Faculty
2025: AI 2000 Most Influential Scholar Award Honorable Mention 2025
2025: Best Machine Learning and Security Paper in Cybersecurity Award 2025
2022: Busy Beaver Award für "Privacy of Machine Learning"
2021: Busy Beaver teaching award for seminar “Privacy of Machine Learning” at Saarland University (2021 Winter)
2019: Best paper award at NDSS
Dr. Yang Zhang is tenured faculty member at CISPA. His research concentrates on trustworthy machine learning (privacy, safety, and security). Moreover, he works on measuring and understanding misinformation and unsafe content like hateful memes on the Internet. Over the years, he has published multiple papers at top venues in computer science, including CCS, NDSS, Oakland, and USENIX Security. His work has received the NDSS 2019 distinguished paper award and the CCS 2022 best paper award runner-up.
ACM Conference on Computer and Communications Security (CCS) BadTV: Unveiling Backdoor Threats in Third-Party Task Vectors
Usenix Security Symposium (USENIX-Security) Unveiling the Pitfalls of Data-Free Backdoor Detection Against Pre-Trained Models
European Conference on Computer Vision (ECCV) GEO-Detective: Unveiling Location Privacy Risks in Images with LLM Agents
Annual Meeting of the Association for Computational Linguistics (ACL) Reward Yourself: Efficient Self Rewards for Trustworthy Sampling
Annual Meeting of the Association for Computational Linguistics (ACL) PeerCheck: Enhancing LLM-Generated Academic Reviews Towards Human-Level Quality
Annual Meeting of the Association for Computational Linguistics (ACL) Peering Behind the Shield: Guardrail Identification in Large Language Models
International Conference on Machine Learning (ICML) Position: Preparing for AI Systems That Deceive Developers
Annual Meeting of the Association for Computational Linguistics (ACL) Open Schrödinger’s Closed Box: Identifying Retrieval Augmented Generation in API-Accessible Large Language Model Services
Annual Meeting of the Association for Computational Linguistics (ACL)
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) When Understanding Becomes a Risk: Authenticity and Safety Risks in the Emerging Image Generation Paradigm