Dr Lauritz Thamsen

  • Lecturer in Computer Systems (School of Computing Science)

telephone: 0141 330 2000 (x0652)
email: Lauritz.Thamsen@glasgow.ac.uk
pronouns: He/him/his

S 152, 15 Lilybank Gardens, via 18 Lilybank Gardens (Sir Alwyn Williams Building), University of Glasgow, Glasgow, G12 8RZ

Import to contacts

Biography

I am a Lecturer in Computer Systems in the School of Computing Science at the University of Glasgow, where my research focuses on resource-efficient distributed computer systems, and where I teach Operating Systems and Systems Programming basics.

Within the School, I am a member of the Glasgow Systems Section (GLASS), I am part of the Glasgow Parallelism Group (GPG), and I lead the Glasgow Carbon-Conscious Computing (GC3) lab. In addition, I am active in the School's Low-Carbon and Sustainable Computing research theme.
Beyond the School, I co-lead the collaboration on Adaptive Resource Management (ARM) with TU Berlin and started the Distributed Systems Engineering Lab (diselab), a collaboration with researchers from TU Berlin and Hasso Plattner Institute (HPI).

Before joining the University of Glasgow, I was a senior researcher in the Distributed and Operating Systems group at TU Berlin, lecturing on Cloud Computing and on Distributed Systems, and a guest professor in the Department of Computer Science at HU Berlin, substituting for Prof. Ulf Leser and lecturing on Data-Intensive Systems. At TU Berlin, I also completed a postdoc and did my PhD under Prof. Odej Kao, working on dynamic resource allocation for distributed dataflow systems. Prior to that, I was part of the Software Architecture Group of Prof. Robert Hirschfeld and the Research School at HPI, and I worked at SAP Labs in Palo Alto, California, in the Technology Infrastructure Practice group, under Dan Ingalls, as well as at Signavio in Berlin.

More about me and my research can be found at https://lauritzthamsen.org/.

Research interests

I am passionate about making computing more efficient, resilient, and sustainable. To this end, I work on methods and tools that make it easier to create resource-efficient and reliable distributed computer systems, focusing especially on adaptive resource management and carbon-aware computing for data-intensive applications running on edge/cloud infrastructure.

My research interests include:

  • Distributed Computer Systems
  • Edge and Cloud Computing
  • Carbon-Aware Computing
  • Adaptive Resource Management
  • Data-Intensive Systems
  • Software Development and Operation Tools

Publications

Selected publications

Bader, J., Lehmann, F., Thamsen, L., Leser, U. and Kao, O. (2024) Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures. Future Generation Computer Systems, 150, pp. 171-185. (doi: 10.1016/j.future.2023.08.022)

Geldenhuys, M. K., Scheinert, D., Kao, O. and Thamsen, L. (2022) Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads. In: IEEE International Conference on Web Services (ICWS 2022), Barcelona, Spain, 11-15 July 2022, pp. 198-207. ISBN 9781665481434 (doi: 10.1109/ICWS55610.2022.00041)

Bermbach, D., Bader, J., Hasenburg, J., Pfandzelter, T. and Thamsen, L. (2022) AuctionWhisk: using an auction-inspired approach for function placement in serverless fog platforms. Software: Practice and Experience, 52(5), pp. 1143-1169. (doi: 10.1002/spe.3058)

Wiesner, P., Behnke, I., Scheinert, D., Gontarska, K. and Thamsen, L. (2021) Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud. In: 22nd International Middleware Conference (Middleware '21), 06-10 Dec 2021, pp. 260-272. ISBN 9781450385343 (doi: 10.1145/3464298.3493399)

Wiesner, P. and Thamsen, L. (2021) LEAF: Simulating Large Energy-Aware Fog Computing Environments. In: 2021 5th IEEE International Conference on Fog and Edge Computing (ICFEC), Melbourne, Australia, 10-13 May 2021, pp. 29-36. ISBN 9781665402910 (doi: 10.1109/ICFEC51620.2021.00012)

All publications

List by: Type | Date

Jump to: 2024 | 2023 | 2022 | 2021 | 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2012
Number of items: 85.

2024

Chen, S. et al. (2024) Coupled simulation of urban water networks and interconnected critical urban infrastructure systems: A systematic review and multi-sector research agenda. Sustainable Cities and Society, 104, 105283. (doi: 10.1016/j.scs.2024.105283)

Will, J., Scheinert, D., Zunzer, S., Bode, J., Kring, C. and Thamsen, L. (2024) Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling. In: ICPE 2024: The 9th Workshop on Challenges in Performance Methods for Software Development (WOSP-C), London, UK, 7-11 May 2024, (Accepted for Publication)

Geldenhuys, M. K., Scheinert, D., Kao, O. and Thamsen, L. (2024) Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization. In: 15th ACM/SPEC International Conference on Performance Engineering (ICPE 2024), London, UK, 7-11 May 2024, (Accepted for Publication)

Bader, J., Diedrich, N., Thamsen, L. and Kao, O. (2024) Predicting Dynamic Memory Requirements for Scientific Workflow Tasks. In: 2023 IEEE International Conference on Big Data, Sorrento, Italy, 15-18 Dec 2023, ISBN 9798350324457 (doi: 10.1109/BigData59044.2023.10386837)

Scheinert, D., Becker, S., Will, J., Englaender, L. and Thamsen, L. (2024) Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications. In: 2023 IEEE International Conference on Big Data (Big Data), Sorrento, Italy, 15-18 Dec 2023, pp. 2339-2345. ISBN 9798350324457 (doi: 10.1109/BigData59044.2023.10386195)

Bader, J., Lehmann, F., Thamsen, L., Leser, U. and Kao, O. (2024) Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures. Future Generation Computer Systems, 150, pp. 171-185. (doi: 10.1016/j.future.2023.08.022)

2023

Wiesner, P., Khalili, R., Grinwald, D., Agrawal, P., Thamsen, L. and Kao, O. (2023) FedZero: Leveraging Renewable Excess Energy in Federated Learning. In: 15th ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy 2024), Singapore, 4-7 June 2024, (Accepted for Publication)

Lehmann, F., Bader, J., Thamsen, L. and Leser, U. (2023) The Common Workflow Scheduler Interface: Status Quo and Future Plans. Workshop on Workflows in Support of Large-Scale Science (WORKS23),The International Conference on High Performance Computing, Network, Storage, and Analysis, New York, USA, 12-17 Nov 2023. ISBN 9798400707858 (doi: 10.1145/3624062.3626283)

Will, J., Thamsen, L., Scheinert, D. and Kao, O. (2023) Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing? In: 35th International Conference on Scientific and Statistical Database Management (SSDBM2023), Los Angeles, California, USA, 10-12 July 2023, ISBN 9798400707469 (doi: 10.1145/3603719.3603733)

Scheinert, D., Wiesner, P., Wittkopp, T., Thamsen, L., Will, J. and Kao, O. (2023) Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics. In: 42nd IEEE International Performance Computing and Communications Conference (IPCCC 2023), Anaheim, CA, USA, 17-19 Nov 2023, (Accepted for Publication)

Lehmann, F., Bader, J., Tschirpke, F., Thamsen, L. and Leser, U. (2023) How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface. In: 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023), Bangalore, India, 1-4 May 2023, pp. 166-179. ISBN 9798350301199 (doi: 10.1109/CCGrid57682.2023.00025)

Trihinas, D. and Thamsen, L. (2023) Towards Energy-Aware Machine Learning in Geo-Distributed IoT Settings. In: Euro-Par 2023, Limassol, Cyprus, 28 Aug - 01 Sep 2023, (Accepted for Publication)

Behnke, I., Blumschein, C., Danicki, R., Wiesner, P., Thamsen, L. and Kao, O. (2023) Towards a real-time IoT: approaches for incoming packet processing in cyber-physical systems. Journal of Systems Architecture, 140, 102891. (doi: 10.1016/j.sysarc.2023.102891)

Scheinert, D., Becker, S., Bader, J., Thamsen, L., Will, J. and Kao, O. (2023) Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics. In: 2022 IEEE International Conference on Big Data, Osaka, Japan, 17-20 December 2022, pp. 209-216. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10020860)

Scheinert, D., Zadeh Aghdam, B. S., Becker, S., Kao, O. and Thamsen, L. (2023) Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments. In: 2022 IEEE International Conference on Big Data (IEEE BigData 2022), Osaka, Japan, 17-20 Dec 2022, pp. 4583-4588. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10021129)

