Markus Götz
Professor
Works on scalable machine learning for high-performance computing, including distributed neural network training, low-rank architectures and energy-aware AI for scientific data.
Markus Götz's lab at TUM works on distributed, cloud & edge systems and accelerators, chips & architecture. 28 papers since 2023, mostly ECML-PKDD and ICONIP.
At a glance
- papers, 2023–2026
- 7/ year
- at T1 venues
- 0%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 6 papers2026 · T1 0 · collecting
- 9 papers2025 · T1 0
- 8 papers2024 · T1 0
- 5 papers2023 · T1 0
T1 venuesOtherVenues: ECML-PKDD, ICONIP, IJCNN
Research focuspaper titles that use each topic's words
- Distributed, cloud & edge systems3 papers
- network training1 paper
- Accelerators, chips & architecture0 papers
- scalable machine0 papers
- scientific data0 papers
Paper list
From DBLP.
20266 papers
- PASCCombining Domain and Tensor Parallelism to Train Multi-billion-parameter AI Weather Models (opens in a new tab)
- Künstliche Intell.Correlations Matter in Explanations for Energy Systems (opens in a new tab)
- CCGridInversetune: Inverse Synthetic Fine-Tuning for Reliable Structured Output in Small Language Models (opens in a new tab)
- IEEE Trans. Serv. Comput.LEMON: LLM-Enabled Monitoring for Microservices Orchestration (opens in a new tab)
- PASCSampling Parallelism for Fast and Efficient Bayesian Learning (opens in a new tab)
- J. Open Source Softw.torch_blue: A Flexible Python Package for Bayesian Neural Networks in PyTorch (opens in a new tab)
20259 papers
- ICDM WorkshopA Comparative Study of Pruning Methods in Transformer-Based Time Series Forecasting (opens in a new tab)
- IJCNNBeyond Backpropagation: Optimization with Multi-Tangent Forward Gradients (opens in a new tab)
- Electron. Commun. Eur. Assoc. Softw. Sci. Technol.Engineering a large-scale data analytics and array computing library for research: Heat (opens in a new tab)
- IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.Enhancing UAS-Based Multispectral Semantic Segmentation Through Feature Engineering (opens in a new tab)
- ICCSAExploring Federated Learning for Thermal Urban Feature Segmentation - A Comparison of Centralized and Decentralized Approaches (opens in a new tab)
- ISC High PerformanceLarge-Scale Modeling of the Extracellular Matrix on Hybrid Supercomputers Using Highly Parallel Stencil Computations (opens in a new tab)
- IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens.Ordinal METAR Cloud Cover Classification in Sentinel-2 Satellite Data (opens in a new tab)
- ICPPpyGinkgo: A Sparse Linear Algebra Operator Framework for Python (opens in a new tab)
- Trans. Mach. Learn. Res.Scaling Laws of Distributed Random Forests (opens in a new tab)
20248 papers
- e-EnergyFeasibility of Forecasting Highly Resolved Power Grid Frequency Utilizing Temporal Fusion Transformers (opens in a new tab)
- ICONIPFeed-Forward Optimization With Delayed Feedback for Neural Network Training (opens in a new tab)
- ECAIHarnessing Orthogonality to Train Low-Rank Neural Networks (opens in a new tab)
- ECML-PKDDModel Fusion via Neuron Transplantation (opens in a new tab)
- NICEPETNet-Coincident Particle Event Detection using Spiking Neural Networks (opens in a new tab)
- CAIReCycle: Fast and Efficient Long Time Series Forecasting with Residual Cyclic Transformers (opens in a new tab)
- ECAITaylor Expansion in Neural Networks: How Higher Orders Yield Better Predictions (opens in a new tab)
- IEEE AccessVectorized Highly Parallel Density-Based Clustering for Applications With Noise (opens in a new tab)
20235 papers
- Proc. IEEEDeep-Learning-Based 3-D Surface Reconstruction - A Survey (opens in a new tab)
- ISC High PerformanceMassively Parallel Genetic Optimization Through Asynchronous Propagation of Populations (opens in a new tab)
- Euro-Parperun: Benchmarking Energy Consumption of High-Performance Computing Applications (opens in a new tab)
- PatternsProviding AI expertise as an infrastructure in academia (opens in a new tab)
- Nat. Mac. Intell.Reporting electricity consumption is essential for sustainable AI (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to TUMRolling
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Computer Science Doctorate (CIT)
- Round
- Rolling (position-based)
- Opens
- —
- GRE
- See program page
- English
- Very good written and spoken English; no minimum test score
- Fee
- See program page
Confirmed on the program page.
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- DBLP
- Members
- Not collected
- Alumni
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