Tag: Computer Science

I began my Ph.D. journey at Michigan Technological University in Fall 2018, joining the Department of Computer Science under the guidance of Dr. Scott Kuhl. Over the years, my research has spanned diverse fields, including computer graphics, human perception, and augmented and virtual reality (AR/VR). My dissertation, in particular, focuses on developing innovative interaction techniques for AR systems.

In this work, previously supported by funding from the National Science Foundation (NSF), I explore the use of acoustic sensors to transform everyday surfaces into intuitive interactive interfaces. By detecting and localizing taps on surfaces through advanced signal processing and algorithms, my system offers a natural, portable, and seamless way to interact with AR technologies. This approach addresses the limitations of traditional input methods in immersive environments, integrating AR more effectively into daily life and making it more accessible.

I am deeply grateful to Michigan Tech’s Graduate School and the Graduate Dean Awards Advisory Panel for awarding me the Finishing Fellowship, which enables me to focus on completing my research. I extend heartfelt thanks to Dr. Scott Kuhl and Dr. Keith Vertanen for their invaluable mentorship, which has been pivotal in navigating the challenges of interdisciplinary research.

Looking ahead, I am excited to share my findings with the academic and professional community, contributing to advancements in AR technology and enhancing how humans connect with the digital world.

I started my Ph.D. program in the summer of 2019 under the guidance of Dr. Jianhui Yue in the Computer Science Department at Michigan Technological University. My research focuses on near memory acceleration for Approximate Nearest Neighbor (ANN) search.

ANN search finds approximate nearest points in high-dimensional spaces, offering a balance between accuracy and speed. It’s widely used in fields like image retrieval and recommendation systems. Our work has centered on designing new hardware components and circuits to enable large-scale computations within memory, reducing the overhead of CPU-memory data transfers. We also implemented optimization techniques to further accelerate the search process. These contributions have significant implications for ANN research work, enhancing both performance and efficiency.

I extend my heartfelt gratitude to the Graduate School and the Graduate Dean Awards Advisory Panel for awarding me the Doctoral Finishing Fellowship. I am honored to receive this recognition, which inspires me to continue pursuing excellence in my Ph.D. studies and future endeavors. Special sincere thanks go to my advisor, Dr. Jianhui Yue, and the Department Chair, Dr. Zhenlin Wang, and everyone in the College of Computing, for their motivation, guidance, and support throughout my Ph.D. period, I deeply appreciate their encouragement.

I began my academic journey at Michigan Technological University in the fall of 2016, starting with majors in Computer Engineering and Mathematics. Over the years of my undergraduate studies, I found myself increasingly drawn to the area of computer systems optimization. My first experience with computer science research came in 2019 under the guidance of Professor Zhenlin Wang, focusing on LRU cache modeling. This pivotal experience deepened my interest in computer memory systems and ultimately led me to pursue a Ph.D. in Computer Science in fall of 2021, continuing under the mentorship of Professor Wang. My research focuses on the modeling and optimization of software caching systems, motivated by the challenges posed by diverse and variable software caching workloads and the critical need for efficient use of memory to reduce resource costs. The optimization involves developing dynamic cache configuration strategies that adapt in real-time to these changing workloads, thereby improving resource allocation across caching clusters to minimize waste and maximize system performance.、

I am honored to receive the Finishing Fellowship and would like to extend my sincere gratitude to the Graduate School and the Graduate Dean Awards Advisory Panel. I also want to thank my advisor, Prof. Zhenlin Wang, for his invaluable guidance and support throughout my graduate studies.