🚀(she/her)
Human–Machine Collaboration for Biodiversity Monitoring
Hello, I am a Ph.D. candidate in Computer Engineering at Colorado State University, advised by Professor Anthony A. Maciejewski and Professor Agnieszka Miguel at Seattle University. My research focuses on developing efficient machine learning and computer vision algorithms for image-pattern recognition that minimize human involvement. While my primary application is wildlife conservation (African leopards), the methods are extendable to domains such as person identification and medical imaging. In addition, I actively contribute to the academic community through paper reviewing and service as an associate editor for the course International Conference on Systems, Man, and Cybernetics (SMC) . I have also gained teaching experience as a teaching assistant for the course Introduction to Robot Programming/Simulation, ECE 455. Previously, I received my B.S. degree in Electrical Engineering and Automation from University of Jinan.
Beyond research, I enjoyed traveling, hiking, and photography, often documenting the world through my own experiences. Hiking, in particular, resonates with me: even when I know where the summit lies, the climb can still be a struggle, and on the way back, although the hardest part is behind me, my energy is nearly spent. To me, this mirrors the research journey, challenging, exhausting, but ultimately rewarding.
PhD Computer Engineering
Colorado State University, USA
MS Computer Engineering
Colorado State University, USA
BS Computer Science
University of Jinan, China
My current research focuses on developing automated algorithms for individual animal identification that minimize human involvement while maintaining high reliability, leveraging clustering, verification, and human-in-the-loop supervision. In particular, I work with small-scale, real-world, unlabeled camera trap image sets of African leopards.
More broadly, I am passionate about applying machine learning and computer vision to diverse image-pattern recognition challenges, including wildlife conservation, person identification, and medical image analysis. I aim to bridge advanced AI techniques with real-world problems, creating tools that enable scientists and practitioners to make data-driven decisions with greater efficiency and accuracy.
Outside of research, I share insights through academic publications, conference presentations, and collaborative projects. I am always open to connecting with researchers and practitioners interested in conservation technology and applied AI. Please feel free to reach out if you would like to collaborate.😃
This article describes an algorithm to solve the real-world animal identification problem, i.e., determine the unknown number of K individual animals in a dataset of N unlabeled …
A news feature by **The Rocky Mountain Collegian** highlighting Cheng Guo’s AI-driven leopard identification research.
A research feature by Colorado State University highlighting Cheng Guo’s machine learning work for identifying endangered leopards.
An interview by the College of Engineering at Colorado State University featuring Cheng Guo’s research on AI for individual leopard identification.
