Chaoxiang He
Assistant Professor
Works on trustworthy AI and the security and privacy of deep learning models, including model protection in cloud settings.
Chaoxiang He's lab at SJTU works on trustworthy & safe ML and interpretability, evaluation & safety of LMs. 9 papers since 2023, 6 at top venues, mostly CVPR and EMNLP. Applications close .
At a glance
- papers, 2023–2026
- 2/ year
- at T1 venues
- 67%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 1 papers2026 · T1 1 · collecting
- 4 papers2025 · T1 3
- 4 papers2024 · T1 2
- 0 papers2023 · T1 0
T1 venuesOtherVenues: CVPR, EMNLP, ICME, ICML, IJCAI
Research focuspaper titles that use each topic's words
- Trustworthy & safe ML0 papers
- Interpretability, evaluation & safety of LMs0 papers
- settings0 papers
- protection0 papers
- learning models0 papers
Paper list
From the lab homepage.
20261 paper
- S&P (T1)SoK: Robustness in Large Language Models against Jailbreak Attacks
20254 papers
- RAIDBadFU: Backdoor Federated Learning Through Adversarial Machine Unlearning
- CVPR (T1)Enhancing Adversarial Transferability with Checkpoints of a Single Model’s Training
- IJCAI (T1)Fine-Grained and Efficient Self-Unlearning with Layered Iteration
- EMNLP (T1)RESF: Regularized-Entropy-Sensitive Fingerprinting for Black-Box Tamper Detection of Large Language Models
20244 papers
- NDSS (T1)DorPatch: Distributed and Occlusion-Robust Adversarial Patch to Evade Certifiable Defenses
- ICML (T1)Intersecting-boundary-sensitive fingerprinting for tampering detection of DNN models
- ICMEMysticMask: Adversarial Mask for Impersonation Attack Against Face Recognition Systems
- MMTowards Stricter Black-box Integrity Verification of Deep Neural Network Models
Where alumni went
Alumni are not listed on the lab homepage.
Apply to SJTUFall 2027
Computer Science PhD (international applicants)
- Round
- Self-funded
- Opens
- —
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
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