Will, J., Thamsen, L., Bader, J., Scheinert, D. and Kao, O. (2023) Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing. In: 2022 IEEE International Conference on Big Data, Osaka, Japan, 17-20 December 2022, pp. 161-169. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10020295)

2022

Trihinas, D., Thamsen, L., Beilharz, J. and Symeonides, M. (2022) Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services. In: 10th IEEE International Conference on Cloud Engineering (IC2E), California, USA, 26-30 Sept 2022, pp. 29-35. ISBN 9781665491150 (doi: 10.1109/IC2E55432.2022.00011)

Will, J., Thamsen, L., Bader, J., Scheinert, D. and Kao, O. (2022) Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing. In: 10th IEEE International Conference on Cloud Engineering (IC2E), California, USA, 26-30 Sept 2022, ISBN 9781665491150 (doi: 10.1109/IC2E55432.2022.00014)

Zhu, H., Scheinert, D., Thamsen, L., Gontarska, K. and Kao, O. (2022) Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning. In: 10th IEEE International Conference on Cloud Engineering (IC2E), California, USA, 26-30 Sept 2022, pp. 150-159. ISBN 9781665491150 (doi: 10.1109/IC2E55432.2022.00023)

Bader, J., Lehmann, F., Groth, A., Thamsen, L., Scheinert, D., Will, J., Leser, U. and Kao, O. (2022) Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures. In: 41st IEEE International Performance, Computing, and Communications Conference (IPCCC 2022), Austin, TX, USA, 11-13 November 2022, pp. 269-274. ISBN 9781665480185 (doi: 10.1109/IPCCC55026.2022.9894299)

Geldenhuys, M. K., Pfister, B. J. J., Scheinert, D., Thamsen, L. and Kao, O. (2022) Khaos: Dynamically Optimizing Checkpointing for Dependable Distributed Stream Processing. In: 17th Conference On Computer Science And Intelligence Systems (FedCSIS 2022), Sofia, Bulgaria, 4-7 September 2022, pp. 553-561. ISBN 9788396242396 (doi: 10.15439/2022F225)

Geldenhuys, M. K., Scheinert, D., Kao, O. and Thamsen, L. (2022) Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads. In: IEEE International Conference on Web Services (ICWS 2022), Barcelona, Spain, 11-15 July 2022, pp. 198-207. ISBN 9781665481434 (doi: 10.1109/ICWS55610.2022.00041)

Bader, J., Lehmann, F., Thamsen, L., Will, J., Leser, U. and Kao, O. (2022) Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters. In: 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022), Copenhagen, Denmark, 6-8 July 2022, ISBN 9781450396677 (doi: 10.1145/3538712.3538739)

Wiesner, P., Scheinert, D., Wittkopp, T., Thamsen, L. and Kao, O. (2022) Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads. In: 28th International European Conference on Parallel and Distributed Computing (EURO-PAR 2022), Glasgow, Scotland, United Kingdom, 22-26 August 2022, pp. 218-232. ISBN 9783031125966 (doi: 10.1007/978-3-031-12597-3_14)

Thamsen, L., Scheinert, D., Will, J., Bader, J. and Kao, O. (2022) Collaborative cluster configuration for distributed data-parallel processing: a research overview. Datenbank-Spektrum, 22(2), pp. 143-151. (doi: 10.1007/s13222-022-00416-z)

Pfister, B. J. J., Lickefett, W. S., Nitschke, J., Paul, S., Geldenhuys, M. K., Scheinert, D., Gontarska, K. and Thamsen, L. (2022) Rafiki: Task-level Capacity Planning in Distributed Stream Processing Systems. In: 27th International European Conference on Parallel and Distributed Computing (Euro-Par 2021), 30 August – 3 September 2021, pp. 352-363. ISBN 9783031061554 (doi: 10.1007/978-3-031-06156-1_28)

Bermbach, D., Bader, J., Hasenburg, J., Pfandzelter, T. and Thamsen, L. (2022) AuctionWhisk: using an auction-inspired approach for function placement in serverless fog platforms. Software: Practice and Experience, 52(5), pp. 1143-1169. (doi: 10.1002/spe.3058)

Habenicht, D., Kreutz, K., Becker, S., Bader, J., Thamsen, L. and Kao, O. (2022) SyncMesh: Improving Data Locality for Function-as-a-Service in Meshed Edge Networks. In: 5th International Workshop on Edge Systems, Analytics and Networking (EdgeSys' 22), Rennes, France, 05 Apr 2022, pp. 55-60. ISBN 9781450392532 (doi: 10.1145/3517206.3526275)

Blumschein, C., Behnke, I., Thamsen, L. and Kao, O. (2022) Differentiating Network Flows for Priority-Aware Scheduling of Incoming Packets in Real-Time IoT Systems. In: 2022 IEEE 25th International Symposium on Real-Time Distributed Computing (ISORC), Västerås, Sweden, 17-18 May 2022, ISBN 9781665406277 (doi: 10.1109/ISORC52572.2022.9812841)

Behnke, I., Wiesner, P., Danicki, R. and Thamsen, L. (2022) A Priority-Aware Multiqueue NIC Design for Real-Time IoT Devices. In: 37th ACM/SIGAPP Symposium on Applied Computing (SAC), Virtual Event, 25 - 29 April 2022, pp. 534-538. ISBN 9781450387132 (doi: 10.1145/3477314.3507165)

Thamsen, L., Beilharz, J., Polze, A. and Kao, O. (2022) The Methods of Cloud Computing. Technical Report. Technische Universität Berlin DepositOnce. (doi: 10.14279/depositonce-15190).

2021

Wiesner, P., Behnke, I., Scheinert, D., Gontarska, K. and Thamsen, L. (2021) Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud. In: 22nd International Middleware Conference (Middleware '21), 06-10 Dec 2021, pp. 260-272. ISBN 9781450385343 (doi: 10.1145/3464298.3493399)

Bader, J., Thamsen, L., Kulagina, S., Will, J., Meyerhenke, H. and Kao, O. (2021) Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters. In: 2021 IEEE International Conference on Big Data (Big Data), 15-18 Dec 2021, pp. 65-75. ISBN 9781665439022 (doi: 10.1109/BigData52589.2021.9671519)

Scheinert, D., Alamgiralem, A., Bader, J., Will, J., Wittkopp, T. and Thamsen, L. (2021) On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds. In: 2021 IEEE International Conference on Big Data (Big Data), 15-18 Dec 2021, pp. 3113-3118. ISBN 9781665439022 (doi: 10.1109/BigData52589.2021.9671275)

Will, J., Arslan, O., Bader, J., Scheinert, D. and Thamsen, L. (2021) Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud. In: 2021 IEEE International Conference on Big Data (Big Data), 15-18 Dec 2021, pp. 3141-3146. ISBN 9781665439022 (doi: 10.1109/BigData52589.2021.9671742)

Geldenhuys, M. K., Will, J., Pfister, B. J.J., Haug, M., Scharmann, A. and Thamsen, L. (2021) Dependable IoT Data Stream Processing for Monitoring and Control of Urban Infrastructures. In: 1st International Workshop on Testing Distributed Internet of Things Systems (TDIS) at 9th IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 244-250. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00041)

Gontarska, K., Geldenhuys, M., Scheinert, D., Wiesner, P., Polze, A. and Thamsen, L. (2021) Evaluation of Load Prediction Techniques for Distributed Stream Processing. In: 9th International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 91-98. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00023)

Haug, M., Lorenz, F. and Thamsen, L. (2021) GRAL: Localization of Floating Wireless Sensors in Pipe Networks. In: 1st International Workshop on Testing Distributed Internet of Things Systems (TDIS) at 9th IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 251-257. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00042)

Becker, S., Schmidt, F., Thamsen, L., Ferrer, A. J. and Kao, O. (2021) LOS: Local-Optimistic Scheduling of Periodic Model Training For Anomaly Detection on Sensor Data Streams in Meshed Edge Networks. In: 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), 27 Sep - 01 Oct 2021, pp. 41-50. ISBN 9781665412612 (doi: 10.1109/ACSOS52086.2021.00033)

