A Generative Foundation Model for Heterogeneous Tabular Data
Director of ML Research · Principal Scientist · SAP AI
Foundation models, agents, structured data, generative UI
Tassilo J. Klein, Ph.D.
Research leadership in foundation models, enterprise data, and applied AI systems.
My work connects machine learning research with deployed AI systems, from representation learning and foundation models for structured data to agentic workflows and AI-native interfaces.
About
I lead ML research at the SAP AI CTO Office, where my team works on foundation models for enterprise data. As a scientific advisor to startups, I also work on agentic AI systems and generative UI. My research spans the full stack: from representation learning and self-supervised pre-training to autonomous agent runtimes and AI-native interface generation.
I’m particularly interested in the coordination economics of agentic AI — how protocol-mediated agent systems change the cost structure of enterprise software, and what that implies for how we build and deploy AI at scale.
Previously: postdoc at MIT CSAIL and Harvard Medical School (representation learning, multimodal medical imaging). Ph.D. summa cum laude from TU Munich. Member of ELLIS.
Selected Impact
- Lead ML research in the SAP AI CTO Office on foundation models for enterprise data
- Advise startups on agentic systems and generative UI as a scientific advisor
- Recent work accepted at ACL, SIGMOD workshops, NeurIPS TRL, and ICML FMSD
- Built and released datasets, benchmarks, and models for enterprise-scale structured data
- Mentored 15+ researchers and Ph.D. students now at top labs and faculty-track roles
Research
| Area | Focus |
|---|---|
| Agentic AI | Autonomous agent runtimes, MCP-based tool calling, multi-agent orchestration, self-growing tool libraries |
| Foundation Models for Structured Data | Tabular pre-training, relational reasoning, enterprise-scale data representation |
| generative UI | AI-driven dynamic interface generation, adaptive UX, intent-to-UI synthesis |
| Knowledge-Augmented LLMs | RAG, privacy-preserving fine-tuning, domain adaptation |
| Representation Learning | Contrastive and self-supervised methods, multimodal alignment |
Selected Publications & Projects
2026
TableFactory: Generating Semantically Linked Tabular Data via Multi-Agent Behavioral Simulation
Statistically Indistinguishable, Operationally Distinct: A Formal Barrier for Tabular Foundation Models
Tabular Foundation Model for Generative Modelling
The Headless Firm: How AI Reshapes Enterprise Boundaries
2025
SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables
Table Dissolution: Adding Salt To Your Data
SALT integrated into RelBench
Foundation Models for Tabular Data in Enterprises
Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Model
2024
SALT: Sales Autocompletion Linked Business Tables Dataset
PORTAL: Scalable Tabular Foundation Models via Content-Specific Tokenization
2023
Low-shot contrastive learning of sentence representations
2022
Self-supervised sentence representation learning
2021
Contrastive Language Model Refinement for Commonsense Reasoning
Contrastive Self-Supervised Learning for Commonsense Reasoning
Co-organizer: Self-Supervised Learning for Reasoning and Perception
Self-supervised representation learning for medical imaging
2020
Commonsense reasoning in AI
Contrastive self-supervised commonsense reasoning
Representation learning for medical imaging
2019
Multi-Domain Learning
Commonsense reasoning
Deep generative models for visual learning
2017
DeepNAT
Service & Mentorship
- Program committee / reviewer: ACL, EMNLP, CVPR, NeurIPS
- Mentored 15+ researchers and Ph.D. students across academia and industry
- Co-organizer: ICML 2021 Workshop on Self-Supervised Learning for Reasoning and Perception
Alumni
Former interns who have gone on to research roles at top labs and faculty positions:
-
Enrico Fini — Member of Technical Staff at Microsoft AI (previously Research Scientist at Apple Machine Learning Research)
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Stefan Lionar — Ph.D. Student in Computer Science at National University of Singapore (NUS) & Sea AI Lab
-
Jan Nikolas Morshuis — Doctoral Researcher at the University of Tübingen (Cluster of Excellence “Machine Learning” / Tübingen AI Center)
-
Aiham Taleb — Senior Applied Scientist – Generative AI at Amazon Web Services (AWS) / Generative AI Innovation Center
-
Artur Speiser — AI Research Scientist at Exscientia, formerly Ph.D. in Machine Learning in Science at University of Tübingen
-
Jannik Wolff — Machine Learning Research Scientist & Research Associate at BIFOLD, Ph.D. Candidate at TU Berlin (Machine Learning Group)
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Daniel Dorda — Scientific Software Engineer at uniqFEED and researcher/teaching assistant at ETH Zürich (Computer Graphics / Computer Science)
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Max Bain — Research Scientist at Google DeepMind, previously Member of Technical Staff at Reka and Ph.D. at VGG, University of Oxford
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Mahdyar Ravanbakhsh — (Principal) Research Scientist at Zalando SE in Berlin
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Oleksiy Ostapenko — Research Scientist at ServiceNow AI Research (Foundation Models Lab); Ph.D. from MILA / Université de Montréal
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Colin Samplawski — Ph.D. Student at UMass Amherst (REML group) and Advanced Computer Scientist at SRI International (NuSCI Research Group)
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Mihai M. Puscas — Computer Vision Researcher at SPORTTOTAL.TV (previously Huawei Research, Dublin)
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Sandro Pezzelle — Assistant Professor in Responsible AI at ILLC, University of Amsterdam; Scientific Advisor at IVADO Labs
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Frederik Pahde — Research Associate / Ph.D. Candidate at Fraunhofer HHI & TU Berlin (previously Applied ML Scientist at Amazon)
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Shailza Jolly — Research Scientist at Amazon Alexa AI in Berlin (Ph.D. in Generative AI from TU Kaiserslautern / DFKI)
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Denis Dushi — Machine Learning Engineer at Amazon
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Vadim Tschernezki — Ph.D. Student in Computer Vision at the Department of Engineering Science, University of Oxford (VGG)
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Robin C. Geyer — Ph.D. Student at the Institute for Machine Learning, ETH Zürich
Last updated — July 2026