Lauritz Thamsen | Postdoc @ TU Berlin

I am a postdoctoral researcher at TU Berlin, where I lead the subgroup on Adaptive Resource Management (ARM) in the research group on Distributed and Operating Systems and teach Cloud Computing. I also started the diselab, an ongoing collaboration between researchers from TU Berlin and Hasso Plattner Institute (HPI) focused on distributed systems engineering. I am furthermore a co-investigator with Odej Kao in the research centers BIFOLD and FONDA, a sub-project lead in WaterGridSense and OPTIMA, a senior researcher in ide3a, as well as an associated researcher with Telemed5000 and HEIBRiDS.

Lauritz Thamsen

I got my PhD from TU Berlin, working on adaptive resource management for distributed dataflows in the Berlin Big Data Center and the research project Stratosphere under Odej Kao. Prior to that, I was part of the Software Architecture Group of Robert Hirschfeld at HPI, the HPI Research School, and the HPI-Stanford Design Thinking Research Program. I also worked at SAP Labs in Palo Alto on interactive programming systems in SAP's Technology Infrastructure Practice under Dan Ingalls and at Signavio in Berlin as a software engineer.

I am into distributed systems, operating systems, and software engineering. I am a fan of systems research and generally passionate about making computing more accessible. My research interests include distributed data processing, resource management and scheduling, cluster and cloud computing, as well as dependable and adaptive systems. Currently, I am looking a lot at efficient processing on diverse cluster infrastructures and dependable processing of IoT sensor streams.

contact

lauritz.thamsen at tu-berlin.de
+49 30 314 24539

office

TEL 1210
TU Berlin

web

twitter: lauritzthamsen
LinkedIn: lauritzthamsen

news

  • October 2020: This winter semester I will lecture again on virtual resources and distributed cloud applications as well as on critical IoT systems.
  • August 2020: I am helping to organize IEEE IC2E 2021 and it promises to be a good conference, so if your work is related to Cloud Computing, consider submitting a paper. Deadline will be sometime in early 2021!
  • July 2020: We are part of FONDA, a new Research Center funded by the DFG. In the project, we will look at how adaptive resource management can make life easier for scientists, who have to work with their available cluster infrastructures to analyze large scientific datasets.
  • June 2020: To strengthen the ongoing collaboration between the DOS group at TU Berlin and the OSM group at HPI, we founded the Distributed Systems Engineering Lab (diselab). In the lab, we come together to investigate new methods, systems, and tools for dependable distributed systems, specifically looking at IoT systems in context of critical urban infrastructures.
  • June 2020: It took two years after my own PhD defense and over a dozen project proposals, but now I have assembled a sizable working group around me at TU Berlin: I am now leading the working group on Adaptive Resource Management (ARM) in Distributed and Operating Systems at TU Berlin.
  • March 2020: There will be two new and exciting research projects with PhD positions in my team this summer. In BIFOLD, we will continue to do systems research at the intersection of distributed systems, data management, and machine learning. In ide3a, we will set up new teaching around critical urban infrastructures, implementing elements of MOOCs and new simulation tools, in a network of European universities.

availabilities

  • Collaboration: If you are interested in working with me on adaptive resource management for critical and data-intensive distributed systems, let me know.
  • Community service: I am available for community service in the areas of resource management and scheduling, distributed data processing, cluster and cloud computing, as well as adaptive and dependable systems. Please find an up-to-date list of my previous services below.
  • Theses: We are always looking to work with motivated students. Have a look at the proposed topics, at our previous research results, and get in touch.

