About

Junyi Ye is an Assistant Professor in the School of Computing, College of Science and Mathematics, at Montclair State University (faculty profile). His research asks when and why learned models can be trusted under distribution shift, compression, and model editing, and how to make that trust measurable. Working at the intersection of large language models, time series analysis, graph neural networks, and computer vision, he combines rigorous methodology with systems-level engineering, building models and evaluation pipelines that scale to real-world, high-stakes deployment across domains such as engineering, finance, psychology, and decision support.

Junyi received a Ph.D. and an M.S. in Computer Science from the New Jersey Institute of Technology (NJIT), where he was advised by Distinguished Professor Guiling (Grace) Wang and affiliated with the NJIT Fintech Lab and the Center for AI Research. He also holds an M.S. in Optics from Shanghai University, where he studied under Professor Ye Dai.