The Center for Scalable Architectures and Systems (SAS) carries out groundbreaking research that leads to new areas of parallelism and scalability in computing infrastructure, hardware and software architectures, and algorithms. Researchers representing the center’s focus areas – VLSI circuits, micro-architecture, systems, all the way up to algorithms – are collaborating to achieve the needed breakthroughs.
Center Resources
- Immersive Virtual Studio (IVS) Computing Cluster
- Computer Science System Group Labs
Center Director
Soner Onder
- Professor, Computer Science
- Affiliated Professor, Electrical and Computer Engineering
Center Members
Hongyu An
- Assistant Professor, Electrical and Computer Engineering
- hongyua@mtu.edu
- 906-487-3382
- EERC 612
Courses Taught
- EE 4271 VLSI Design
Areas of Interest
- Neuromorphic Engineering/Computing
- Energy-efficient Neuromorphic Electronic Circuit Design for Artificial Intelligence
- Emerging Nanoscale Device Design
- Spiking Neural Networks
Ali Ebnenasir
- Associate Professor, Computer Science
- aebnenas@mtu.edu
- 906-487-4372
- Rekhi Hall 206
Area of Expertise
- Software Engineering
- Automated Analysis of Fault-Tolerance
- Formal Methods
Daniel R. Fuhrmann
- Chair, Department of Applied Computing
- Dave House Professor of Computer Engineering
- fuhrmann@mtu.edu
- 906-487-2871
- Rekhi Hall 221
Areas of Interest
- Signal Processing
Yakov Nekrich
- Associate Professor, Computer Science
- yakov@mtu.edu
- 906-487-2271
- Rekhi Hall 311
Areas of Expertise
-
- String Algorithms
- Compressed Data Structures
- Computational Geometry
Links of Interest
Research and Teaching Interests
- Data Structures
- Algorithms
Junqiao Qiu
- Assistant Professor, Computer Science
- junqiaoq@mtu.edu
- Rekhi 310
Links of Interest
Research Focus
- Parallel Computing
- Programming Systems
- Compiler Optimization
Zhenlin Wang
- Professor, Computer Science
- zlwang@mtu.edu
- 906-487-2187
- Rekhi Hall 207
Links of Interest
Area of Expertise
- Optimizing Compilers
- High Performance Architectures
- Cloud Computing