publications

2020:
  • Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs. Morgan K. Geldenhuys, Lauritz Thamsen, and Odej Kao. To appear in the Proceedings of the 2020 IEEE International Conference on Big Data (Big Data). IEEE. 2020. [pdf]
    • To be published by IEEE. © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring. Felix Lorenz, Morgan Geldenhuys, Harald Sommer, Frauke Jakobs, Carsten Lüring, Volker Skwarek, Ilja Behnke, and Lauritz Thamsen. To appear in the Proceedings of the 2020 IEEE International Conference on Big Data (Big Data). To be presented at the Second International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2020. [pdf]
    • To be published by IEEE. © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs. Jonathan Will, Jonathan Bader, and Lauritz Thamsen. To appear in the Proceedings of the 2020 IEEE International Conference on Big Data (Big Data). To be presented at the 4th International Workshop on Benchmarking, Performance Tuning and Optimization for Big Data Applications (BPOD). IEEE. 2020. [pdf] [video] [data]
    • To be published by IEEE. © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet?. Ilja Behnke, Lukas Pirl, Lauritz Thamsen, Robert Danicki, Andreas Polze, and Odej Kao. To appear in the Proceedings of the 39th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2020. [pdf] [video]
    • To be published by IEEE. © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Mary, Hugo, and Hugo*: Learning to Schedule Distributed Data-Parallel Processing Jobs on Shared Clusters. Lauritz Thamsen, Jossekin Beilharz, Vinh Thuy Tran, Sasho Nedelkoski, and Odej Kao. In Concurrency and Computation: Practice and Experience (e5823). Wiley. 2020. [pdf] [code]
  • Fingerprinting Analog IoT Sensors for Secret-Free Authentication. Felix Lorenz, Lauritz Thamsen, Andreas Wilke, Ilja Behnke, Jens Waldmüller-Littke, Ilya Komarov, Odej Kao, and Manfred Paeschke. To appear in the Workshop Proceedings of the 29th International Conference on Computer Communications and Networks (ICCCN). Presented at the 10th International Workshop on Security, Privacy, Trust, and Machine Learning for Internet of Things (IoTSPT-ML). IEEE. 2020. [preprint]
    • To be published by IEEE. © IEEE 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
2019:
  • Effectively Testing System Configurations of Critical IoT Analytics Pipelines. Morgan K. Geldenhuys, Lauritz Thamsen, Kain Kordian Gontarska, Felix Lorenz, and Odej Kao. In the Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData). Presented at the Second International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2019. [pdf]
    • https://doi.org/BigData47090.2019.9005504, © IEEE 2019. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Héctor: A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds. Ilja Behnke, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 12th IEEE/ACM International Conference on Utility and Cloud Computing (UCC). Presented at the 8th International Workshop on Cloud and Edge Computing and Applications Management (CloudAM). ACM. 2019. [pdf] [code]
    • © ACM, 2019. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in the UCC Companion.
  • Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs. Lauritz Thamsen, Ilya Verbitskiy, Sasho Nedelkoski, Vinh Thuy Tran, Vinícius Meyer, Miguel G. Xavier, Odej Kao, and César A. F. De Rose. In the Proceedings of the Euro-Par 2019 Workshops (Euro-Par). Presented at the 1st International Workshop on Parallel Programming Models in High-Performance Cloud. Springer. 2019. [pdf] [code]
  • Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures. Sasho Nedelkoski, Lauritz Thamsen, Ilya Verbitskiy, and Odej Kao. In the Proceedings of the 2019 IEEE International Conference on Edge Computing (EDGE). IEEE. 2019. [pdf]
    • https://doi.org/10.1109/EDGE.2019.00015, © IEEE 2019. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
2018:
  • CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters. Ilya Verbitskiy, Lauritz Thamsen, Thomas Renner, and Odej Kao. In the Proceedings of the 10th IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE. 2018. [pdf]
    • https://doi.org/10.1109/CloudCom2018.2018.00029, © IEEE 2018. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources. Gerrit Janßen, Ilya Verbitskiy, Thomas Renner, and Lauritz Thamsen. In the Proceedings of the 2018 IEEE International Conference on Big Data (IEEE BigData). Presented at the First International Workshop on the Internet of Things Data Analytics (IoTDA). IEEE. 2018. [pdf]
    • https://doi.org/10.1109/BigData.2018.8622651, © IEEE 2018. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Learning Efficient Co-locations for Scheduling Distributed Dataflows in Shared Clusters. Lauritz Thamsen, Ilya Verbitskiy, Benjamin Rabier, and Odej Kao. In Services Transactions on Big Data (Vol. 4, No. 1). Services Society. 2018. [pdf] [code]
  • Adaptive Resource Management for Distributed Data Analytics. Lauritz Thamsen, Thomas Renner, Ilya Verbitskiy, and Odej Kao. In Lucio Grandinetti, Seyedeh Leili Mirtaheri, Reza Shahbazian, Thomas Sterling, Vladimir Voevodin (eds.), Advances in Parallel Computing – Big Data and HPC: Ecosystem and Convergence. IOS Press. 2018. [pdf]
Previously:
  • Ellis: Dynamically Scaling Distributed Dataflows to Meet Runtime Targets. Lauritz Thamsen, Ilya Verbitskiy, Jossekin Beilharz, Thomas Renner, Andreas Polze, and Odej Kao. In the Proceedings of the 9th IEEE International Conference on Cloud Computing Technology and Science (CloudCom). IEEE. 2017. Best Paper Candidate. [pdf] [code]
    • https://doi.org/10.1109/CloudCom.2017.37, © IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows. Jannis Koch, Lauritz Thamsen, Florian Schmidt, and Odej Kao. In the Proceedings of the 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). IEEE. 2017. [pdf] [code]
    • https://doi.org/10.1109/PDCAT.2017.00034, © IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference. Lauritz Thamsen, Benjamin Rabier, Florian Schmidt, Thomas Renner, and Odej Kao. In the Proceedings of the 6th IEEE BigData Congress. IEEE. 2017. [pdf] [code]
