Science courses at KFUPM.

Publications

2025
Geospatial Computing from Data Lakes to Deep Learning Applications
Rlease date: January 18, 2026
Under Review
GeoGen I: Towards General Geospatial Point Data Generation from Text
Under Review
Under Review
Towards Learned Geospatial Analysis & Exploration
Under Review
Under Review
GS-QA: A Benchmark for GeoSpatial Question Answering
Under Review
2023
dsJSON: A Distributed SQL JSON Processor
Proceedings of the ACM on Management of Data, Volume 1, Issue 1
2022
Spatial Parquet: A Column File Format for Geospatial Data Lakes
The 30th International Conference on Advances in Geographic Information Systems (SIGSPATIAL '22)
2021
Beast: Scalable Exploratory Analytics on Spatio-temporal Data
Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM '21)
2020
Evaluating the Robustness of Deep Learning for Time Series Classification
Master's Thesis, King Fahd University of Petroleum and Minerals

Education

PhD in Computer Science
University of California, Riverside
June 2025
M.Sc. in Computer Science
King Fahd University of Petroleum and Minerals
December 2020
B.Sc. in Computer Science
King Fahd University of Petroleum and Minerals
January 2018