Ahmed Eldawy is an Assistant Professor in Computer Science at the University of California Riverside. His research interests lie in the broad area of databases with a focus on big data management and spatial data processing. Ahmed is the main inventor of SpatialHadoop, the most comprehensive open source system for big spatial data management. Ahmed has many collaborators in industrial research labs including Microsoft Research and IBM Watson. He was awarded the Quality Metrics Fellowship in 2016, Doctoral Dissertation Fellowship in 2015, and Best Poster Runner-up award in ICDE 2014. His work is supported by the National Science Foundation (NSF) and the US Department of Agriculture (USDA).





USDA NIFA - 2020-69012-31914 - Artificial Intelligence for Sustainable Water, Nutrient, Salinity, and Pest Management in the Western U.S.

Total amount: $10,000,000 (PI share: $636,393)
From 9/01/2020 - 8/31/2025

  • PI: Elia Scudiero - Environmental Sciences UCR - USDA Salinity Lab
  • Ahmed Eldawy - UCR
  • Vagelis Papalexakis - Computer Science and Engineering - UCR
  • Milt McGiffen - Department of Botany and Plant Sciences - UCR
  • Kurt Schwabe - School of Public Policy - UCR
  • Hoori Ajami - Department of Environmental Sciences - UCR
  • Khaled Bali - Kearney Agricultural Research & Extension Center - UC ANR
  • Michael D. Cahn - Irrigation and Water Resources, University of California Cooperative Extension
  • Alexander I Putman - Microbilogy & Plant Path - UCR
  • Ray Anderson - USDA Salinity Lab
  • Todd Skaggs - USDA Salinity Lab
  • Andrew French - US Arid-Land Agricultural Research Center at Arizona
  • Karletta Chief - Department of Soil, Water and Environmental Science, University of Arizona
  • Charles A Sanchez - Department of Soil, Water and Environmental Science, University of Arizona
  • Nathaniel Chaney - Civil and Environmental Engineering Department, Duke University
  • Raj Khosla - Department of Soil & Crop Sciences, Colorado State University
  • George Vellidis - Crop & Soil Sciences - University of Georgia
  • Monique Joy Rivera - Department of Entomology - CMU




Beast is a system for Big Exploratory Analytics on Spatio-temporal data. It adds many Spark-based functions for loading, indexing, analyzing, visualizing, and summarizing big spatio-temporal data. UCR-Star is one example of a system that is built using Beast.

UCR-Star website


UCR STAR the spatio-temporal active repository that hosts terabytes of public geospatial data in an interactive repository. The main goal is to allow the researchers worldwide to unleash the true value of the datasets that are available all over the web. We encourage the community to submit their requests to add new datasets to UCR-Star and we will be process them.



    Raptor is the Raster+Vector query processing engine built in Spark. Raptor is designed to efficiently combine raster data, e.g., satellite images, with vector data, e.g., roads and boundaries, in one efficient query processing core. Raptor has already been applied in many scientific applications including crop yield estimation and combating wild fires.


       Spider (

      Spider is a web-based spatial data generator that aims at making synthetic datasets easier to generate, visualize, and share. With Spider, you can generate billions of records of synthetic data and share them with your project members with a simple web link.


      I am an accredited Scientific Teaching Fellow by Yale Center for Teaching and Learning sponsored by the National Science Foundation.

      • CS 167 - Introduction to Big-data [Sptring 2020, Spring 2021]: CS 167 introduces students to the big-data ecosystem including storage, processing, and analysis.
      • CS 226 - Big-data Management [Winter 2018, Fall 2018, Fall 2019, Fall 2020]: CS 226 covers the data management in big data platforms such as Hadoop, Spark, and AsterixDB.
      • CS 133 - Computational Geometry [Spring 2018, Spring 2019]: CS 133 covers the fundamentals of computational geometry.
      • CS 010c (Formerly CS014) - Introduction to Data Structures and Algorithms [Fall 2017]: CS 014 introduces the students to the fundamental data structures and algorithmic analysis techniques such as lists, stacks, queues, search trees, sorting algorithms, hash tables, and graphs.
      • CS 267 - New Trends in Database Research [Fall 2016]: The goal of this course is to introduce the students to the latest research trends in database systems. The course is based on the recent research literature in major database conferences and journals. By the end of this course, students should be aware of the active research topics in database systems and possibly identify new research topics of their own. Students will also get a hands-on experience by working on a research problem of their choice individually or in groups.
      • CS 141 - Intermediate Data Structures and Algorithms [Winter 2017, Winter 2019,Summer 2019,Winter 2020,Winter 2021]: CS 141 provides the basic background for a computer scientist in the area of data structures and algorithms. During this course, students will learn problem solving skills, how to compare them, and how to apply them in real problems.


      PhD Stduents
      Master Students
      • Ponmanikandan Velmurugan
      • Bharath Mysore Nagendra
      • Ankitha Sathyanarayana
      • Abraham Palaniswamy Miller
      •  Ganesh Krishnan Sivaram
      •  Bhavana Vangala
      •  Zoama Hassan
      •  Mehrad Amin Eskandari
      •  Jonathan Peng
      •  Lek Tin
      •  Jiaqing Chen
      •  Husna Sayedi
      •  Shipra Jais
      •  Akarsha Byadarahalli-Mahadeva
      •  Lyuye Niu
      Undergraduate Students
      • Andre Tran
      • Vinayak Gajjewar
      • Nikhil Kadekar
      • Puloma Katiyar
      • Hsiang-Yin Hsieh
      • Yaming Zhang
      • Steven Li
      • Qiwen (Nelly) Lyu
      • Zhiba (Ryan) Su