Tassilo J. Klein, Ph.D.

Director of Machine Learning Research & Principal Scientist

📍 SAP AI CTO Office | 💡 LLMs, Foundation Models, Structured Data AI
🌐 LinkedIn • Google Scholar

Building intelligent systems that connect language, knowledge, and enterprise data.

🧠 About Me

I am Director of Machine Learning Research & Principal Scientist in the SAP AI CTO Office, where I lead research on large language models (LLMs), foundation models, and structured data AI.
My mission is to advance how enterprises leverage AI — by designing systems that can reason over complex structured data, adapt to limited supervision, and integrate domain-specific knowledge with the general capabilities of LLMs.

I oversee strategic research programs that bridge fundamental machine learning and enterprise-scale deployment, turning scientific innovation into robust, trustworthy AI platforms.
This includes developing foundation models for structured and relational data, knowledge-augmented LLMs, and agentic AI systems that coordinate decision-making and automation across business processes.

Over the past decade, I’ve built and led multidisciplinary research teams translating state-of-the-art methods into product-ready technologies — shaping how AI is applied in large-scale enterprise systems.
Before joining SAP, I conducted postdoctoral research at MIT CSAIL and Harvard Medical School, focusing on representation learning and multimodal modeling for medical imaging.
I hold a Ph.D. in Computer Science (summa cum laude) from the Technical University of Munich (TUM) and am a member of the European Laboratory for Learning and Intelligent Systems (ELLIS).

🔬 Research Focus

Foundation models for structured/tabular data
Knowledge-augmented and privacy-preserving LLMs
Representation learning and contrastive methods
Intelligent and agentic systems for enterprise automation

📄 Selected Publications & Projects

[2026.03] - Released MCPRuntime — An open-source, high-performance local runtime for autonomous agents implementing programmatic tool calling and self-growing tool libraries.

[2025.11] - A paper accepted (spotlight) at the EurIPS’25 Workshop on AI for Tabular Data

SALT-KG: A Benchmark for Semantics-Aware Learning on Enterprise Tables

[2025.07] - A paper accepted at the SIGMOD’25 Workshop on Data Management for End-to-End Machine Learning

Table Dissolution: Adding Salt To Your Data

[2025.07] - SALT officially integrated into RelBench.

[2025.05] - New pre-print available on foundation models for tabular data in enterprises

[2025.05] - Paper accepted at ACL 2025 — Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Model

[2024.10] - Two papers accepted at the NeurIPS’24 Table Representation Learning Workshop

SALT: Sales Autocompletion Linked Business Tables Dataset - pre-print
PORTAL: Scalable Tabular Foundation Models via Content-Specific Tokenization - pre-print

[2023.05] - Paper accepted at ACL 2023 on low-shot contrastive learning of sentence representations.

[2022.02] Paper accepted at ACL 2022 on self-supervised sentence representation learning

[2021.08] Paper at EMNLP 2021 on Contrastive Language Model Refinement for Commonsense Reasoning

[2021.08] Paper at EMNLP 2021 on Contrastive Self-Supervised Learning for Commonsense Reasoning

[2021.04] Acceptance of co-organized at ICML 2021 workshop on Self-Supervised Learning for Reasoning and Perception

[2021.02] Paper accepted at IPMI 2021 on self-supervised representation learning for medical imaging (acceptance rate 30.0%)

[2020.09] Presentation on commonsense reasoning in AI

[2020.04] Paper accepted at ACL 2020 on contrastive self-supervised commonsense reasoning (acceptance rate of 17.6%)

[2020.02] Paper accepted at NeuroImage

[2019.10.20] Paper on Multi-Domain Learning accepted at ICCV 2019 (acceptance rate 25.0%)

[2019.05.14] Short-paper on commonsense reasoning accepted at ACL 2019 (acceptance rate 18.2%)

[2019.02.25] Paper accepted at CVPR 2019 (acceptance rate 25.2%)

[2017.02.01] Paper accepted at NeuroImage

🧭 Vision & Collaboration

I aim to advance AI systems that reason over structured data, adapt efficiently, and integrate domain knowledge to support enterprise decision-making.
At SAP, I drive the interface between fundamental AI research and strategic enterprise transformation, shaping how LLMs evolve for real-world impact.

🤝 Community & Mentorship

Reviewer for ACL, EMNLP, CVPR, and NeurIPS
Mentor to 15+ researchers and Ph.D. students
Co-organizer: ICML 2021 Workshop on Self-Supervised Learning for Reasoning and Perception

👩‍🔬 Former Interns & Where They Are Now

I’ve had the privilege of mentoring exceptional research interns who have since moved on to leading academic labs and industrial AI teams:

Enrico Fini — Member of Technical Staff at Microsoft AI (previously Research Scientist at Apple Machine Learning Research)
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)
Daniel Dorda — Scientific Software Engineer at uniqFEED and researcher/teaching assistant at ETH Zürich (Computer Graphics / Computer Science)
Max Bain — Research Scientist at Google DeepMind, previously Member of Technical Staff at Reka and Ph.D. at VGG, University of Oxford
Mahdyar Ravanbakhsh — (Principal) Research Scientist at Zalando SE in Berlin
Oleksiy Ostapenko — Research Scientist at ServiceNow AI Research (Foundation Models Lab); Ph.D. from MILA / Université de Montréal
Colin Samplawski — Ph.D. Student at UMass Amherst (REML group) and Advanced Computer Scientist at SRI International (NuSCI Research Group)
Mihai M. Puscas — Computer Vision Researcher at SPORTTOTAL.TV (previously Huawei Research, Dublin)
Sandro Pezzelle — Assistant Professor in Responsible AI at ILLC, University of Amsterdam; Scientific Advisor at IVADO Labs
Frederik Pahde — Research Associate / Ph.D. Candidate at Fraunhofer HHI & TU Berlin (previously Applied ML Scientist at Amazon)
Shailza Jolly — Research Scientist at Amazon Alexa AI in Berlin (Ph.D. in Generative AI from TU Kaiserslautern / DFKI)
Denis Dushi — Machine Learning Engineer at Amazon
Vadim Tschernezki — Ph.D. Student in Computer Vision at the Department of Engineering Science, University of Oxford (VGG)
Robin C. Geyer — Ph.D. Student at the Institute for Machine Learning, ETH Zürich

_{Status to the best of my knowledge as of November 2025.}

_{Last updated — 4 March 2026}