About InspiringGroup

The InspiringGroup is a dynamic research group led by Prof. Zhuotao Liu. We build trustworthy AI systems for real-world networked and distributed environments. Our research spans networking, systems, security, and applied cryptography, with a focus on deployable AI, robust learning-based systems, and privacy-preserving collaboration.

Zhuotao Liu

Associate Professor

Institute for Network Sciences and Cyberspace

Tsinghua University

Biography

Dr. Zhuotao Liu is an Associate Professor and Distinguished Researcher at Tsinghua University, where he leads the InspiringGroup. Prior to joining Tsinghua, Dr. Liu served as a Technical Lead at Google’s NetInfra (Network Infrastructure) and Google Global Networking (GGN) teams, where he received two “Feats of Engineering” Awards for his contributions to datacenter networking. He is the Principal Investigator of multiple NSFC projects and other national research programs. Dr. Liu earned his Ph.D. from the University of Illinois at Urbana-Champaign (UIUC) in 2017, supported by the Chiang Chen Overseas Graduate Fellowship. He received his B.S. from Shanghai Jiao Tong University in 2012 and was awarded the National Scholarship for three consecutive years.

Interests

Network Security Blockchain Infrastructures and Web3.0 Initiatives Datacenter Networking and Infrastructures Privacy Preserving Machine Learning Agent Security

Secure Networking and Systems Infrastructure

AI/Agent and Data Security

Privacy-Preserving Machine Learning (with Applied Cryptography)

[CCS 25] [NDSS 24] [CCS 24]

Federated Learning

[Security 24] [CCS 23]

AI Security

[TIFS 24] [WWW 23] [TIFS 22] [CCS 21]

Agent Security

[Preprint A2A] [Preprint Agentic PPML] [Preprint Agents] [Preprint Security]

Intelligent Networking for AI Infra

Datacenter Networking

[NSDI 25] [ATC 23] [ToN 23] [TPDS 21] [TPDS 19] [INFOCOM 18]

Intelligent Network Architecture

[SIGCOMM 25] [NSDI 24] [SIGCOMM 25] [NSDI 24]

Web3.0 and Blockchain

Interoperability

[CCS 19]

Web3 Infrastructure and Applications

[ISSTA 23] [TDSC 22]

Zero-Knowledge Proof

[CCS 23]

PhD/RA Opening: Network Security & Privacy

We are actively recruiting self-motivated PhD students and research assistants. Prior background in systems, security, or machine learning is a plus.

Pinned Apr 2026

We welcome collaboration from academia and industry

If your team is interested in joint projects on secure networking, privacy-preserving AI, or Web3 infrastructure, please feel free to reach out.

News August 2026

[Trust-native AI Infra] Our paper on understanding and realizing the trust-native AI inference infra based on legacy GPUs is accepted by ACM CCS 2026, this work features the most advanced obfuscation-based LLM inference algorithm.

News May 2026

[Meta Learning for Continuous Flow-Data] Our paper on applying meta-learning to address the continual learning in traffic analysis domain is accepted by USENIX Security 2026.

News Oct 2025

[Awards] Our paper on cryptography-based secure graph processing wins the Distinguished Paper Award in ACM CCS 2025.

News Sep 2025

[AI and Cryptography] Our paper on confidential/private inference over Transfomer-based large models via novel cryptography constructions is accepted by IEEE S&P 2026.

News Aug 2025

[Applied AI for Traffic Analysis] Another three papers on RL-driven traffic analysis, website fingerprinting, and website application firewall are accepted by ISOC NDSS 2026.