Scholar
Wenbo Guo
Google Scholar ID: KyPheRMAAAAJ
UC Santa Barbara
Citations & Impact
Citations
3,263
H-index
29
i10-index
56
Publications
20
Co-authors
51
list available
Publications
Resume (English only)
Academic Achievements
- - Academic Senate Faculty Research Award, UCSB, 2025
- - Google ML and Systems Junior Faculty Award, Google, 2025
- - Berkeley RDI AI & Decentralization Innovation Award, UC Berkeley, 2025
- - Amazon Research Award (with Christopher Kruegel), Amazon, 2024
- - FAR AI Research Award, FAR AI, 2024
- - IBM Fellowship Award, IBM, 2020
- - Facebook Fellowship Finalist, Facebook, 2020
- - Baidu AI Fellowship Finalist, Baidu, 2020
- - CCS Outstanding Paper Award, ACM, 2018
- - Black Hat Student Scholarship, Black Hat, 2018-2020
- - Multiple Conference Travel Grants (e.g., USENIX Security, CCS, NeurIPS, ICML), 2018-2021
- - DARPA AIxCC Top 7, August, 2025
- - SWE-bench-Verfied: PatchPilot ranked in the top five open source tools; PatchPilot+co-PatcheR ranked second in the open weighted model
- - Google SBFT Fuzzing Tool Competition Top 1, April, 2024
- - Geekpwn Competition on Data Tracing CTF Top 10, August, 2018
- - Geekpwn Competition on Advesarial Attacks and Defenses CTF Finalist (Top 6 worldwide), August, 2018
- - Kanxue AI CTF Competition Question Maker, June, 2018
Research Experience
- - Assistant Professor and Zhu Chair at UCSB CS
- - Head of Agent Security at Virtue AI
- - Completed Postdoc at UC Berkeley as part of the RDI center and BAIR Lab, under the mentorship of Prof. Dawn Song
Education
- - Ph.D.: Penn State, Advisor: Prof. Xinyu Xing
- - Master's Degree: Shanghai Jiao Tong University
- - Postdoc: UC Berkeley, RDI center and BAIR Lab, Mentor: Prof. Dawn Song
Background
- - Research Interests: LLM agents and agentic RLs for software engineering and security; LLM reasoning and post-training for coding; AI agents security and safety.
- - Professional Field: Studied the trustworthiness of DRL and DNN via statistical modeling (e.g., Deep GP, Gaussian process models) and learning (e.g., variational inference, MCMC, empirical bayes), and designing DNNs for security applications with a focus on noisy learning and OOD.
Miscellany
- - Actively recruiting Ph.D. students with a solid background in LLMs, DRL, or software analysis skills (static analysis, fuzzing, symbolic execution)