    • https://doi.org/10.1109/BigDataCongress.2017.28, © IEEE 2017. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Adaptive Resource Management for Distributed Data Analytics Based on Container-level Cluster Monitoring. Thomas Renner, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA). SCITEPRESS. 2017. [pdf] [code]
    • © SCITEPRESS, 2017. This contribution was presented at DATA 17. This is the authors' version of the work.
  • Addressing Hadoop’s Small File Problem With an Appendable Archive File Format. Thomas Renner, Johannes Müller, Lauritz Thamsen, and Odej Kao. In the Proceedings of the Big Data Analytics Workshop (BigDAW), co-located with the ACM International Conference on Computing Frontiers. ACM. 2017. [pdf] [code]
    • © ACM, 2017. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in the proceedings of the Big Data Analytics Workshop (BigDAW17).
  • Selecting Resources for Distributed Dataflow Systems According to Runtime Targets. Lauritz Thamsen, Ilya Verbitskiy, Florian Schmidt, Thomas Renner, and Odej Kao. In the Proceedings of the 35th IEEE International Performance Computing and Communications Conference (IPCCC). IEEE. 2016. [pdf] [code] [data]
    • https://doi.org/10.1109/PCCC.2016.7820629, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • CoLoc: Distributed Data and Container Colocation for Data-Intensive Applications. Thomas Renner, Lauritz Thamsen, and Odej Kao. In the Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData). Presented at the 4th International Workshop on Distributed Storage Systems and Coding for Big Data. IEEE. 2016. [pdf]
    • https://doi.org/10.1109/BigData.2016.7840954, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Visually Programming Dataflows for Distributed Data Analytics. Lauritz Thamsen, Thomas Renner, Marvin Byfeld, Markus Paeschke, Daniel Schröder, and Felix Böhm. In the Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData). Presented at the 3rd Workshop on Advances in Software and Hardware for Big Data to Knowledge Discovery (ASH). IEEE. 2016. [pdf] [code]
    • https://doi.org/10.1109/BigData.2016.7840860, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • When to Use a Distributed Dataflow Engine: Evaluating the Performance of Apache Flink. Ilya Verbitskiy, Lauritz Thamsen, and Odej Kao. In the Proceedings of the IEEE International Conference on Cloud and Big Data Computing (CBDCom). IEEE. 2016. [pdf]
    • https://doi.org/10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0114, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Continuously Improving the Resource Utilization of Iterative Parallel Dataflows. Lauritz Thamsen, Thomas Renner, and Odej Kao. In the Proceedings of the IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW). Presented at the International Workshop on Big Data and Cloud Performance (DCPerf). IEEE. 2016. [pdf] [code]
    • http://dx.doi.org/10.1109/ICDCSW.2016.20, © IEEE 2016. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Aura: A Flexible Dataflow Engine for Scalable Data Processing. Tobias Herb, Lauritz Thamsen, Thomas Renner, and Odej Kao. In Andreas Knüpfer, Tobias Hilbrich, Christoph Niethammer, José Gracia, Wolfgang E. Nagel, Michael M. Resch (eds.), Tools for High Performance Computing 2015. Springer. 2016. [pdf] [code]
  • Exploratory Authoring of Interactive Content in a Live Environment. Philipp Otto, Jaqueline Pollak, Daniel Werner, Felix Wolff, Bastian Steinert, Lauritz Thamsen, Marcel Taeumel, Jens Lincke, Robert Krahn, Daniel H. H. Ingalls, and Robert Hirschfeld. HPI Technical Reports, vol. 101. Hasso Plattner Institute. 2016. [pdf]
  • Lively Groups: Shared Behavior in a World of Objects without Classes or Prototypes. Tim Felgentreff, Jens Lincke, Robert Hirschfeld, and Lauritz Thamsen. In the Proceedings of the Future Programming Workshop (FPW) 2015, co-located with the Conference on Object-oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM. 2015. [pdf]
    • © ACM, 2015. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in the proceedings of the Future Programming Workshop (FPW) 2015.
  • Network-Aware Resource Management for Scalable Data Analytics Frameworks. Thomas Renner, Lauritz Thamsen, and Odej Kao. In the Proceedings of the First Workshop on Data-Centric Infrastructure for Big Data Science (DIBS) 2015, co-located with the 2015 IEEE International Conference on BigData (BigData). IEEE. 2015. [pdf] [code]
    • http://dx.doi.org/10.1109/BigData.2015.7364083, © IEEE 2015. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
  • Preserving Access to Previous System States in the Lively Kernel. Lauritz Thamsen, Bastian Steinert, and Robert Hirschfeld. In Hasso Plattner, Christoph Meinel, and Larry Leifer (eds.), Design Thinking Research: Making Design Thinking Foundational. Springer. 2015. [pdf] [code]
  • Implicit Parallelism through Deep Language Embedding. Alexander Alexandrov, Andreas Kunft, Asterios Katsifodimos, Felix Schüler, Lauritz Thamsen, Odej Kao, Tobias Herb, and Volker Markl. In the Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD). ACM. 2015. [pdf] [project]
    • © ACM, 2015. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in the proceedings of the ACM SIGMOD international conference.
  • Object Versioning to Support Recovery Needs: Using Proxies to Preserve Previous Development States in Lively. Bastian Steinert, Lauritz Thamsen, Tim Felgentreff, and Robert Hirschfeld. In the Proceedings of the Dynamic Languages Symposium (DLS) 2014, co-located with the Conference on Object-oriented Programming, Systems, Languages, and Applications (OOPSLA). ACM. 2014. [pdf] [code]
    • © ACM, 2014. This is the authors' version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in the proceedings of the Dynamic Languages Symposium.
  • Orca: A Single-language Web Framework for Collaborative Development. Lauritz Thamsen, Anton Gulenko, Michael Perscheid, Robert Krahn, Robert Hirschfeld, and David A. Thomas. In the Proceedings of the Conference on Creating, Connecting and Collaborating through Computing (C5) 2012. IEEE. 2012. [pdf] [project]
    • http://dx.doi.org/10.1109/C5.2012.9, © IEEE 2012. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

