I am a Principal Research Scientist and research manager SAP AI CTO Office. 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 the Technical University of Munich (TUM) at the intersection of medical imaging and machine learning (raw ultrasound data processing for applications such as early detection of Parkinson’s disease), advised by Nassir Navab.

I am a member of the European Laboratory for Learning and Intelligent Systems (ELLIS).

Research

My current research interests lie in natural language processing (NLP) and the intersection with computer vision:

• Learning with minimal supervision (few-shot learning, self-supervised learning)
• Foundation models for structured data (table representation learning)
• LLM alignment (integration of knowledge-graphs)
• Multi-modal LLMs (advanced reasoning with LLMs, knowledge-grounded LLMs)

Research

I am currently looking for interns. Please reach out to me on LinkedIn. Potential topics are related to the following topics.

• Table representation learning
• Neurosymbolic reasoning
• Diffusion models for text/tables

News

[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

[last update: 10/25/2024]