Daniel Kang

Dr. Daniel Kang is an Assistant Professor at the University of Illinois at Urbana-Champaign (UIUC) in the Department of Computer Science and Department of Electrical and Computer Engineering (by courtesy). His research focuses on integrating machine learning with data systems and zero-knowledge proofs to enhance privacy and trust in AI deployments. He previously worked as a postdoctoral researcher at UC Berkeley's Sky Lab and earned his PhD at Stanford University under Peter Bailis and Matei Zaharia.

• Developed frameworks like ZKML and ZK-IMG
• Co-creator of DawnBench and MLPerf benchmarks
• Research funded by Google, Open Philanthropy Project, and Emergent Ventures

His research spans machine learning systems, privacy-preserving AI, and security risks in LLMs. Recent work includes optimizing zero-knowledge proofs for ML inference and studying adversarial attacks on AI agents. Articles reveal expertise in benchmark design, video analytics, and ML deployment systems.

Dr. Kang actively recruits students at all levels and focuses on trustless AI verification and efficient query processing. His work addresses critical challenges in ML reproducibility, code generation, and web security.