About Me

• [Bio] Jun Zhuang is an Assistant Professor of Computer Science at Boise State University (BSU). Prior to joining BSU, he earned a Ph.D. degree in Computer Science at Purdue University in Indianapolis (PIN, previous IUPUI) in 2023, advised by Prof. Mohammad Al Hasan, and obtained an M.S. degree in Computer Science from University at Buffalo (UB) in 2018, advised by Prof. Mingchen Gao. Besides, he received an M.S. degree in Finance from the Rochester Institute of Technology (RIT) in 2013 and was awarded a B.E. degree in Safety Engineering from South China University of Technology (SCUT) in 2011.
• [Research Interests] He aims to develop trustworthy and robust machine learning algorithms (or deep learning models) to address critical challenges in various domains, such as medicine and healthcare, bridging classical AI with Quantum Machine Learning (QML). Feel free to check out his Google Scholar profile for more details.

Team

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 does Jun help 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.

Current Students

• Ph.D. Student(s): Maqsudur Rahman.
• M.S. Student(s): Chia-Ying Wu, Shipra Kumari.

Alumni

• M.S. Student(s): Jack Cunningham.

Teaching and Mentoring

Instructor

• CS434 Applied Deep Learning (Sp24), BSU
• CS533 Introduction to Data Science (Fa23-25), BSU
• CS597 Deep Learning (Sp25), BSU
• CS695 Capstone Course (Sp25), BSU

Mentor

• DS REU: Data Science of Risk and Human Activity (Su19), PIN
• Blockchain REU: Embracing Blockchain for a Secure and Trustworthy Tomorrow (Su24-25), BSU
• LSAMP REU: Louis Stokes Alliances for Minority Participation (Fa24), BSU

Services

Internal Services

• Served on the CS graduate committee. (Fa23 - now)
• Served on the PhD admission committee. (Sp24 - now)
• Served on the AI faculty search committee. (Fa24)

External Services

• Reviewer(s): ICML, NeurIPS, ICLR, AAAI, ECAI, ICCV, MICCAI, SIGKDD, CIKM, ECML-PKDD (DISTINGUISHED PC MEMBER’25), AISTATS, QCE, TKDE, TNNLS, etc.
• Panelist(s): NSF’24, DOE’24.
• Session chair(s): CIKM’24.
• Workshop co-organizer(s):
  1. Trustworthy and Responsible AI (TRAI) @ CIKM’24.
  2. Quantum Computing and Reinforcement Learning (QCRL) @ QCE’25.

Professional Experience [LinkedIn]

• Assistant Professor, Boise State University, ID (Jul23 - present)
• Ph.D. Software Engineer Intern, Uber Technologies, Inc., CA (Su22)
• Algorithms and Advanced Analytics Intern, Roche Diabetes Care, Inc., IN (Su21)
• Research Intern, The University of Tennessee, Knoxville, TN (Su20)
• Foreign Exchange Trading Specialist, China Merchants Bank Co., Ltd., China (Jan14 - Jul16)

Contact

• Email: junzhuang [AT] boisestate [DOT] edu
• Office: CCP 354
• Address: 777 W Main St., Boise, ID 83702

News

• 2025-12: Jun attends the 36th International Symposium on ALS/MND in San Diego, CA.
• 2025-11: Jun is recognized as a Distinguished Program Committee Member for the Research Track of ECML-PKDD 2025.
• 2025-11: Two papers are accepted to AAAI 2026: “QAPNet: A Quantum-Attentive Patchwise Network for Robust Medical Image Classification under Noisy Inputs” (Main track, AR 17.6%) and “Fair Graph Learning with Limited Sensitive Attribute Information” (AISI track, AR 24.1%). Big congrats to our brilliant collaborators and Maqsudur!
• 2025-09: One paper, “Enhancing the Trainability of Variational Quantum Circuits with Regularization Strategies”, is presented in the QML workshop @ QCE 2025 in Albuquerque, NM. Also, Jun co-organizes a QCRL workshop and is honorably invited to be a mentor in the Student Speed ​​Mentorship Session.
• 2025-08: One paper, “Fairness-Aware Graph Representation Learning with Limited Demographic Information”, won the Best Student Paper Award at ECML-PKDD 2025! Huge congrats to my brilliant collaborators.
• 2025-08: One survey paper, “Deconstructing the ethics of large language models from long-standing issues to new-emerging dilemmas: a survey”, is published in the AI & Ethics journal.
• 2025-08: One paper, “Exploring the Vulnerability of the Content Moderation Guardrail in Large Language Models via Intent Manipulation”, is accepted to the Findings of EMNLP 2025.
• 2025-07: Jun attends the Alzheimer’s Association International Conference (AAIC) online.
• 2025-06: One paper, “NQNN: Noise-aware Quantum Neural Networks for Medical Image Classification”, is accepted to MICCAI 2025. Congrats to Maqsudur!
• 2025-06: Keystone achievement!!! Gratefully acknowledge the support from the National Science Foundation (NSF) for the CRII Award on bridging classical and quantum models for robust neural networks via Bayesian inference.
• 2025-01: One survey paper, “Investigating and Mitigating Barren Plateaus in Variational Quantum Circuits: A Survey”, has been accepted for publication in the Quantum Information Processing (QIP) journal.
• 2024-10: Jun attends a QED-C annual meeting in Seattle, WA.
• 2024-10: Jun co-organizes a TRAI workshop and serves as a session chair at CIKM’24 in Boise, ID.
• 2024-09: Jun is awarded a Course Design Certificate by the Center for Teaching and Learning at BSU.
• 2024-08: One survey paper, “Blockchain for large language model security and safety: A holistic survey”, has been accepted for publication in the SIGKDD Explorations journal.
• 2024-08: One paper, “Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning”, is presented in the ACL’24 SDP workshop.
• 2024-05: One paper, “Robust Data-centric Graph Structure Learning for Text Classification”, is presented in the WWW’24 DCAI workshop.
• 2024-02: Jun attends CRA’s Career Mentoring Workshops in DC.
• 2023-08: Jun attends the QSim conference in Telluride, CO.
• 2023-07: Jun joins the computer science department at Boise State University as an assistant professor.
• 2023-06: Jun defends his Ph.D. dissertation.
• 2022-08: Jun is awarded the SIGIR Student Travel Grant for the 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 the CIKM 2022 conference.
• 2022-02: One paper, “How Does Bayesian Noisy Self-Supervision Defend Graph Convolutional Networks?”, is published in the Neural Process Lett Journal.
• 2021-12: One paper, “Deperturbation of Online Social Networks via Bayesian Label Transition”, is accepted for the SIAM SDM 2022 conference.
• 2021-12: One paper, “Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision”, is accepted for the AAAI 2022 conference (Main track, AR 14.96%).
• 2021-06: One paper, “Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks”, is accepted for the ECML-PKDD 2021 conference.
• 2021-02: One paper, “Geometrically Matched Multi-source Microscopic Image Synthesis Using Bidirectional Adversarial Networks”, is accepted for the MICAD 2021 conference.
• 2019-10: One paper, “Into the Reverie: Exploration of the Dream Market”, is accepted for the IEEE BigData 2019 conference.
• 2019-10: One paper, “Lighter U-Net for Segmenting White Matter Hyperintensities in MR Images”, is accepted for publication in the proceedings of MobiQuitous 2019.