Some of Yanyu Zhang's most memorable experiences at UC Riverside happened on weekends during the hottest months of the year.
Working alongside fellow researchers, Zhang spent many weekends collecting data for a multiyear project examining how connected vehicles could share information about their surroundings. The work, conducted with electrical and computer engineering professor Hang Qiu, required patience and close collaboration, often under challenging conditions.
"That experience taught me the persistence and teamwork required to carry out a large research project," Zhang said.
The project focused on technologies that allow connected vehicles to exchange information about their environments. Zhang also contributed to CooperScene, a research dataset developed at UCR to help scientists study how autonomous vehicles share information when their individual sensors cannot capture the full picture. The work was selected for a spotlight presentation at the 2026 European Conference on Computer Vision.
Those experiences exposed Zhang to challenges that extend beyond developing an algorithm. Collecting real-world data, working through technical problems, and collaborating across research teams were all part of turning an idea into something that could be tested.
Today, Zhang applies that experience as a staff research scientist at VisionNav Robotics USA Inc., a company developing autonomous industrial vehicles and logistics automation systems. He leads the company's Control, Localization, and Mapping (CLaM) team in the United States and its localization team in China.
His career at VisionNav began before he completed his doctorate. Zhang joined the company as an algorithm engineering intern in December 2024, became a staff algorithm engineer after graduating in March 2025, and advanced to his current position in September 2026.
Much of his work addresses a fundamental challenge in robotics. For a machine to navigate independently, it must accurately determine its location and make sense of the environment around it. Zhang develops and studies methods that combine information from cameras, laser sensors, and motion measurements to help robots accomplish those tasks. His research also explores how artificial intelligence can improve the way autonomous systems perceive and interact with their surroundings.
"I enjoy the connection between mathematical ideas and practical scenarios," Zhang said. "It is rewarding to understand a difficult problem, develop an algorithm, and see how it can improve a robot’s ability to operate in the world."
That connection between theory and application has been a consistent part of Zhang's research, dating back to his doctoral studies in UCR's Department of Electrical and Computer Engineering.
Working with Professor Wei Ren in the Cooperative Vehicle Networks (COVEN) Lab, Zhang investigated how multiple devices could work together to determine their positions and develop a shared understanding of their surroundings. His dissertation explored techniques combining visual information, motion sensing, and learned three-dimensional representations to support multi-user augmented reality, where several devices must maintain a consistent understanding of the same physical space.
The technical foundation he developed at UCR remains directly relevant to his work today. Equally influential were the people and research practices that helped him develop confidence as an independent researcher.
Zhang particularly remembers the weekly reading groups Ren organized in the COVEN Lab. Students would examine a scientific paper in depth, discussing its methods, challenging assumptions, and considering how its findings might inform their own work.
"Prof. Wei Ren’s mentorship and the collaborative environment in the COVEN Lab had a lasting impact on me," Zhang said.
Those discussions strengthened his understanding of visual-inertial odometry and cooperative simultaneous localization and mapping (SLAM), technologies that help machines estimate their movement and build maps of their surroundings. They also taught him to approach new research ideas critically rather than simply accepting published findings.
As Zhang became more comfortable examining complex technical concepts, he gained another perspective on learning through his work as a teaching assistant.
For two years, he worked with Professor Ran Cheng in EE230, Mathematical Methods for Electrical Engineers. Helping students understand challenging mathematical concepts required him to revisit fundamental principles and find clearer ways to explain them. The experience strengthened his own understanding while developing communication skills that would become increasingly valuable in his career.
Today, those lessons carry into Zhang's responsibilities as a research scientist and team leader. His position allows him to remain involved in academic research, publish papers, and participate in robotics and control conferences while working on technologies intended for practical applications.
The transition from university research to industry has also changed how he evaluates technical success. An algorithm might perform well during development, but its usefulness ultimately depends on whether it can operate consistently under the demands of real-world systems.
"My biggest lesson has been that an algorithm’s value depends on how reliably it works," Zhang said. "Accuracy matters, but so do computation time, maintainability, and the ability to handle unexpected conditions."
For current engineering students interested in robotics and artificial intelligence, Zhang emphasizes the importance of building strong mathematical and programming skills while developing an understanding of practical engineering challenges.
"In the age of AI, you can contribute through deep mathematical expertise, a clear understanding of real-world problems, or ideally, both," Zhang said.
He also encourages students to practice communicating their ideas and seek collaborators with different strengths, drawing on lessons he learned through research discussions, fieldwork, and teaching at UCR.
Those lessons have taken Zhang from a doctoral researcher working through complex localization problems to a scientist leading engineering teams in two countries. Less than two years after earning his Ph.D., he is helping guide the development of technologies that bring autonomous robotics into practical applications.
The work has taken him well beyond the UCR laboratories where he began his research, but the problems he tackles remain closely connected to those he studied as a graduate student. Now, with the responsibility of leading others through those challenges, Zhang is putting both his technical training and the collaborative approach he developed at UCR to work in the robotics industry.