Riverside, California, USA
zshan011@ucr dot edu
Résumé
Work Repositories
I am Zhuocheng Shang,
a Ph.D. candidate in Computer Science at
University of California, Riverside,
advised by Prof. Ahmed Eldawy.
Research Interests:
Distributed Data Systems
Spark Query Engines
Geospatial Analytics
Raster-Vector Processing
Agentic LLM Systems
AI for Scientific Workflows
Current Research
I build high-performance geospatial data infrastructure and AI agents that make it easier to run scientific workflows at scale. My systems work focuses on Spark/Scala execution engines for terabyte-scale raster and raster-vector workloads, including data models, partitioning strategies, shuffle reduction, memory-aware execution, and interactive visualization.
My current agentic AI work builds LangGraph/RAG systems that translate Python or plain-language scientific workflows into executable distributed programs. I am especially interested in agents that can select APIs, diagnose compiler and runtime failures, reuse prior fixes, and assemble successful data pipelines under real system constraints.
Publications
2026
GRAIL: AI Translation for Scientists' Application Workflow on Satellite Data,
VLDB Demo, 2026.
Geo-ReAct: Concept-Grounded Agentic Framework for Geospatial QA,
SIGSPATIAL, 2026.
GS-QA2: A Benchmark for Question Answering over Raster-Vector Data,
SIGSPATIAL, 2026.
2025
AgriParcel: Geometry Farmlands Generation Framework for Comprehensive Agricultural Analysis,
In Proceedings of the 8th ACM SIGSPATIAL Workshop on GeoAI, November, 2025.
FieldSAT: A Scalable Query Workflow for Precision Agriculture with Large Raster Datasets,
In Proceedings of the 33rd ACM SIGSPATIAL, November, 2025. Best Application Paper.
Demonstration of FutureFarmNow: Interactive Spatial Data Exploration for Precision Agriculture,
In Proceedings of the 33rd ACM SIGSPATIAL, November, 2025.
Scalable Raster Processing: Models, Systems, Algorithms, and Open Challenges,
In Proceedings of the 33rd ACM SIGSPATIAL, November, 2025.
2024
RDPro: Distributed Processing of Big Raster Data, In Proceedings of VLDB, 2024.
DynoViz: Dynamic Visualization of Large Scale Satellite Data, In Proceedings of the 12th SIGSPATIAL
Workshop on BigSpatial, September, 2024.
2023
Viper: Interactive Exploration of Large Satellite Data, In Proceedings of the 18th SSTD, August, 2023.
Top 5 Paper.
2022
Object Delineation in Satellite Images, In Proceedings of the 4th ACM SIGSPATIAL International Workshop
on Spatial Gems, November, 2022.
Work Experiences
June - Sep, 2025Software Engineering Internship @ Wherobots
Software Engineering
Implemented a new Geography data type in Apache Sedona, developing complete object representations, serialization pipelines, constructor functions, spatial functions, and seamless conversion mechanisms between Geometry and Geography types.
Education
2021 - PresentUniversity of California, Riverside
PhD Student
2019 - 2021
University of California, Riverside
M.S. Computer Science
2014 - 2019
Lindenwood University, Missouri
B.S. Computer Science
Talks
Tutorial talk at SIGSPATIAL: Scalable Raster Processing: Models, Systems, Algorithms, and Open Challenges (November, 03, 2025)UCR DS Fellowship workshop: Intro to Satellite Data processing (August, 07, 2023)
UCR gradquant workshop: Bringing Satellite Data Down to Earth (May, 17, 2023)
Teaching Assistant
CS 167 - Introduction to Big Data (Winter 2023)CS 226 - Big-data Management (Fall 2022)
Award
First winner at SIGSPATIAL SRC, 2025End-to-end raw scalable raster processing with customized pipeline and foundation model integration, In Proceedings of the 33rd ACM SIGSPATIAL, November, 2025.