Research
Our Research
Our research is dedicated to advancing the next generation of intelligent data and computing systems by holistically integrating innovations across seven key domains.
Big Data / Database & Cloud Systems
High-performance, AI-centric data platforms that unify diverse data and enable intelligent data infrastructure at scale.
Hardware–Software Co-Design
Co-optimizing algorithms, software, and emerging hardware for data- and AI-intensive workloads.
Systems for AI
System infrastructures optimized for AI workloads across diverse environments, bridging algorithm design and real-world deployment.
Quantum Data & AI
Exploring quantum computing to unlock new capabilities in data management and machine learning.
AI for Systems
Leveraging ML to build self-optimizing systems with learned optimization, predictive scheduling, and adaptive management.
Data + AIX (Data-Centric AI Apps)
Developing domain-focused AI applications using LLMs and advanced AI techniques for real-world impact.
AI Model & Architecture Innovation
New paradigms for large-scale AI models with a focus on accuracy, efficiency, and scalable deployment.
Research Areas
- • AI-Centric Data System Design
- • Vector Database System Design
- • GPU-Accelerated Database System Design
- • Cloud-Native Data System Architectures
- • Distributed Data Processing and Cloud Data Platforms
- • HTAP (OLTP/OLAP) Systems Design
- • Query Optimization / Execution
- • Tensor-Relational Hybrid Query Processing
- • Data Lakehouse System Architecture
- • Multi-Model and Multimodal Data Management
- • Learned and Adaptive Indexing Techniques
- • Data-Centric AI Pipeline Integration (ML-in-DB / DB-in-ML)
- • Scalable Infrastructure for AI Model Training
- • High-Performance Inference Systems
- • Distributed and Heterogeneous Systems for Deep Learning
- • System Software for AI Workload Scheduling and Resource Management
- • AI Cluster Resource Scheduling and Job Orchestration (e.g., Kubernetes, Ray)
- • Model Serving Systems and Low-Latency Inference Serving
- • Model Weight Sharding and Storage Optimization
- • Security and Privacy-Preserving AI Systems
- • Data/Model/Pipeline Parallelism Infrastructure
- • Machine Learning-Based Query Optimization
- • Learned Indexes and Data Structure
- • AutoML for Database and System Configuration
- • AI-Guided Scheduling and Job Placement
- • Predictive Modeling for Resource Management
- • Workload Forecasting and Autoscaling
- • AI-Powered Storage Tiering and Caching
- • Reinforcement Learning for System Parameter Tuning
- • AI-Augmented System Profiling and Bottleneck Detection
- • Data Placement and Partitioning via ML Techniques
- • ARM/Diffusion LLM/MLLM Finetuning and Domain Adaptations
- • Korean Language Model Pretraining and Development
- • Medical Domain Data + AI Applications
- • Data Science Domain Data + AI Applications
- • Social Science Domain Data + AI Applications
- • LLM Architecture Optimization (e.g., MoE, weight sharing, KV cache compression)
- • Diffusion Model Acceleration for training and inference (e.g., distillation, scheduler design)
- • Multimodal Foundation Models (text-image-video-audio integration)
- • Domain-specialized Architecture (e.g., medical, legal, scientific LLMs)
- • Continual and Lifelong Learning Model Design
- • Architectures for tight integration of memory, storage, and compute resources
- • Co-optimization of query engines and machine learning pipelines with emerging hardware (e.g., GPUs, TPUs, NPUs, DPUs, CXL)
- • In-memory and near-data processing for high-throughput analytics
- • CXL-enabled memory disaggregation and composable infrastructure
- • Hybrid quantum-classical machine learning systems
- • Quantum-inspired optimization for large-scale systems
- • Data encoding and preprocessing in quantum systems
- • Quantum algorithms for data representation and search
Join Us
We seek motivated students and researchers who want to grow as independent scholars and contribute to high-impact data and AI-centric research.
M.S. / Ph.D. Students
We welcome motivated graduate students who want to grow as independent researchers. Students are expected to identify important problems, design rigorous solutions, conduct careful experiments, and communicate their work through high-quality publications.
- Develop research taste through paper reading, discussion, and problem formulation
- Design and evaluate data and AI-driven approaches, and publish at leading venues
- Collaborate with academic and industry partners on impactful research
Postdoctoral Researchers
We welcome postdoctoral researchers interested in leading data and AI-centric research with strong academic and real-world impact. You will have opportunities to define independent research directions, collaborate with international partners, and contribute to high-impact publications.
- Independent research leadership
- International and industry collaboration
- Publication-driven academic career development
Undergraduate Interns
Undergraduate students can join research projects and develop hands-on experience in data and AI research.
- Research-oriented internship opportunities
- Mentoring from graduate students and faculty
- Pathway to advanced graduate research
Please email your CV, transcript, and brief research interests to Prof. Kwanghyun Park.