Tassilo Klein, Ph.D.

tjk

LinkedIn

Google scholar

GitHub

CV

Bio

Currently, I am Director of AI Research at SAP SE in Berlin, Germany. Prior to joining SAP, I was a postdoctoral research fellow at Harvard Medical School, Brigham & Women’s Hospital, Boston, in the group of Sandy Wells. At the same time, I was a postdoctoral research associate in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT, working with the group of Polina Goland. During that time I was conducting research on large-scale machine learning and optimization technologies for discriminative pattern discovery of genetically driven imaging biomarkers. I obtained my Ph.D. from Technical University of Munich (TUM) at the intersection of medical imaging and machine learning (raw ultraound data processing for applications such as early detection of Parkinson’s disease), advised by Nassir Navab.

Research

My research interests lie at the intersection of machine learning, computer vision, and natural language processing. Although not my current main focus, I am very much interested in machine learning in the medical domain and hope to pursue this further at some point.

Internship Position: Our research team is continuously hiring students and interns. If you’re a Ph.D. student interested in a research internship working in Berlin, please send an email with your CV and research interests. Right now, we are particularly looking for interns for medical imaging (self-supervised learning), NLP (commonsense reasoning), and privacy-preserving ML. Check out our research team’s blog to learn more about current research activity there.

News

[2021.08] Two papers accepted at EMNLP 2021 on Contrastive Self-Supervised Learning for Commonsense Reasoning - arXiv, code and arXiv, [code] (https://github.com/SAP-samples/emnlp2021-attention-contrastive-learning/)

[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: video, blog.

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

[2020.02] Paper accepted at NeuroImage - link

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

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

[2019.02.25] Paper accepted at CVPR 2019 (acceptance rate 25.2%) -link, code

[2017.02.01] Paper accept at NeuroImage - arXiv, code

[last update: 09/15/2021]