
Larry Rudolph
Research Professor · Mobile Computing
Massachusetts Institute of TechnologyAbout
Larry Rudolph is a Research Professor at the Massachusetts Institute of Technology (MIT), affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds the role of Principal Research Scientist and has been a key contributor to projects like the Oxygen Research Group and the Computational Structures Group (CSG). His academic journey includes roles as a Full Professor at the Hebrew University of Jerusalem and postdoctoral research at the University of Toronto. Rudolph specializes in pervasive computing, mobile systems, and virtualization, with notable contributions to Bluetooth technology and location-aware computing. He has advised numerous students and pioneered courses such as MIT's 6.883 Pervasive Human-Centric & Mobile Computing.
Education:
- PhD in Mathematics, Courant Institute, NYU (1977-1981)
- Postdoc in Computer Science, University of Toronto (1981-1982)
- Research Faculty, Carnegie-Mellon University (1982-1986)
- Full Professor, Hebrew University (1986-1997)
Research Interests include mobile device virtualization, privacy in pervasive systems, and adaptive algorithms for personal data management. He co-founded Redigi, a marketplace for digital music resale, and led VMware's Mobile Virtualization Project. His work emphasizes experimental approaches to computer science, integrating hardware and software innovations.
Notable Projects:
- Bluetooth Essentials for Programmers (book, 2007)
- Oxygen Research Group (2000-2005)
- Virtualization and pervasive computing systems
Advising and Collaborations:
- Guided over 15 students to completion of Master’s and PhD theses
- Co-organizer of DOE Supercomputer Valuation and Workshop on Job Scheduling
- Contributions to CSAIL and NECSI (New England Complex Systems Institute)
Labs/Teams: Active in CSAIL's Pervasive Computing initiatives and Oxygen sub-projects. Current focus includes mobile phone security, privacy-aware systems, and adaptive learning frameworks for personal data tagging.
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