About

Jun Zhuang is an Assistant Professor in Computer Science at Boise State University. Prior to joining Boise State University, he obtained a Ph.D. degree in Computer Science at Indiana University-Purdue University Indianapolis (IUPUI) in 2023, advised by Prof. Mohammad Al Hasan, and received an M.S. degree in Computer Science from University at Buffalo (UB) in 2018, advised by Prof. Mingchen Gao.
He aims to investigate trustworthy and robust ML/AI systems/algorithms in multiple domains, such as graphs, text, and quantum computing. His recent research improved the robustness of graph neural networks via Bayesian inference models.

Join Trustworthy and Robust AI Lab (TRAIL)

He is looking for highly self-motivated Ph.D. students generally interested in his research topics.
Please send him an email with the title “Ph.D. Application (Fall24) - {Name} - {Research Interests}” and the following information.
1. Introduce your education, technical, and research background in your CV;
2. Introduce your motivation (why do you want to pursue a Ph.D. degree?), your past research experience (what are the connections between both research interests?), your future research interests (how Jun’s research can help you; please feel free to propose any idea that you would like to work with Jun), and your future career plan (what’s your plan after graduation?).
Prospective students are expected to be passionate about research. Strong programming skills and a solid foundation in statistics will be a big plus. Students with other STEM majors are welcome.
How Jun helps his Ph.D. students?
1. Provide weekly hands-on instructions on their research projects;
2. Collaborate with students on top-tier conference (or journal) papers;
3. Help students build strong professional profiles for future careers.

Email: junzhuang [AT] boisestate [DOT] edu

2023-08: Jun attended QSim conference in Telluride, CO.
2023-07: Jun joined the computer science department at Boise State University as an assistant professor.
2023-06: Jun defended his dissertation.
2022-08: Jun is awarded SIGIR Student Travel Grant for CIKM 2022 conference.
2022-08: One paper “Robust Node Classification on Graphs: Jointly from Bayesian Label Transition and Topology-based Label Propagation” is accepted for CIKM 2022 Conference.
2022-02: One paper “How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks?” is published in Neural Process Lett Journal.
2021-12: One paper “Deperturbation of Online Social Networks via Bayesian Label Transition” is accepted for SIAM SDM 2022 Conference.
2021-12: One paper “Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision” is accepted for AAAI 2022 Conference.
2021-06: One paper “Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks” is accepted for ECML-PKDD 2021 Conference.
2021-02: One paper “Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks” is accepted for MICAD 2021 conference.
2019-10: One paper “Into the Reverie: Exploration of the Dream Market” is accepted for IEEE BigData 2019 conference.
2019-10: One paper “Lighter U-Net for Segmenting White Matter Hyperintensities in MR Images” is accepted for MobiQuitous 2019 conference.