KSIS Lab
Research
KSIS Lab conducts research across secure computing, intelligent systems, computer architecture, cybersecurity, cryptography, hardware acceleration, and emerging high-performance computing platforms.
Research Themes
Six interlocking research directions spanning computer architecture, security, cryptography, and intelligent systems.
Secure Computer Architecture
Designing processors and microarchitectures with security as a primary concern, spanning cache and memory integrity, GPU security, and trusted execution environments.
- Hardware Security
- Memory Security
- GPU Security
- Trusted Architectures
- Secure Cache Systems
Chiplet-Based Systems
Investigating disaggregated, multi-die computing architectures and their security, integrity, and interconnect implications for scalable and modular system design.
- Chiplet Security
- Secure Interconnects
- Trusted Computing Base Reduction
- Heterogeneous Chiplet Platforms
Cybersecurity and Privacy
Research in privacy-preserving systems, information-flow enforcement, mobile platform security, IoT security, and automated privacy violation detection.
- Information-Flow Control
- Access Control
- Mobile OS Security
- IoT Security
- Privacy Violation Detection
Cryptography and Quantum Computing
Advancing cryptographic protocols, quantum computation models, and complexity-theoretic foundations for secure and quantum-resilient system design.
- Cryptographic Protocols
- Quantum Computing
- Quantum Federated Learning
- Computational Complexity
Intelligent Systems
Applying machine learning and AI across systems-level problems, including adversarial robustness, federated learning security, and intelligent automation in applied domains.
- Machine Learning Systems
- AI-Enabled Applications
- Adversarial Robustness
- Disease Prediction
- Intelligent Automation
Hardware Acceleration and Data Processing
Domain-specific accelerators, FPGA and GPU computation, high-throughput sorting networks, and compression architectures for data movement and storage in modern systems.
- FPGA Acceleration
- GPU Acceleration
- Sorting Networks
- Compression Systems
- Domain-Specific Accelerators
Cross-Cutting Directions
Our research themes converge around four shared principles that shape how we design, build, and evaluate systems.
Secure Computing
Integrating security into every layer of the computing stack — from microarchitecture and memory to software and policy enforcement.
Intelligent Systems
Applying machine learning and AI methods to systems-level problems, with rigorous attention to robustness and safety.
Hardware/Software Co-Design
Bridging hardware and software through accelerators, FPGA platforms, and co-designed system architectures.
Practical Experimental Evaluation
Grounding all research in measurement, simulation, and prototype implementation against documented baselines.
Explore Further
See our active projects and full publication record across these research areas.