Prof. Paul Grubbs — Research Profile
Graduate application deadlines
Computer Science and Engineering (Division of the Department of Electrical Engineering and Computer Science)
PhD in Computer Science and Engineering
Fall 2027Application deadline
Dec 15, 2026
Source: cse.engin.umich.eduChecked Nov 2, 2026MS/MSE in Computer Science and Engineering
Fall 2027Application deadline
Jan 15, 2027
Source: cse.engin.umich.eduChecked Nov 2, 2026
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미시간 대학교 컴퓨터공학과 소속 Paul Grubbs 교수의 등록된 연구 분야는 Applied cryptography, computer security, privacy, systems security, technology policy입니다.
Research keywords
Recent papers
From OpenAlex · papers whose author affiliation matches this university
Labs doing related research
Labs that recently published on topics this lab's recent papers share
Shared topic: Cryptography and Data Security
Marshall Ball Lab
New York University · Department of Computer Science (Courant Institute of Mathematical Sciences)
On the Complexity of Symmetric Boolean Functions in Multiparty Private Simultaneous MessagesPreprintarXiv (Cornell University) · Oct 15Mary Wootters Lab
Stanford University · Computer Science
Improved Local Leakage Resilience of Shamir Secret Sharing and Worst-Case Optimal Polynomial IntersectionPreprintarXiv (Cornell University) · Oct 8James Bartusek Lab
Columbia University · Computer Science
Natural Barriers to Quantum Extraction: On the Post-Quantum (In)security of (O)EKE and Masny-Rindal OTPreprintarXiv (Cornell University) · Sep 30
Other labs in this department
- Alanson Sample Lab
Human-computer interaction, wireless technology, embedded systems
- Alexander Rodríguez Lab
Artificial intelligence, machine learning, multi-agent systems, computational epidemiology, public health
- Amrita Roy Chowdhury Lab
Cryptography, Differential Privacy, Machine Learning
- Ang Chen Lab
Systems, networking, security
- Anhong Guo Lab
Human-computer interaction, artificial intelligence, accessibility, augmented reality, human-AI interaction
- Atul Prakash Lab
Computer systems security; Security and privacy of machine learning models; trustworthy AI; and trustworthy computing infrastructures