AI & Data Analytics

The MPCDF provides end-to-end solutions on HPC systems for artificial intelligence (AI), machine learning (ML) and high performance data-analytics (HPDA) for Max Planck scientists. This ranges from the integration of the infrastructure (such as Jupyter notebooks, remote visualization services, support of Apache Spark, ...) into the HPC system, the provisioning of  HPC optimized software (like various frameworks for deep learning and AI), as well as dedicated high-level project support in close collaboration with MPG research groups. Project support comprises of the consulting on state of the art techniques, of the development and optimization of data analytics (especially deep learning and AI) solutions.

Support

For dedicated project support, please contact A. Marek (HPDA).

Ongoing projects

• MRI Reconstruction with deep learning (MPI for Human Cognitive and Brain Sciences, Leipzig).
• MRI Segmentation using synthetic images (MPI for Human Cognitive and Brain Sciences, Leipzig).
• Cell image segmentation via deep learning networks (MPI for Microstructure Physics, Leipzig).
• 3D dust mapping of the Milky Way (MPI for Astronomy, Heidelberg).
• Solving the laplacian connectivity embedding problem in MRI imaging via the ELPA library (MPI for Human Cognitive and Brain Sciences, Leipzig).
• I/O optimization and parallelization of deep learning applications for material sciences within the BIGmax collaboration (MPI für Eisenforschung, Düsseldorf).

Shortlist of completed projects

• Integration of environmental information into thermal videos for wild animal tracking (Max Planck Institute for Animal Behavior, Konstanz).
• 3D segmentation of histology data (MPI for Human Cognitive and Brain Sciences, Leipzig).
• Deep-prior for denoising of MRI images (MPI for Human Cognitive and Brain Sciences, Leipzig).
• Next generation sequencing at scale (MPI for Biology of Ageing, Cologne).
• 2D and 3D segmentation of cellular structures (BigMax and MPI of Colloids and Interfaces, Potsdam).
• Particle track reconstruction (MPI for Physics, Munich).
• Robust classification of neuro-degenerative diseases (MPI for Human Cognitive and Brain Sciences, Leipzig).
• Dataset creation with a BigGAN (MPI for Human Cognitive and Brain Sciences, Leipzig).
• Optimizing the RoboEM application for synaptic-resolution connectomics (MPI for Brain Research, Frankfurt am Main).

 

People

Members of the AI & HPDA division:

• Dr. Andreas Marek (leader)
• Dr. Timoteo Colnaghi
• Dr. Piero Coronica
• Dr. David Carreto Fidalgo

Members of other divisions working partly for the AI & HPDA division

• Dr. Michele Compostella (System Operations division)
• Dr. Nicolas Fabas (Data Services division)
• Dr. John Alan Kennedy (Data Services division)

Former members of the data-analytics group

• Dr. Giuseppe di Bernardo (Industry)
• Dr. Pavel Kus (University of Praque)
• Dr. Luka Stanisic (Industry)

Talks and Training Material

• High-performance Data Analytics (HPDA) at the MPCDF. M. Rampp & A. Marek. Talk presented at DV-Treffen der Max-Planck-Institute. Göttingen, Germany, Sep 17-19, 2019.
• High-performance Data Analytics: Basic concepts of distributed deep learning. M. Rampp & A. Marek. Invited lecture at the BiGmax Big Data Summer 2019, Platja d'Aro (Spain), Sep 9-13, 2019.

Shortlist of Publications

For a complete list see: Publications by MPCDF

• Z. Rao, Y. Li, H. Zhang, T. Colnaghi, A. Marek, M. Rampp, B. Gault, Direct recognition of crystal structures via three-dimensional convolutional neural networks with high accuracy and tolerance to random displacements and missing atoms, Scripta Materialia 234, 115542 (2023). [Link]
• T.E. Dharmawardena, C.A.L. Bailer-Jones, M. Fouesneau, D. Foreman-Mackey, P. Coronica, T. Colnaghi, T. Müller, J. Henshaw, The three-dimensional structure of galactic molecular cloud complexes out to 2.5 kpc, MNRAS 519,  228–247 (2023). [Link]
• Y. Li, T. Colnaghi , Y. Wei, A. Marek, H. Li, S. Bauer, M. Rampp, L.T. Stephenson, Convolutional neural network-assisted recognition of nanoscale L1₂ ordered structures in face-centred cubic alloys, Npj Comput. Mater. 7, 1-9 (2021). [Link]