Graduate (List)

This course covers advanced research topics in Systems research for Artificial Intelligence and Machine Learning. The course is designed to cover system software, distributed machine learning frameworks, and AI applications in the context of Cloud and Mobile computing. We will carry on an in-depth study on the environment in which AI applications run including NPU, GPU, CPU and mobile APUs as well as software systems design to run AI applications on various platforms.

This course covers topics of interest in electrical engineering at the graduate level. The course content is specifically designed by the instructor.

Much progress has been made in quantum information technologies in recent years. The course provides an overview of the recent progress. It focuses on fundamental and theoretical tools that enable quantum information applications to outperform today’s information processing. Quantum signal processing, quantum state distinguishability, entanglement verification, quantum optimization, quantum networks, etc. are reviewed

This course covers topics of interest in electrical engineering at the graduate level. The course content is specifically designed by the instructor.

This course covers topics of interest in electrical engineering at the graduate level. The course content is specifically designed by the instructor.

This course covers topics of interest in computer engineering to students at the graduate level. The contents of this course are specifically designed by the instructor.

Modern flash-based solid state disk (SSDs) can be plagued by enormous performance variations depending on whether the underlying architectural complexities and flash management overheads can be hidden or not. Designing a smart flash controller and storage system is key hiding these architectural complexities and reducing the internal firmware overheads. In this course, we first understand the core components of SSD architecture and key concepts behind flash firmware. It then presents a set of novel storage optimizations including various concurrency methods, I/O scheduling algorithms, and garbage collection avoidance mechanisms. Lastly, the lectures will cover simple basic file system and storage stack

The objective of the course is to understand key concepts and techniques required for the explainable AI(XAI) and DL acceleration system. Lecture will cover core methodology for XAI in a variety of detailed areas, such as visual explanation, attention mechanisms, human-AI collaboration, explainable reinforcement learning (RL), explainable graph neural networks (GNNs), etc. We also introduce the acceleration methodology for training/inference of explainable models in the perspective of system frameworks.

In this work, we cover a broad range of the materials of the operating systems. The topics include basic operating system structures, memory management, storage and filesystem, process scheduling and resource management, virtualization, and distributed systems. We will read the essential papers for each topic. All papers in the reading list is a must read for the students who want to do computer systems related research. The papers in the reading list range from the classic reading, “THE” operating system(CACM 1968), to the recent one such as BarrierFS (FAST 2018).

Interconnection networks are critical in computer systems because they facilitate efficient communication and data transfer between various components, such as processors, memory, and input/output devices. These networks underpin the performance and scalability of parallel and distributed computing systems by enabling high-speed data exchange and minimizing latency. Effective interconnection networks ensure that data can be rapidly and reliably transmitted across the system, which is essential for executing complex computations and handling large-scale data processing tasks.