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.