Bermbach, B., Chandra, A., Krintz, C., Gokhale, A., Slominski, A., Thamsen, L., Cavalcante, E., Guo, T., Brandic, I. and Wolski, R. (2021) On the Future of Cloud Engineering. In: 2021 IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 264-275. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00044)

Scheinert, D., Zhu, H., Thamsen, L., Geldenhuys, M. K., Will, J., Acker, A. and Kao, O. (2021) Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs Using Graph Propagation. In: 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), 29-31 Oct 2021, ISBN 9781665443319 (doi: 10.1109/IPCCC51483.2021.9679361)

Will, J., Thamsen, L., Scheinert, D., Bader, J. and Kao, O. (2021) C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds. In: 2021 IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 43-52. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00018)

Thamsen, L., Beilharz, J., Tran, V. T., Nedelkoski, S. and Kao, O. (2021) Mary, Hugo, and Hugo*: learning to schedule distributed data-parallel processing jobs on shared clusters. Concurrency and Computation: Practice and Experience, 33(18), e5823. (doi: 10.1002/cpe.5823)

Scheinert, D., Thamsen, L., Zhu, H., Will, J., Acker, A., Wittkopp, T. and Kao, O. (2021) Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts. In: 2021 IEEE International Conference on Cluster Computing (CLUSTER), 07-10 Sep 2021, pp. 261-270. ISBN 9781728196664 (doi: 10.1109/Cluster48925.2021.00052)

Tilcher, D. K., Popescu, F., Sommer, H., Thamsen, L. and Thamsen, P. U. (2021) Control Optimization Through Prediction-Based Wastewater Management. In: ASME 2021 Fluids Engineering Division Summer Meeting, 10-12 Aug 2021, ISBN 9780791885291 (doi: 10.1115/FEDSM2021-65375)

Gontarska, K., Wrazen, W., Beilharz, J., Schmid, R., Thamsen, L. and Polze, A. (2021) Predicting Medical Interventions From Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring. In: 19th Conference on Artificial Intelligence in Medicine (AIME'21), 15-18 Jun 2021, pp. 293-297. ISBN 9783030772109 (doi: 10.1007/978-3-030-77211-6_33)

Danicki, R., Haug, M., Behnke, I., Mädje, L. and Thamsen, L. (2021) Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems. In: 4th International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2021), Online, United Kingdom, 26 April 2021, pp. 25-30. ISBN 9781450382915 (doi: 10.1145/3434770.3459733)

Beilharz, J., Wiesner, P., Boockmeyer, A., Brokhausen, F., Behnke, I., Schmid, R., Pirl, L. and Thamsen, L. (2021) Towards a Staging Environment for the Internet of Things. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), 22-26 Mar 2021, pp. 312-315. ISBN 9781665404242 (doi: 10.1109/PerComWorkshops51409.2021.9431087)

Beilharz, J., Wiesner, P., Boockmeyer, A., Friedenberger, D., Brokhausen, F., Pirl, L., Behnke, I., Polze, A. and Thamsen, L. (2021) Continuously Testing Distributed IoT Systems: An Overview of the State of the Art. 19th International Conference on Service-Oriented Computing (ICSOC) Workshops, Virtual Event, 22–25 November 2021.

Bender, F., Brune, J. J., Keutel, N. L., Behnke, I. and Thamsen, L. (2021) PIERES: A Playground for Network Interrupt Experiments on Real-Time Embedded Systems in the IoT. In: 9th International Workshop on Load Testing and Benchmarking of Software Systems (LTB) at the 12th ACM/SPEC International Conference on Performance Engineering (ICPE), 19 - 23 April 2021, pp. 81-84. ISBN 9781450383318 (doi: 10.1145/3447545.3451189)

Chen, S., Brokhausen, F., Wiesner, P., Thamsen, L. and Cominola, A. (2021) Assessing the Resilience of Water Distribution Networks Under Different Sensor Network Architectures and Data Sampling Frequencies. In: 2nd International Symposium on Water System Operations (ISWSO), Bristol, United Kingdom, 1-3 September 2021,

Ferrer, A. J., Becker, S., Schmidt, F., Thamsen, L. and Kao, O. (2021) Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms. In: 1st Workshop on the Cloud-to-Things Continuum (Cloud2Things) at 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Melbourne, Australia, 10-13 May 2021, pp. 634-641. ISBN 9781728195865 (doi: 10.1109/CCGrid51090.2021.00076)

Scheinert, D., Acker, A., Thamsen, L., Geldenhuys, M. K. and Kao, O. (2021) Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies. In: 2nd Workshop on Cloud Intelligence (CloudIntelligence) at the 43th International Conference on Software Engineering (ICSE), 29 May 2021, pp. 7-12. ISBN 9781665445634 (doi: 10.1109/CloudIntelligence52565.2021.00011)

Wiesner, P. and Thamsen, L. (2021) LEAF: Simulating Large Energy-Aware Fog Computing Environments. In: 2021 5th IEEE International Conference on Fog and Edge Computing (ICFEC), Melbourne, Australia, 10-13 May 2021, pp. 29-36. ISBN 9781665402910 (doi: 10.1109/ICFEC51620.2021.00012)

2020

Geldenhuys, M., Thamsen, L. and Kao, O. (2020) Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs. In: 2020 IEEE International Conference on Big Data (Big Data), 10-13 Dec 2020, pp. 434-440. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9378474)

Lorenz, F., Geldenhuys, M., Sommer, H., Jakobs, F., Lüring, C., Skwarek, V., Behnke, I. and Thamsen, L. (2020) A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring. In: 2020 IEEE International Conference on Big Data (Big Data), 10-13 Dec 2020, pp. 3488-3493. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9378138)

Will, J., Bader, J. and Thamsen, L. (2020) Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs. In: 2020 IEEE International Conference on Big Data (Big Data), 10-13 Dec 2020, pp. 2851-2856. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9377994)

Behnke, I., Pirl, L., Thamsen, L., Danicki, R., Polze, A. and Kao, O. (2020) Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet? In: 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC), 06-08 Nov 2020, ISBN 9781728198293 (doi: 10.1109/IPCCC50635.2020.9391536)

Lorenz, F., Thamsen, L., Wilke, A., Behnke, I., Waldmüller-Littke, J., Komarov, I., Kao, O. and Paeschke, M. (2020) Fingerprinting Analog IoT Sensors for Secret-Free Authentication. In: 2020 29th International Conference on Computer Communications and Networks (ICCCN), 03-06 Aug 2020, ISBN 9781728166070 (doi: 10.1109/ICCCN49398.2020.9209643)

Thamsen, L., Verbitskiy, I., Nedelkoski, S., Tran, V. T., Meyer, V., Xavier, M. G., Kao, O. and De Rose, C. A. F. (2020) Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs. In: Euro-Par 2019 International Workshops, Göttingen, Germany, 26-30 Aug 2019, pp. 519-530. ISBN 9783030483401 (doi: 10.1007/978-3-030-48340-1_40)

Geldenhuys, M. K., Thamsen, L., Gontarska, K. K., Lorenz, F. and Kao, O. (2020) Effectively Testing System Configurations of Critical IoT Analytics Pipelines. In: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 09-12 Dec 2019, pp. 4157-4162. ISBN 9781728108582 (doi: 10.1109/BigData47090.2019.9005504)

2019

Behnke, I., Thamsen, L. and Kao, O. (2019) Héctor: A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds. In: IEEE/ACM 12th International Conference on Utility and Cloud Computing (UCC '19), Auckland, New Zealand, 02-05 Dec 2019, pp. 15-20. ISBN 9781450370448 (doi: 10.1145/3368235.3368832)

Nedelkoski, S., Thamsen, L., Verbitskiy, I. and Kao, O. (2019) Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures. In: 2019 IEEE International Conference on Edge Computing (EDGE), Milan, Italy, 08-13 Jul 2019, pp. 8-12. ISBN 9781728127088 (doi: 10.1109/EDGE.2019.00015)

Janßen, G., Verbitskiy, I., Renner, T. and Thamsen, L. (2019) Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10-13 Dec 2018, pp. 5159-5164. ISBN 9781538650356 (doi: 10.1109/BigData.2018.8622651)

2018

Verbitskiy, I., Thamsen, L., Renner, T. and Kao, O. (2018) CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters. In: 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Nicosia, Cyprus, 10-13 Dec 2018, pp. 81-88. ISBN 9781538678992 (doi: 10.1109/CloudCom2018.2018.00029)

