Scholar
Lauritz Thamsen
Google Scholar ID: yOXcVswAAAAJ
Computer Systems, University of Glasgow
Citations & Impact
Citations
1,135
H-index
19
i10-index
41
Publications
20
Co-authors
70
list available
Contact
Publications
Resume (English only)
Academic Achievements
- Won the Early-Career Teaching Excellence Award of the College of Science and Engineering at the University of Glasgow in May 2025. Published several papers on sustainable scientific workflows, including an introductory book chapter on energy-aware workflows, a workshop paper on a new carbon footprint estimator, and a systematic exploration of the potential of carbon-aware workflow scheduling and scaling.
Research Experience
- Currently a Lecturer in Computer Systems at the University of Glasgow, leading the Carbon-Conscious Computing Lab, a member of the Systems Section, and contributing to the Low-Carbon and Sustainable Computing theme. Previously, a senior researcher in the Distributed and Operating Systems group at TU Berlin, driving research on Adaptive Resource Management with a team of PhD students and lecturing on Cloud Computing and Distributed Systems. Also collaborated closely with the Operating Systems and Middleware group at Hasso Plattner Institute (HPI), mainly on Distributed Systems Engineering. Was a guest professor in the Knowledge Management group and the Department of Computer Science at HU Berlin, lecturing on Data-Intensive Systems, contributing to the research activities of the DFG Collaborative Research Center FONDA, and substituting for Ulf Leser.
Education
- PhD from TU Berlin, working on dynamic resource allocation for distributed dataflows in the BMBF-funded Berlin Big Data Center and the DFG Research Unit Stratosphere under Odej Kao.
Background
- Passionate about making computing more efficient, resilient, and sustainable. Focuses on methods and tools for creating resource-efficient and reliable distributed computer systems, particularly through adaptive resource management and carbon-aware computing. Specializes in data-intensive applications (e.g., data analytics, scientific workflows, AI/ML) running on cloud or edge infrastructure.
Miscellany
- Personal interests and hobbies not mentioned
