Meet the Faculty - Caiwen Ding

Tell us about your journey to the University of Minnesota.

I began my academic journey with a bachelor's degree in electrical engineering, where my focus was on power systems. While I appreciated the technical challenges, I found that the relatively heavy mathematical nature did not align with my primary interests. This led me to pursue a PhD in computer engineering, concentrating on electronic design automation (EDA). In 2016, I was introduced to deep learning and began developing accelerators and systems to enable efficient deep learning.

After earning my PhD from Northeastern University, I joined the University of Connecticut as a faculty member, specializing in AI algorithm and hardware co-design. During my interview at the University of Minnesota, I was deeply impressed by the Department’s outstanding research programs, strong reputation in the computer science community, and dedication to innovation across disciplines. The chance to collaborate with experts in AI, robotics, human-centered computing, computing foundations, and computing education was particularly inspiring. Joining this exceptional community has been a rewarding experience, and I feel truly honored to be a part of it.

We would love to hear more about your research!

My research mainly focuses on developing efficient system support for AI and leveraging AI to enhance system development. This includes exploring various computing platforms such as GPUs, FPGAs, ReRAM, and optical technologies. With the rapid growth of large language models, we are creating AI agents to address significant challenges in computing systems. For instance, we are developing AI agents that generate and debug code in Python, Fortran, and C++, as well as generate Verilog for chip development. We also work on privacy-preserving techniques to enhance the security of machine learning as a service and protect user data. Additionally, we are developing real-time machine-learning systems for applications in transportation, agriculture, and medicine.

What do you hope to accomplish with this work? What is the real-world impact for the average person?

Our goal is to democratize advanced AI technologies by making them more efficient, accessible, and environmentally friendly. By optimizing AI models to operate efficiently on affordable hardware, we reduce the need for expensive equipment and large-scale computational resources. Ultimately, we hope to bridge the gap between cutting-edge AI technologies and practical, real-world applications.  

What courses are you teaching in the future? What can students expect to get out of that class?

This semester, I taught CSCI/EE 4203/4363 - Computing Architecture, a course offered jointly with the Department of Electrical and Computer Engineering. Drawing from my background in computer engineering and computer science, I focused the class on the intersection of hardware and software. My hope is that students gain an interdisciplinary experience, with ECE students learning more about software and CS&E students gaining a deeper understanding of hardware. I plan to teach the same course again in the spring.

What do you do outside of the classroom for fun?

For more than 10 years, I have been playing badminton, which is my favorite sport.

Do you have a favorite spot in the city?

I enjoy visiting the Mall of America and the Minnesota Landscape Arboretum.

Is there anything else you would like students to know about you?

I am passionate about interdisciplinary research and education. I am excited to develop new courses, such as a graduate-level class on embedded machine learning that connects theoretical algorithms to real-world implementations. I am grateful to be part of this community and am always open to collaborating with students.