Koch, J., Thamsen, L., Schmidt, F. and Kao, O. (2018) SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows. In: 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Taipei, Taiwan, 18-20 Dec 2017, pp. 156-163. ISBN 9781538631515 (doi: 10.1109/PDCAT.2017.00034)

Thamsen, L., Renner, T., Verbitskiy, I. and Kao, O. (2018) Adaptive Resource Management for Distributed Data Analytics. In: Grandinetti, L., Mirtaheri, S. L., Shahbazian, R., Sterling, T. and Voevodin, V. (eds.) Big Data and HPC: Ecosystem and Convergence. Series: Advances in Parallel Computing. IOS Press: Amsterdam, pp. 155-170. ISBN 9781614998815 (doi: 10.3233/978-1-61499-882-2-155)

2017

Thamsen, L., Verbitskiy, I., Beilharz, J., Renner, T., Polze, A. and Kao, O. (2017) Ellis: Dynamically Scaling Distributed Dataflows to Meet Runtime Targets. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Hong Kong, China, 11-14 Dec 2017, pp. 146-153. ISBN 9781538606926 (doi: 10.1109/CloudCom.2017.37)

Renner, T., Müller, J., Thamsen, L. and Kao, O. (2017) Addressing Hadoop's Small File Problem with an Appendable Archive File Format. In: Computing Frontiers Conference (CF '17) - Workshop on Big Data Analytics (BigDAW '17), Siena, Italy, 15-17 May 2017, pp. 367-372. ISBN 9781450344876 (doi: 10.1145/3075564.3078888)

Renner, T., Thamsen, L. and Kao, O. (2017) CoLoc: Distributed Data and Container Colocation for Data-Intensive Applications. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 05-08 Dec 2016, pp. 3008-3015. ISBN 9781467390057 (doi: 10.1109/BigData.2016.7840954)

Thamsen, L., Renner, T., Byfeld, M., Paeschke, M., Schröder, D. and Böhm, F. (2017) Visually Programming Dataflows for Distributed Data Analytics. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 05-08 Dec 2016, pp. 2276-2285. ISBN 9781467390057 (doi: 10.1109/BigData.2016.7840860)

Thamsen, L., Verbitskiy, I., Schmidt, F., Renner, T. and Kao, O. (2017) Selecting Resources for Distributed Dataflow Systems According to Runtime Targets. In: 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), Las Vegas, NV, USA, 09-11 Dec 2016, ISBN 9781509052523 (doi: 10.1109/PCCC.2016.7820629)

Verbitskiy, I., Thamsen, L. and Kao, O. (2017) When to Use a Distributed Dataflow Engine: Evaluating the Performance of Apache Flink. In: 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 18-21 Jul 2016, pp. 698-705. ISBN 9781509027712 (doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0114)

Renner, T., Thamsen, L. and Kao, O. (2017) Adaptive Resource Management for Distributed Data Analytics Based On Container-Level Cluster Monitoring. In: 6th International Conference on Data Science, Technology and Applications, Madrid, Spain, 24-26 Jul 2017, pp. 38-47. ISBN 9789897582554 (doi: 10.5220/0006420100380047)

Thamsen, L., Rabier, B., Schmidt, F., Renner, T. and Kao, O. (2017) Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference. In: 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA, 25-30 June 2017, pp. 145-152. ISBN 9781538619964 (doi: 10.1109/BigDataCongress.2017.28)

Thamsen, L., Verbitskiy, I., Rabier, B. and Kao, O. (2017) Learning efficient co-locations for scheduling distributed dataflows in shared clusters. Services Transactions on Big Data, 4(1),

2016

Thamsen, L., Renner, T. and Kao, O. (2016) Continuously Improving the Resource Utilization of Iterative Parallel Dataflows. In: 2016 IEEE 36th International Conference on Distributed Computing Systems Workshops (ICDCSW), Nara, Japan, 27-30 Jun 2016, pp. 1-6. ISBN 9781509036868 (doi: 10.1109/ICDCSW.2016.20)

Herb, T., Thamsen, L., Renner, T. and Kao, O. (2016) Aura: A Flexible Dataflow Engine for Scalable Data Processing. In: 9th International Workshop on Parallel Tools for High Performance Computing, Dresden, Germany, pp. 117-126. ISBN 9783319395890 (doi: 10.1007/978-3-319-39589-0_9)

Otto, P. et al. (2016) Exploratory Authoring of Interactive Content in a Live Environment. Technical Report. Universitätsverlag Potsdam.

Thamsen, L., Steinert, B. and Hirschfeld, R. (2016) Preserving Access to Previous System States in the Lively Kernel. In: Plattner, H., Meinel, C. and Leifer, L. (eds.) Design Thinking Research: Making Design Thinking Foundational. Series: Understanding Innovation. Springer: Cham, pp. 235-264. ISBN 9783319196404 (doi: 10.1007/978-3-319-19641-1_15)

2015

Renner, T., Thamsen, L. and Kao, O. (2015) Network-Aware Resource Management for Scalable Data Analytics Frameworks. In: 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 29 Oct - 01 Nov 2015, pp. 2793-2800. ISBN 9781479999262 (doi: 10.1109/BigData.2015.7364083)

Felgentreff, T., Lincke, J., Hirschfeld, R. and Thamsen, L. (2015) Lively Groups: Shared Behavior in a World of Objects Without Classes or Prototypes. In: Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH '15) - Workshop on Future Programming (FPW 2015), Pittsburgh, PA, USA, 26-26 Oct 2015, pp. 15-22. ISBN 9781450339056 (doi: 10.1145/2846656.2846659)

Alexandrov, A., Kunft, A., Katsifodimos, A., Schüler, F., Thamsen, L., Kao, O., Herb, T. and Markl, V. (2015) Implicit Parallelism Through Deep Language Embedding. In: International Conference on Management of Data (SIGMOD/PODS '15), Melbourne, Australia, 31 May - 04 Jun 2015, pp. 47-61. ISBN 9781450327589 (doi: 10.1145/2723372.2750543)

2014

Steinert, B., Thamsen, L., Felgentreff, T. and Hirschfeld, R. (2014) Object Versioning to Support Recovery Needs: Using Proxies to Preserve Previous Development States in Lively. In: 10th ACM Symposium on Dynamic languages (DLS '14), Portland, OR, USA, 20-24 Oct 2014, pp. 113-124. ISBN 9781450332118 (doi: 10.1145/2661088.2661093)

2012

Thamsen, L., Gulenko, A., Perscheid, M., Krahn, R., Hirschfeld, R. and Thomas, D. A. (2012) Orca: A Single-Language Web Framework for Collaborative Development. In: 2012 10th International Conference on Creating, Connecting and Collaborating through Computing, Playa Vista, CA, USA, 18-20 Jan 2012, pp. 45-52. ISBN 9781467310093 (doi: 10.1109/C5.2012.9)

This list was generated on Sun Apr 14 11:48:09 2024 BST.
Number of items: 85.

Articles

Chen, S. et al. (2024) Coupled simulation of urban water networks and interconnected critical urban infrastructure systems: A systematic review and multi-sector research agenda. Sustainable Cities and Society, 104, 105283. (doi: 10.1016/j.scs.2024.105283)

Bader, J., Lehmann, F., Thamsen, L., Leser, U. and Kao, O. (2024) Lotaru: Locally predicting workflow task runtimes for resource management on heterogeneous infrastructures. Future Generation Computer Systems, 150, pp. 171-185. (doi: 10.1016/j.future.2023.08.022)

Behnke, I., Blumschein, C., Danicki, R., Wiesner, P., Thamsen, L. and Kao, O. (2023) Towards a real-time IoT: approaches for incoming packet processing in cyber-physical systems. Journal of Systems Architecture, 140, 102891. (doi: 10.1016/j.sysarc.2023.102891)

Thamsen, L., Scheinert, D., Will, J., Bader, J. and Kao, O. (2022) Collaborative cluster configuration for distributed data-parallel processing: a research overview. Datenbank-Spektrum, 22(2), pp. 143-151. (doi: 10.1007/s13222-022-00416-z)

Bermbach, D., Bader, J., Hasenburg, J., Pfandzelter, T. and Thamsen, L. (2022) AuctionWhisk: using an auction-inspired approach for function placement in serverless fog platforms. Software: Practice and Experience, 52(5), pp. 1143-1169. (doi: 10.1002/spe.3058)

