Senior Research Engineer @ SAP Business AI. I build AI systems that turn complex enterprise data into usable knowledge—from learned semantic models to production applications.
AI that understands enterprise data
My work sits at the intersection of AI, data systems, and knowledge representation. I develop systems that discover structure and meaning in complex enterprise data by combining LLMs, representation learning, human feedback, and symbolic methods. The result is reliable context for AI: semantic data layers, ontologies, and knowledge graphs learned from data rather than modelled entirely by hand.
I completed my PhD at the Hasso Plattner Institute in close cooperation with SAP SE, researching AI methods for ontology learning and data integration, with lead-author work at VLDB and SIGMOD. At SAP Business AI, I now translate this research and broader applied-AI experience into enterprise-scale systems.
My practical experience extends beyond knowledge graphs: industry engineering since 2018, from building a global Industrial IoT platform at Siemens Energy to applying computer vision to wildlife conservation in Rwanda.
AI research, engineered for practice
Senior Research Engineer
Developing AI systems for enterprise data, with a focus on semantic grounding, ontology learning, and knowledge graphs—and translating research into scalable product capabilities.
Doctoral Researcher
Research at the intersection of AI, databases, and knowledge representation, developing learning-based methods for ontology construction and data integration in close cooperation with SAP SE. Lead-author publications at VLDB and SIGMOD.
Visiting Researcher
Human-in-the-loop ontology learning with Prof. Padhraic Smyth, combining databases, Semantic Web and AI methods.
Technical Advisor & AI Engineer
Consulting on wildlife re-identification AI for a species-conservation startup based in Rwanda.
Software Engineer (Working Student)
Primary owner of the architecture and development of the automated edge-provisioning solution for the global "Connected Factory" IIoT platform, benefiting 80+ factories; full-stack applications on AWS; technical management of external staff in Europe and India. Recognised as a successful digitalisation project.
Cloud Operations Engineer (Internship)
Service administration on the SAP Cloud platform and automated deployment pipelines.
Selected publications
Hamilton: A Human-in-the-Loop Ontology Learning System for Relational Databases
Bringing expert feedback into the ontology-learning loop, building on the research visit at UC Irvine.
Schuyler: Self-Supervised Clustering of Tables in Relational Databases
Clusters database tables by combining structural and semantic signals via triplet-loss fine-tuning of an LLM, with state-of-the-art results (+0.13 ARI, +0.10 AMI) on a new five-database benchmark.
Burr: A Benchmark for Ontology Learning from Relational Databases
54 real-world and synthetic scenarios plus a novel mapping-based metric, showing that rule-based methods still lead while LLM-based approaches hold substantial promise.
Learning Conditional Marked Event Sequences with Mixed Data Types
An intensity-free, Transformer-based marked temporal point process that jointly models event times and mixed-type marks with normalizing flows. With UC Irvine; Spotlight, top 3% of submissions.
Along the way
NeurIPS Spotlight (~top 3%)
Our MTPP paper with UC Irvine was selected as a Spotlight.
Talk at MIT
On entity resolution on numerical data and ontology engineering.
Research visit, UC Irvine
Human-in-the-loop ontology learning with Prof. Padhraic Smyth.
Paper talk at VLDB, Sydney
Presented Frost at the 48th VLDB conference.
Young Volunteer of the Year, Berlin
For board work as vice chair of the tennis department of VfB Hermsdorf (250+ members).
SAP Signavio Knowledge Club
On integrating enterprise data into a unified knowledge graph.
Let's talk
Enterprise AI, machine learning for data systems, ontology learning, knowledge graphs, or AI for conservation? I'm happy to hear from you.
lukas.laskowski@gmx.de