teaching

  • Lectures:
    • Winter 2020: Cloud Computing – Virtual Resources and Distributed Applications
    • Also winter 2020: Smart Sensing / Critical IoT Systems (winter school lectures)
    • Winter 2019: The Methods of Cloud Computing
    • Summer 2019: Distributed Systems
    • Winter 2018: The Methods of Cloud Computing
  • Seminars:
    • Summer 2020: Hot Topics in Distributed and Operating Systems
    • Winter 2017: Hot Topics in Systems Operation
    • Summer 2015: Operating Complex IT-Systems
    • Winter 2012: Advanced Modularity
  • Supervised projects:
    • Summer 2020: Real-Time Applications on IoT Devices
    • Summer 2019: Resource Management for Distributed IoT Stream Processing
    • Winter 2018: Machine Learning for Sustainability
    • Also winter 2018: Implementing IoT Data Pipelines
    • Winter 2016: Interactive Cluster Performance Visualization
    • Winter 2015: Visually Programming Distributed Dataflows
    • Winter 2014: Software-Defined Networking for Big Data
    • Winter 2013: Scripting Interactive Web Visualizations
  • Teaching assistance:
    • Summer 2018: Systems Programming / Operating Systems
    • Summer 2017: Systems Programming / Operating Systems
    • Summer 2013: Software Engineering
    • Summer 2012: Software Engineering
    • Summer 2011: Software Engineering
    • Winter 2010: Software Architecture
  • Thesis supervision: Well over a dozen master theses successfully (co-)supervised.

community service

  • Organization committee: IC2E 2021
  • Program committee: IC2E 2021, WWW 2020, Euro-Par 2020, IoTDA workshop 2020, COINS 2019, IoTDA workshop 2019, SDNCC workshop 2016
  • External reviewer: IEEE Transactions on Parallel and Distributed Systems, IEEE Transactions on Services Computing, Elsevier's Future Generation Computer Systems, IEEE Access, Springer's Cluster Computing, Springer's Journal of Grid Computing, Wiley's Concurrency and Computation - Practice and Experience, STU Spektrum's Computing and Informatics, ICPE 2020, Euro-Par 2020 workshops, Euro-Par 2019, Euro-Par 2018, IEEE eScience 2018, IEEE ICCAC 2017
  • Student volunteer: AOSD 2012, ESUG 2012

theses

  • Dynamic Resource Allocation for Distributed Dataflows. Lauritz Thamsen. PhD thesis submitted at TU Berlin in March 2018 and successfully defended on May 4, 2018. [pdf] [slides]
  • Object Versioning for the Lively Kernel: Preserving Access to Previous System States in an Object-oriented Programming System. Lauritz Thamsen. Master thesis submitted at Hasso-Plattner-Institut, University of Potsdam, in May 2014. [pdf] [slides] [code]
  • Object Collaboration in the Orca Web Framework. Lauritz Thamsen. Bachelor thesis submitted at Hasso-Plattner-Institut, University of Potsdam, in June 2011. [pdf] [project]

© Lauritz Thamsen   |   Last Update: 16 Jan 2021