Thamsen, L., Beilharz, J., Tran, V. T., Nedelkoski, S. and Kao, O. (2021) Mary, Hugo, and Hugo*: learning to schedule distributed data-parallel processing jobs on shared clusters. Concurrency and Computation: Practice and Experience, 33(18), e5823. (doi: 10.1002/cpe.5823)

Thamsen, L., Verbitskiy, I., Rabier, B. and Kao, O. (2017) Learning efficient co-locations for scheduling distributed dataflows in shared clusters. Services Transactions on Big Data, 4(1),

Book Sections

Thamsen, L., Renner, T., Verbitskiy, I. and Kao, O. (2018) Adaptive Resource Management for Distributed Data Analytics. In: Grandinetti, L., Mirtaheri, S. L., Shahbazian, R., Sterling, T. and Voevodin, V. (eds.) Big Data and HPC: Ecosystem and Convergence. Series: Advances in Parallel Computing. IOS Press: Amsterdam, pp. 155-170. ISBN 9781614998815 (doi: 10.3233/978-1-61499-882-2-155)

Thamsen, L., Steinert, B. and Hirschfeld, R. (2016) Preserving Access to Previous System States in the Lively Kernel. In: Plattner, H., Meinel, C. and Leifer, L. (eds.) Design Thinking Research: Making Design Thinking Foundational. Series: Understanding Innovation. Springer: Cham, pp. 235-264. ISBN 9783319196404 (doi: 10.1007/978-3-319-19641-1_15)

Research Reports or Papers

Thamsen, L., Beilharz, J., Polze, A. and Kao, O. (2022) The Methods of Cloud Computing. Technical Report. Technische Universität Berlin DepositOnce. (doi: 10.14279/depositonce-15190).

Otto, P. et al. (2016) Exploratory Authoring of Interactive Content in a Live Environment. Technical Report. Universitätsverlag Potsdam.

Conference or Workshop Item

Lehmann, F., Bader, J., Thamsen, L. and Leser, U. (2023) The Common Workflow Scheduler Interface: Status Quo and Future Plans. Workshop on Workflows in Support of Large-Scale Science (WORKS23),The International Conference on High Performance Computing, Network, Storage, and Analysis, New York, USA, 12-17 Nov 2023. ISBN 9798400707858 (doi: 10.1145/3624062.3626283)

Beilharz, J., Wiesner, P., Boockmeyer, A., Friedenberger, D., Brokhausen, F., Pirl, L., Behnke, I., Polze, A. and Thamsen, L. (2021) Continuously Testing Distributed IoT Systems: An Overview of the State of the Art. 19th International Conference on Service-Oriented Computing (ICSOC) Workshops, Virtual Event, 22–25 November 2021.

Conference Proceedings

Will, J., Scheinert, D., Zunzer, S., Bode, J., Kring, C. and Thamsen, L. (2024) Privacy-Preserving Sharing of Data Analytics Runtime Metrics for Performance Modeling. In: ICPE 2024: The 9th Workshop on Challenges in Performance Methods for Software Development (WOSP-C), London, UK, 7-11 May 2024, (Accepted for Publication)

Geldenhuys, M. K., Scheinert, D., Kao, O. and Thamsen, L. (2024) Demeter: Resource-Efficient Distributed Stream Processing under Dynamic Loads with Multi-Configuration Optimization. In: 15th ACM/SPEC International Conference on Performance Engineering (ICPE 2024), London, UK, 7-11 May 2024, (Accepted for Publication)

Bader, J., Diedrich, N., Thamsen, L. and Kao, O. (2024) Predicting Dynamic Memory Requirements for Scientific Workflow Tasks. In: 2023 IEEE International Conference on Big Data, Sorrento, Italy, 15-18 Dec 2023, ISBN 9798350324457 (doi: 10.1109/BigData59044.2023.10386837)

Scheinert, D., Becker, S., Will, J., Englaender, L. and Thamsen, L. (2024) Towards a Peer-to-Peer Data Distribution Layer for Efficient and Collaborative Resource Optimization of Distributed Dataflow Applications. In: 2023 IEEE International Conference on Big Data (Big Data), Sorrento, Italy, 15-18 Dec 2023, pp. 2339-2345. ISBN 9798350324457 (doi: 10.1109/BigData59044.2023.10386195)

Wiesner, P., Khalili, R., Grinwald, D., Agrawal, P., Thamsen, L. and Kao, O. (2023) FedZero: Leveraging Renewable Excess Energy in Federated Learning. In: 15th ACM International Conference on Future and Sustainable Energy Systems (ACM e-Energy 2024), Singapore, 4-7 June 2024, (Accepted for Publication)

Will, J., Thamsen, L., Scheinert, D. and Kao, O. (2023) Selecting Efficient Cluster Resources for Data Analytics: When and How to Allocate for In-Memory Processing? In: 35th International Conference on Scientific and Statistical Database Management (SSDBM2023), Los Angeles, California, USA, 10-12 July 2023, ISBN 9798400707469 (doi: 10.1145/3603719.3603733)

Scheinert, D., Wiesner, P., Wittkopp, T., Thamsen, L., Will, J. and Kao, O. (2023) Karasu: A Collaborative Approach to Efficient Cluster Configuration for Big Data Analytics. In: 42nd IEEE International Performance Computing and Communications Conference (IPCCC 2023), Anaheim, CA, USA, 17-19 Nov 2023, (Accepted for Publication)

Lehmann, F., Bader, J., Tschirpke, F., Thamsen, L. and Leser, U. (2023) How Workflow Engines Should Talk to Resource Managers: A Proposal for a Common Workflow Scheduling Interface. In: 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid 2023), Bangalore, India, 1-4 May 2023, pp. 166-179. ISBN 9798350301199 (doi: 10.1109/CCGrid57682.2023.00025)

Trihinas, D. and Thamsen, L. (2023) Towards Energy-Aware Machine Learning in Geo-Distributed IoT Settings. In: Euro-Par 2023, Limassol, Cyprus, 28 Aug - 01 Sep 2023, (Accepted for Publication)

Scheinert, D., Becker, S., Bader, J., Thamsen, L., Will, J. and Kao, O. (2023) Perona: Robust Infrastructure Fingerprinting for Resource-Efficient Big Data Analytics. In: 2022 IEEE International Conference on Big Data, Osaka, Japan, 17-20 December 2022, pp. 209-216. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10020860)

Scheinert, D., Zadeh Aghdam, B. S., Becker, S., Kao, O. and Thamsen, L. (2023) Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments. In: 2022 IEEE International Conference on Big Data (IEEE BigData 2022), Osaka, Japan, 17-20 Dec 2022, pp. 4583-4588. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10021129)

Will, J., Thamsen, L., Bader, J., Scheinert, D. and Kao, O. (2023) Ruya: Memory-Aware Iterative Optimization of Cluster Configurations for Big Data Processing. In: 2022 IEEE International Conference on Big Data, Osaka, Japan, 17-20 December 2022, pp. 161-169. ISBN 9781665480451 (doi: 10.1109/BigData55660.2022.10020295)

Trihinas, D., Thamsen, L., Beilharz, J. and Symeonides, M. (2022) Towards Energy Consumption and Carbon Footprint Testing for AI-driven IoT Services. In: 10th IEEE International Conference on Cloud Engineering (IC2E), California, USA, 26-30 Sept 2022, pp. 29-35. ISBN 9781665491150 (doi: 10.1109/IC2E55432.2022.00011)

Will, J., Thamsen, L., Bader, J., Scheinert, D. and Kao, O. (2022) Get Your Memory Right: The Crispy Resource Allocation Assistant for Large-Scale Data Processing. In: 10th IEEE International Conference on Cloud Engineering (IC2E), California, USA, 26-30 Sept 2022, ISBN 9781665491150 (doi: 10.1109/IC2E55432.2022.00014)

Zhu, H., Scheinert, D., Thamsen, L., Gontarska, K. and Kao, O. (2022) Magpie: Automatically Tuning Static Parameters for Distributed File Systems using Deep Reinforcement Learning. In: 10th IEEE International Conference on Cloud Engineering (IC2E), California, USA, 26-30 Sept 2022, pp. 150-159. ISBN 9781665491150 (doi: 10.1109/IC2E55432.2022.00023)

