Tassilo J. Klein, Ph.D.

Principal Research Scientist & Research Manager

📍 SAP AI CTO Office

💡 LLMs, NLP, Structured Data AI

🧑‍💻 About Me

I am a Principal Research Scientist and Research Manager in the SAP AI CTO Office, working on Natural Language Processing (NLP), large language models (LLMs), and machine learning for enterprise structured data.
My work spans from advancing foundational AI techniques to delivering enterprise-ready systems — including knowledge-augmented LLMs, privacy-preserving AI, and intelligent agents for complex workflows.

Previously, I was a postdoctoral research fellow at Harvard Medical School and Brigham & Women’s Hospital in Boston, and a postdoctoral research associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT.
I earned my Ph.D. from the Technical University of Munich (TUM) at the intersection of medical imaging and machine learning.

Member of the European Laboratory for Learning and Intelligent Systems (ELLIS).

🎯 Research Focus

Large language models (LLMs) and NLP; representation learning for structured/tabular data; few-shot & self-supervised learning; multi-modal AI; intelligent agents

📄 Selected Publications & Projects

[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

🤝 Community & Mentorship

Reviewer for ACL, EMNLP, CVPR, and related workshops
Mentor to 15+ students and interns (alumni in academia and industry)
Current interests: table representation learning, agentic AI systems

_{Last updated — 14 August 2025}