Big Data Engineering
Prof. Dr. Matthias Böhm
Technische Universität Berlin
Ernst-Reuter-Platz 7, 10587 Berlin
Data Science Abstractions and Systems, Performance-Accuracy Tradeoffs in Data Science, Data Cleaning Pipelines and Optimization
The mission of the Big Data Engineering group, led by Prof. Dr. Matthias Böhm, is to simplify data science by providing high-level, data-science-centric abstractions and building systems and tools to execute these tasks in an efficient and scalable manner. The general research interests include the exploration of performance-accuracy tradeoffs, tooling (script generators, label generation, advisors, etc.), seamless data augmentation, cleaning, feature engineering, model debugging and deployment, cost-effective cloud deployments, advanced optimization techniques, adaptive data storage and indexing, and the exploitation of modern hardware.
Current research focuses on:
• Data Cleaning Pipelines: Automatic enumeration of data cleaning pipelines for target ML application, hyper parameter optimization of cleaning primitives.
• Model Debugging: Finding the top-k data slices where a trained model underperforms, linear-algebra-based enumeration and pruning algorithms.
• Fine-grained Lineage Tracing and Reuse: Fine-grained, multi-level lineage tracing for versioning and reuse, lineage deduplication, full and partial reuse of intermediates.
• Federated Linear Algebra and Parameter Servers: ML model training on federated raw data without central data consolidation, plan generation under awareness of privacy constraints, federated linear algebra programs and parameter servers.
• Workload-aware Data Reorganization: Compression under awareness of data and workload (linear algebra program) characteristics, asynchronous data reorganization in standing executors (e.g., at standing federated workers).
• Code Generation for Heterogeneous HW: Extended operator fusion and code generation for GPUs and heterogeneous devices, including sparsity exploitation across operations.
Matthias Boehm, Madelon Hulsebos, Shreya Shankar, Paroma Varma
Matthias Boehm, Matteo Interlandi, Chris Jermaine
Saeed Fathollahzadeh, Matthias Boehm
Sebastian Baunsgaard, Matthias Boehm
At its New Year's reception BIFOLD welcomed a series of distinguished guests and friends from Berlin's AI community.
On October 9 and 10, 2023, BIFOLD welcomed the other Geman AI centers (ScaDS.AI Dresden/Leipzig, Lamarr Institute, Tübingen AI Center, MCML, and the DFKI) in Berlin. The annual meeting featured guests, partners, visitors, and researchers from all over Germany.
On October 9th and 10th, 2023, the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin invited scientists from the university AI competence centers (BIFOLD, ScaDS.AI Dresden/Leipzig, Lamarr Institute, Tübingen AI Center, and MCML) and the DFKI to Berlin to present and discuss the latest results of their research on the EUREF campus.
Research Group Lead
Secretary ML Sec & DAMS