Bader, J., Lehmann, F., Groth, A., Thamsen, L., Scheinert, D., Will, J., Leser, U. and Kao, O. (2022) Reshi: Recommending Resources for Scientific Workflow Tasks on Heterogeneous Infrastructures. In: 41st IEEE International Performance, Computing, and Communications Conference (IPCCC 2022), Austin, TX, USA, 11-13 November 2022, pp. 269-274. ISBN 9781665480185 (doi: 10.1109/IPCCC55026.2022.9894299)

Geldenhuys, M. K., Pfister, B. J. J., Scheinert, D., Thamsen, L. and Kao, O. (2022) Khaos: Dynamically Optimizing Checkpointing for Dependable Distributed Stream Processing. In: 17th Conference On Computer Science And Intelligence Systems (FedCSIS 2022), Sofia, Bulgaria, 4-7 September 2022, pp. 553-561. ISBN 9788396242396 (doi: 10.15439/2022F225)

Geldenhuys, M. K., Scheinert, D., Kao, O. and Thamsen, L. (2022) Phoebe: QoS-Aware Distributed Stream Processing through Anticipating Dynamic Workloads. In: IEEE International Conference on Web Services (ICWS 2022), Barcelona, Spain, 11-15 July 2022, pp. 198-207. ISBN 9781665481434 (doi: 10.1109/ICWS55610.2022.00041)

Bader, J., Lehmann, F., Thamsen, L., Will, J., Leser, U. and Kao, O. (2022) Lotaru: Locally Estimating Runtimes of Scientific Workflow Tasks in Heterogeneous Clusters. In: 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022), Copenhagen, Denmark, 6-8 July 2022, ISBN 9781450396677 (doi: 10.1145/3538712.3538739)

Wiesner, P., Scheinert, D., Wittkopp, T., Thamsen, L. and Kao, O. (2022) Cucumber: Renewable-Aware Admission Control for Delay-Tolerant Cloud and Edge Workloads. In: 28th International European Conference on Parallel and Distributed Computing (EURO-PAR 2022), Glasgow, Scotland, United Kingdom, 22-26 August 2022, pp. 218-232. ISBN 9783031125966 (doi: 10.1007/978-3-031-12597-3_14)

Pfister, B. J. J., Lickefett, W. S., Nitschke, J., Paul, S., Geldenhuys, M. K., Scheinert, D., Gontarska, K. and Thamsen, L. (2022) Rafiki: Task-level Capacity Planning in Distributed Stream Processing Systems. In: 27th International European Conference on Parallel and Distributed Computing (Euro-Par 2021), 30 August – 3 September 2021, pp. 352-363. ISBN 9783031061554 (doi: 10.1007/978-3-031-06156-1_28)

Habenicht, D., Kreutz, K., Becker, S., Bader, J., Thamsen, L. and Kao, O. (2022) SyncMesh: Improving Data Locality for Function-as-a-Service in Meshed Edge Networks. In: 5th International Workshop on Edge Systems, Analytics and Networking (EdgeSys' 22), Rennes, France, 05 Apr 2022, pp. 55-60. ISBN 9781450392532 (doi: 10.1145/3517206.3526275)

Blumschein, C., Behnke, I., Thamsen, L. and Kao, O. (2022) Differentiating Network Flows for Priority-Aware Scheduling of Incoming Packets in Real-Time IoT Systems. In: 2022 IEEE 25th International Symposium on Real-Time Distributed Computing (ISORC), Västerås, Sweden, 17-18 May 2022, ISBN 9781665406277 (doi: 10.1109/ISORC52572.2022.9812841)

Behnke, I., Wiesner, P., Danicki, R. and Thamsen, L. (2022) A Priority-Aware Multiqueue NIC Design for Real-Time IoT Devices. In: 37th ACM/SIGAPP Symposium on Applied Computing (SAC), Virtual Event, 25 - 29 April 2022, pp. 534-538. ISBN 9781450387132 (doi: 10.1145/3477314.3507165)

Wiesner, P., Behnke, I., Scheinert, D., Gontarska, K. and Thamsen, L. (2021) Let's Wait Awhile: How Temporal Workload Shifting Can Reduce Carbon Emissions in the Cloud. In: 22nd International Middleware Conference (Middleware '21), 06-10 Dec 2021, pp. 260-272. ISBN 9781450385343 (doi: 10.1145/3464298.3493399)

Bader, J., Thamsen, L., Kulagina, S., Will, J., Meyerhenke, H. and Kao, O. (2021) Tarema: Adaptive Resource Allocation for Scalable Scientific Workflows in Heterogeneous Clusters. In: 2021 IEEE International Conference on Big Data (Big Data), 15-18 Dec 2021, pp. 65-75. ISBN 9781665439022 (doi: 10.1109/BigData52589.2021.9671519)

Scheinert, D., Alamgiralem, A., Bader, J., Will, J., Wittkopp, T. and Thamsen, L. (2021) On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds. In: 2021 IEEE International Conference on Big Data (Big Data), 15-18 Dec 2021, pp. 3113-3118. ISBN 9781665439022 (doi: 10.1109/BigData52589.2021.9671275)

Will, J., Arslan, O., Bader, J., Scheinert, D. and Thamsen, L. (2021) Training Data Reduction for Performance Models of Data Analytics Jobs in the Cloud. In: 2021 IEEE International Conference on Big Data (Big Data), 15-18 Dec 2021, pp. 3141-3146. ISBN 9781665439022 (doi: 10.1109/BigData52589.2021.9671742)

Geldenhuys, M. K., Will, J., Pfister, B. J.J., Haug, M., Scharmann, A. and Thamsen, L. (2021) Dependable IoT Data Stream Processing for Monitoring and Control of Urban Infrastructures. In: 1st International Workshop on Testing Distributed Internet of Things Systems (TDIS) at 9th IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 244-250. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00041)

Gontarska, K., Geldenhuys, M., Scheinert, D., Wiesner, P., Polze, A. and Thamsen, L. (2021) Evaluation of Load Prediction Techniques for Distributed Stream Processing. In: 9th International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 91-98. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00023)

Haug, M., Lorenz, F. and Thamsen, L. (2021) GRAL: Localization of Floating Wireless Sensors in Pipe Networks. In: 1st International Workshop on Testing Distributed Internet of Things Systems (TDIS) at 9th IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 251-257. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00042)

Becker, S., Schmidt, F., Thamsen, L., Ferrer, A. J. and Kao, O. (2021) LOS: Local-Optimistic Scheduling of Periodic Model Training For Anomaly Detection on Sensor Data Streams in Meshed Edge Networks. In: 2021 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS), 27 Sep - 01 Oct 2021, pp. 41-50. ISBN 9781665412612 (doi: 10.1109/ACSOS52086.2021.00033)

Bermbach, B., Chandra, A., Krintz, C., Gokhale, A., Slominski, A., Thamsen, L., Cavalcante, E., Guo, T., Brandic, I. and Wolski, R. (2021) On the Future of Cloud Engineering. In: 2021 IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 264-275. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00044)

Scheinert, D., Zhu, H., Thamsen, L., Geldenhuys, M. K., Will, J., Acker, A. and Kao, O. (2021) Enel: Context-Aware Dynamic Scaling of Distributed Dataflow Jobs Using Graph Propagation. In: 2021 IEEE International Performance, Computing, and Communications Conference (IPCCC), 29-31 Oct 2021, ISBN 9781665443319 (doi: 10.1109/IPCCC51483.2021.9679361)

Will, J., Thamsen, L., Scheinert, D., Bader, J. and Kao, O. (2021) C3O: Collaborative Cluster Configuration Optimization for Distributed Data Processing in Public Clouds. In: 2021 IEEE International Conference on Cloud Engineering (IC2E), 04-08 Oct 2021, pp. 43-52. ISBN 9781665449700 (doi: 10.1109/IC2E52221.2021.00018)

Scheinert, D., Thamsen, L., Zhu, H., Will, J., Acker, A., Wittkopp, T. and Kao, O. (2021) Bellamy: Reusing Performance Models for Distributed Dataflow Jobs Across Contexts. In: 2021 IEEE International Conference on Cluster Computing (CLUSTER), 07-10 Sep 2021, pp. 261-270. ISBN 9781728196664 (doi: 10.1109/Cluster48925.2021.00052)

