Tassilo J. Klein, Ph.D. — AI Research Scientist

I am a Principal Research Scientist and research manager in the SAP AI CTO Office, working on Natural Language Processing (NLP) and machine learning for enterprise structured data.

Before joining SAP, I was a postdoctoral fellow at Harvard Medical School and MIT CSAIL, where I studied large‑scale optimisation for genetically driven imaging biomarkers. I completed my Ph.D. at the Technical University of Munich (TUM) on raw‑ultrasound signal processing for early disease detection.

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

Research Focus

Natural Language Processing and large language models
Table representation learning
Contrastive and self‑supervised learning
Medical imaging and computational biology

Selected publications and projects

[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

[2024.01] - Pre-print available on Contrastive Perplexity for Controlled Generation: An Application in Detoxifying Large Language Models

[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 and mentorship

Reviewer for ACL, EMNLP, CVPR and related workshops. I supervise students and interns (15 alumni to date). Current interests: table representation learning, neuro‑symbolic reasoning, and diffusion models for text and tables.

_{Last updated — 16 May 2025}