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SNU DBX LAB redefines the boundaries of data-centric systems by innovating at the convergence of database systems and operating systems. We pursue next-generation scalable, reliable, and high-performance platforms for accelerating fast data analytics and AI-driven workloads.
Core Research Focus Areas
Core Research Focus Areas
Database-Oriented Operating Systems (DBOS):
Explore innovative architectures seamlessly integrating data management, analytics, and real-time processing.
Invent a new DB-OS co-design that merges the power of DBMSs with OSs to supercharge data processing speeds.
Fast Vector-Relational Database Platforms:
Harness distributed and parallel computing to extract insights from structured, unstructured, and vector data.
Drive innovations in lakehouse systems, creating end-to-end analytics pipelines that revolutionize how we process and analyze data.
Parallel and Distributed Deep Learning:
Innovate scalable algorithms designed to run efficiently on multi-GPU and multi-node clusters, pushing the boundaries of model performance.
Explore novel approaches to optimize communication, synchronization, and load balancing across distributed deep learning frameworks.
Hardware-Accelerated Data Processing Systems:
Develop high-performance data platforms that leverage advanced hardware innovations—such as CXL memory, specialized accelerators, and next-generation computing architectures—to boost data processing and model training speeds.
Integrate state-of-the-art hardware with modern database architectures, enabling efficient large-scale data ingestion and real-time analytics.
Prospective Students
Prospective Students
We're looking for highly motivated students at all levels (Undergraduate interns, Master's, PhD, and MS/PhD Integrated) to join our research on database systems and related areas. A strong background in systems programming and algorithms is essential (relevant courseworks include Database Systems, Operating Systems, etc.).
Please send an email to Professor Hyungsoo Jung with your CV, academic transcript, and informal statement of your research interests. You would also need to go through the admission process of the Graduate School of Data Science at SNU, so please reach out as early as possible before your intended application cycle.
News
News
(Dec 2024) Our new name: DBX Lab!
Recent top-tier publications
Recent top-tier publications
“Rapid Data Ingestion through DB-OS Co-design,” ACM SIGMOD 2025 (to appear)
“Deploying Computational Storage for HTAP DBMSs Takes More Than Just Computation Offloading,” VLDB 2023
“DIVA: Making MVCC Systems HTAP-Friendly,” ACM SIGMOD 2022
“Rethink the Scan in MVCC Databases,” ACM SIGMOD 2021 (Best Paper Honorable Mention Award)
“Long-lived Transactions Made Less Harmful,” ACM SIGMOD 2020
“Border-Collie: A Wait-free, Read-optimal Algorithm for Database Logging on Multicore Hardware,” ACM SIGMOD 2019
“Scalable Database Logging for Multicores,” VLDB 2018
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