Tilcher, D. K., Popescu, F., Sommer, H., Thamsen, L. and Thamsen, P. U. (2021) Control Optimization Through Prediction-Based Wastewater Management. In: ASME 2021 Fluids Engineering Division Summer Meeting, 10-12 Aug 2021, ISBN 9780791885291 (doi: 10.1115/FEDSM2021-65375)

Gontarska, K., Wrazen, W., Beilharz, J., Schmid, R., Thamsen, L. and Polze, A. (2021) Predicting Medical Interventions From Vital Parameters: Towards a Decision Support System for Remote Patient Monitoring. In: 19th Conference on Artificial Intelligence in Medicine (AIME'21), 15-18 Jun 2021, pp. 293-297. ISBN 9783030772109 (doi: 10.1007/978-3-030-77211-6_33)

Danicki, R., Haug, M., Behnke, I., Mädje, L. and Thamsen, L. (2021) Detecting and Mitigating Network Packet Overloads on Real-Time Devices in IoT Systems. In: 4th International Workshop on Edge Systems, Analytics and Networking (EdgeSys 2021), Online, United Kingdom, 26 April 2021, pp. 25-30. ISBN 9781450382915 (doi: 10.1145/3434770.3459733)

Beilharz, J., Wiesner, P., Boockmeyer, A., Brokhausen, F., Behnke, I., Schmid, R., Pirl, L. and Thamsen, L. (2021) Towards a Staging Environment for the Internet of Things. In: 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), 22-26 Mar 2021, pp. 312-315. ISBN 9781665404242 (doi: 10.1109/PerComWorkshops51409.2021.9431087)

Bender, F., Brune, J. J., Keutel, N. L., Behnke, I. and Thamsen, L. (2021) PIERES: A Playground for Network Interrupt Experiments on Real-Time Embedded Systems in the IoT. In: 9th International Workshop on Load Testing and Benchmarking of Software Systems (LTB) at the 12th ACM/SPEC International Conference on Performance Engineering (ICPE), 19 - 23 April 2021, pp. 81-84. ISBN 9781450383318 (doi: 10.1145/3447545.3451189)

Chen, S., Brokhausen, F., Wiesner, P., Thamsen, L. and Cominola, A. (2021) Assessing the Resilience of Water Distribution Networks Under Different Sensor Network Architectures and Data Sampling Frequencies. In: 2nd International Symposium on Water System Operations (ISWSO), Bristol, United Kingdom, 1-3 September 2021,

Ferrer, A. J., Becker, S., Schmidt, F., Thamsen, L. and Kao, O. (2021) Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms. In: 1st Workshop on the Cloud-to-Things Continuum (Cloud2Things) at 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCGrid), Melbourne, Australia, 10-13 May 2021, pp. 634-641. ISBN 9781728195865 (doi: 10.1109/CCGrid51090.2021.00076)

Scheinert, D., Acker, A., Thamsen, L., Geldenhuys, M. K. and Kao, O. (2021) Learning Dependencies in Distributed Cloud Applications to Identify and Localize Anomalies. In: 2nd Workshop on Cloud Intelligence (CloudIntelligence) at the 43th International Conference on Software Engineering (ICSE), 29 May 2021, pp. 7-12. ISBN 9781665445634 (doi: 10.1109/CloudIntelligence52565.2021.00011)

Wiesner, P. and Thamsen, L. (2021) LEAF: Simulating Large Energy-Aware Fog Computing Environments. In: 2021 5th IEEE International Conference on Fog and Edge Computing (ICFEC), Melbourne, Australia, 10-13 May 2021, pp. 29-36. ISBN 9781665402910 (doi: 10.1109/ICFEC51620.2021.00012)

Geldenhuys, M., Thamsen, L. and Kao, O. (2020) Chiron: Optimizing Fault Tolerance in QoS-aware Distributed Stream Processing Jobs. In: 2020 IEEE International Conference on Big Data (Big Data), 10-13 Dec 2020, pp. 434-440. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9378474)

Lorenz, F., Geldenhuys, M., Sommer, H., Jakobs, F., Lüring, C., Skwarek, V., Behnke, I. and Thamsen, L. (2020) A Scalable and Dependable Data Analytics Platform for Water Infrastructure Monitoring. In: 2020 IEEE International Conference on Big Data (Big Data), 10-13 Dec 2020, pp. 3488-3493. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9378138)

Will, J., Bader, J. and Thamsen, L. (2020) Towards Collaborative Optimization of Cluster Configurations for Distributed Dataflow Jobs. In: 2020 IEEE International Conference on Big Data (Big Data), 10-13 Dec 2020, pp. 2851-2856. ISBN 9781728162515 (doi: 10.1109/BigData50022.2020.9377994)

Behnke, I., Pirl, L., Thamsen, L., Danicki, R., Polze, A. and Kao, O. (2020) Interrupting Real-Time IoT Tasks: How Bad Can It Be to Connect Your Critical Embedded System to the Internet? In: 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC), 06-08 Nov 2020, ISBN 9781728198293 (doi: 10.1109/IPCCC50635.2020.9391536)

Lorenz, F., Thamsen, L., Wilke, A., Behnke, I., Waldmüller-Littke, J., Komarov, I., Kao, O. and Paeschke, M. (2020) Fingerprinting Analog IoT Sensors for Secret-Free Authentication. In: 2020 29th International Conference on Computer Communications and Networks (ICCCN), 03-06 Aug 2020, ISBN 9781728166070 (doi: 10.1109/ICCCN49398.2020.9209643)

Thamsen, L., Verbitskiy, I., Nedelkoski, S., Tran, V. T., Meyer, V., Xavier, M. G., Kao, O. and De Rose, C. A. F. (2020) Hugo: A Cluster Scheduler that Efficiently Learns to Select Complementary Data-Parallel Jobs. In: Euro-Par 2019 International Workshops, Göttingen, Germany, 26-30 Aug 2019, pp. 519-530. ISBN 9783030483401 (doi: 10.1007/978-3-030-48340-1_40)

Geldenhuys, M. K., Thamsen, L., Gontarska, K. K., Lorenz, F. and Kao, O. (2020) Effectively Testing System Configurations of Critical IoT Analytics Pipelines. In: 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 09-12 Dec 2019, pp. 4157-4162. ISBN 9781728108582 (doi: 10.1109/BigData47090.2019.9005504)

Behnke, I., Thamsen, L. and Kao, O. (2019) Héctor: A Framework for Testing IoT Applications Across Heterogeneous Edge and Cloud Testbeds. In: IEEE/ACM 12th International Conference on Utility and Cloud Computing (UCC '19), Auckland, New Zealand, 02-05 Dec 2019, pp. 15-20. ISBN 9781450370448 (doi: 10.1145/3368235.3368832)

Nedelkoski, S., Thamsen, L., Verbitskiy, I. and Kao, O. (2019) Multilayer Active Learning for Efficient Learning and Resource Usage in Distributed IoT Architectures. In: 2019 IEEE International Conference on Edge Computing (EDGE), Milan, Italy, 08-13 Jul 2019, pp. 8-12. ISBN 9781728127088 (doi: 10.1109/EDGE.2019.00015)

Janßen, G., Verbitskiy, I., Renner, T. and Thamsen, L. (2019) Scheduling Stream Processing Tasks on Geo-Distributed Heterogeneous Resources. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 10-13 Dec 2018, pp. 5159-5164. ISBN 9781538650356 (doi: 10.1109/BigData.2018.8622651)

Verbitskiy, I., Thamsen, L., Renner, T. and Kao, O. (2018) CoBell: Runtime Prediction for Distributed Dataflow Jobs in Shared Clusters. In: 2018 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Nicosia, Cyprus, 10-13 Dec 2018, pp. 81-88. ISBN 9781538678992 (doi: 10.1109/CloudCom2018.2018.00029)

Koch, J., Thamsen, L., Schmidt, F. and Kao, O. (2018) SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows. In: 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Taipei, Taiwan, 18-20 Dec 2017, pp. 156-163. ISBN 9781538631515 (doi: 10.1109/PDCAT.2017.00034)

Thamsen, L., Verbitskiy, I., Beilharz, J., Renner, T., Polze, A. and Kao, O. (2017) Ellis: Dynamically Scaling Distributed Dataflows to Meet Runtime Targets. In: 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Hong Kong, China, 11-14 Dec 2017, pp. 146-153. ISBN 9781538606926 (doi: 10.1109/CloudCom.2017.37)

Renner, T., Müller, J., Thamsen, L. and Kao, O. (2017) Addressing Hadoop's Small File Problem with an Appendable Archive File Format. In: Computing Frontiers Conference (CF '17) - Workshop on Big Data Analytics (BigDAW '17), Siena, Italy, 15-17 May 2017, pp. 367-372. ISBN 9781450344876 (doi: 10.1145/3075564.3078888)

Renner, T., Thamsen, L. and Kao, O. (2017) CoLoc: Distributed Data and Container Colocation for Data-Intensive Applications. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 05-08 Dec 2016, pp. 3008-3015. ISBN 9781467390057 (doi: 10.1109/BigData.2016.7840954)

Thamsen, L., Renner, T., Byfeld, M., Paeschke, M., Schröder, D. and Böhm, F. (2017) Visually Programming Dataflows for Distributed Data Analytics. In: 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 05-08 Dec 2016, pp. 2276-2285. ISBN 9781467390057 (doi: 10.1109/BigData.2016.7840860)

Thamsen, L., Verbitskiy, I., Schmidt, F., Renner, T. and Kao, O. (2017) Selecting Resources for Distributed Dataflow Systems According to Runtime Targets. In: 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), Las Vegas, NV, USA, 09-11 Dec 2016, ISBN 9781509052523 (doi: 10.1109/PCCC.2016.7820629)

Verbitskiy, I., Thamsen, L. and Kao, O. (2017) When to Use a Distributed Dataflow Engine: Evaluating the Performance of Apache Flink. In: 2016 Intl IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), Toulouse, France, 18-21 Jul 2016, pp. 698-705. ISBN 9781509027712 (doi: 10.1109/UIC-ATC-ScalCom-CBDCom-IoP-SmartWorld.2016.0114)

Renner, T., Thamsen, L. and Kao, O. (2017) Adaptive Resource Management for Distributed Data Analytics Based On Container-Level Cluster Monitoring. In: 6th International Conference on Data Science, Technology and Applications, Madrid, Spain, 24-26 Jul 2017, pp. 38-47. ISBN 9789897582554 (doi: 10.5220/0006420100380047)

Thamsen, L., Rabier, B., Schmidt, F., Renner, T. and Kao, O. (2017) Scheduling Recurring Distributed Dataflow Jobs Based on Resource Utilization and Interference. In: 2017 IEEE International Congress on Big Data (BigData Congress), Honolulu, HI, USA, 25-30 June 2017, pp. 145-152. ISBN 9781538619964 (doi: 10.1109/BigDataCongress.2017.28)

Thamsen, L., Renner, T. and Kao, O. (2016) Continuously Improving the Resource Utilization of Iterative Parallel Dataflows. In: 2016 IEEE 36th International Conference on Distributed Computing Systems Workshops (ICDCSW), Nara, Japan, 27-30 Jun 2016, pp. 1-6. ISBN 9781509036868 (doi: 10.1109/ICDCSW.2016.20)

Herb, T., Thamsen, L., Renner, T. and Kao, O. (2016) Aura: A Flexible Dataflow Engine for Scalable Data Processing. In: 9th International Workshop on Parallel Tools for High Performance Computing, Dresden, Germany, pp. 117-126. ISBN 9783319395890 (doi: 10.1007/978-3-319-39589-0_9)

Renner, T., Thamsen, L. and Kao, O. (2015) Network-Aware Resource Management for Scalable Data Analytics Frameworks. In: 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 29 Oct - 01 Nov 2015, pp. 2793-2800. ISBN 9781479999262 (doi: 10.1109/BigData.2015.7364083)

Felgentreff, T., Lincke, J., Hirschfeld, R. and Thamsen, L. (2015) Lively Groups: Shared Behavior in a World of Objects Without Classes or Prototypes. In: Conference on Systems, Programming, Languages, and Applications: Software for Humanity (SPLASH '15) - Workshop on Future Programming (FPW 2015), Pittsburgh, PA, USA, 26-26 Oct 2015, pp. 15-22. ISBN 9781450339056 (doi: 10.1145/2846656.2846659)

Alexandrov, A., Kunft, A., Katsifodimos, A., Schüler, F., Thamsen, L., Kao, O., Herb, T. and Markl, V. (2015) Implicit Parallelism Through Deep Language Embedding. In: International Conference on Management of Data (SIGMOD/PODS '15), Melbourne, Australia, 31 May - 04 Jun 2015, pp. 47-61. ISBN 9781450327589 (doi: 10.1145/2723372.2750543)

Steinert, B., Thamsen, L., Felgentreff, T. and Hirschfeld, R. (2014) Object Versioning to Support Recovery Needs: Using Proxies to Preserve Previous Development States in Lively. In: 10th ACM Symposium on Dynamic languages (DLS '14), Portland, OR, USA, 20-24 Oct 2014, pp. 113-124. ISBN 9781450332118 (doi: 10.1145/2661088.2661093)

Thamsen, L., Gulenko, A., Perscheid, M., Krahn, R., Hirschfeld, R. and Thomas, D. A. (2012) Orca: A Single-Language Web Framework for Collaborative Development. In: 2012 10th International Conference on Creating, Connecting and Collaborating through Computing, Playa Vista, CA, USA, 18-20 Jan 2012, pp. 45-52. ISBN 9781467310093 (doi: 10.1109/C5.2012.9)

This list was generated on Sun Apr 14 11:48:09 2024 BST.

Grants

  • I am actively developing new research at the University of Glasgow and am looking for collaborators.
  • I (co-)developed nine successful project proposals under Prof. Odej Kao at TU Berlin as a postdoc and senior researcher.
    • I am currently still part of the DFG-funded collaborative research centre FONDA (until 2024) and the DFG-funded research project C5 (until 2026)
    • Previously, I was involved in two funded research projects in the research centres BBDC/BIFOLD (BMBF), the interdisciplinary projects WaterGridSense (BMBF) and OPTIMA (EU Regional Development Fund), as well as the European teaching network ide3a (DAAD).
    • I also helped to attract and lead an industry collaboration with Bundesdruckerei GmbH as well as acquire university infrastructure funds for a department GPU cluster at TU Berlin.
  • I was also part of the funded research project Telemed5000 (BMWi) and the HEIBRiDS graduate school (Helmholtz Association) as a senior researcher.

Supervision

Currently:

PhD students at Glasgow:

  • West, Kathleen
    Carbon-Aware Execution of Scientific Workflows on Heterogeneous Clusters

Previously:

  • I have worked closely with around a dozen PhD students at TU Berlin (in the Distributed and Operating Systems group of Prof. Odej Kao), HPI (in the Operating Systems and Middleware group of Prof. Andreas Polze), and HU Berlin (in the Knowledge Management in Bioinformatics group of Prof. Ulf Leser) as a postdoc and senior researcher at TU Berlin and as a guest professor at HU Berlin.
  • I have supervised over a dozen L4/L5 and MSc dissertation projects at the University of Glasgow and (co)-supervised over two dozen Bachelor and Master theses as well as ten semester-long student team projects at TU Berlin, HPI, and HU Berlin.

Teaching

I have been teaching the basics of distributed systems, operating systems, and systems engineering as well as related advanced topics such as cloud computing and big data systems.

A full list of lecture modules, seminars, team projects, interdisciplinary courses, and extracurricular teaching activities can be found at https://lauritzthamsen.org/teaching/.

Additional information

You can usually find me in S152 in the School of Computing Science (2nd floor, 15 Lilybank Gardens, accessible through the Sir Alwyn Williams Building, 18 Lilybank Gardens). When I am in the office, you can also reach me via +44 141 330 2000 (x0652).

I have recently started to serve as my School’s Sustainability Subject Adviser. In this role, I am helping to integrate sustainable computing learning objectives into the School's degree programmes. In this context, I also lecture on the growing environmental footprint of computing in our core Level 2 course on Networks and Operating Systems Essentials (NOSE2).

I am a member of the School's EDI (Equality, Diversity, and Inclusion) committee. Together with colleagues, I offer Disability Office Hours for CoSE students. We currently organise these once per semester. They are open to CoSE students of all programmes and levels (including UG, PGT, and PGR).

I help with CompuMatch, a service to match scientists of other disciplines with computer scientists to foster new and innovative interdisciplinary research collaborations.

Previously, I was part of the School's student conduct team, coordinating cases of academic misconduct.