Location: Darmstadt
| I am a PhD student in Multimodal Artificial Intelligence at TU Darmstadt under the supervision of Marcus and Anna Rohrbach and an ELLIS PhD candidate. My research focuses on trustworthy AI, with particular interests in adversarial robustness, AI security, multimodal learning, and vulnerabilities of unified and agent-based systems. I study how modern AI models can be manipulated and how such weaknesses can be detected and mitigated before deployment. Alongside my research, I lead teaching activities for the Statistical Machine Learning course at TU Darmstadt, one of the university’s largest machine learning courses with more than 500 students, and supervise student research projects. Before my PhD, I worked as an independent ML engineer and conducted more than 100 AI workshops across academia and industry. My most influential recent work is GEM: Geometric Erasure by Contrastive Velocity Matching in Rectified Flows (Grebe, Braun, Rohrbach & Rohrbach, ICML 2026 Spotlight), which introduces a novel geometric framework for robust concept erasure in generative models. I was also recognized as an ICML 2026 Gold Reviewer for outstanding reviewing contributions. |
| Baraa Hikal is an M.Sc. student in Artificial Intelligence and Machine Learning at TU Darmstadt, an ELIZA scholarship holder, and a recipient of the Niedersachsenstipendium. He is currently a research assistant at the UKP Lab, working with Prof. Iryna Gurevych and Jonathan Tonglet on multimodal chronolocation, automated fact-checking, and planning agents that combine visual reasoning with external evidence retrieval. Previously, he worked at GippLab on dimensional sentiment analysis and deepfake analysis, and at Qatar University on IRAC-based legal summarization. His research focuses on trustworthy and agentic AI, multimodal reasoning, hallucination detection, and factuality evaluation. His bachelor’s thesis focused on hallucination detection in language models. He has achieved first-place results in shared tasks affiliated with ACL and EMNLP, including SemEval 2025, SemEval 2026, BEA 2025, and ArabicNLP 2025, and received the Best Paper Award at BEA 2025. |
Principal Scientist at Merck Healthcare, part of Merck KGaA, Darmstadt.
Dr. sc ETH in Pharmaceutical Sciences, ETH Zurich.
Bachelor & Master in Chemistry at ETH Zurich, Switzerland.
Interested in AI for Drug Discovery and Development.
Hi! The ‘3D Graphics & Vision’ group at TU Darmstadt works at the intersection of computer graphics, computer vision and machine learning. Specifically, we are interested in marker-less motion capturing of facial performances, human bodies as well as general non-rigid objects. Besides capturing and reconstructing reality, we work on AI-based synthesis techniques that allow for photorealistic image and video synthesis.
Simone Schaub-Meyer is an assistant professor and leads the research group Image and Video Analysis at the Technical University of Darmstadt. She is an ELLIS member and a member of the Hessian Center for Artificial Intelligence (hessian.AI). The focus of her research is on developing efficient, robust, and understandable methods and algorithms for image and video analysis. She received the renowned Emmy Noether Programme (ENP) grant from the German Research Foundation (DFG), supporting her research on Interpretable Neural Networks for Dense Image and Video Analysis. She obtained her doctoral degree from ETH Zurich, advised by Prof. Dr. Markus Gross and in collaboration with Disney Research Zurich. Her doctoral thesis was awarded the ETH Medal.
I am currently pursuing my Master’s degree in Informatik at TU Darmstadt. Additionally, I work as a research assistant at the Intelligent Autonomous Systems Lab at TU Darmstadt, with a focus on humanoid locomotion. My past research experience has mainly focused on reinforcement learning, including an internship at Porsche Motorsport. In addition to robot learning, my current research interests include robotics in complex and uncertain environments.
Marc Saghir is a master’s student in Artificial Intelligence and Machine Learning at TU Darmstadt, after concluding his bachelor in Cognitive Science. He currently works as a student research assistant (HiWi) at the Remote Sensing Institute of TU Darmstadt, contributing to ML projects like landslide detection or sustainability evaluation of cities. Prior to his graduate studies, he gained industry experience working as a Data Scientist and Machine Learning Engineer, where he developed machine learning solutions for real-world applications. His research interests lie broadly in computer science and machine learning, with a particular focus on transdisciplinary applications. He is especially interested in exploring how AI methods can be applied to fields such as environmental science, where data-driven approaches can support the analysis of complex environmental systems. Through his graduate studies and research work, he aims to deepen his machine learning knowledge and contribute to applications of AI for scientific challenges.
Leonie Schüßler is a master’s student in Artificial Intelligence and Machine Learning at TU Darmstadt and recently joined the ELIZA project. She holds a bachelor’s degree in Cognitive Science, which builds her interdisciplinary perspective on AI by bridging fields such as neuroscience, psychology, linguistics, and computer science. Her academic interests include computer vision and deep learning, and she is particularly interested in how these methods can be applied across various domains, including but not limited to medical imaging. Through her graduate studies, she aims to deepen her expertise and contribute to impactful AI research and solutions that address real-world challenges.
Sebastian is a master’s student in Artificial Intelligence and Machine Learning at TU Darmstadt and joined the Zuse School ELIZA in April 2025 as part of its master’s scholarship program, following the completion of his bachelor in Cognitive Science. His research focuses on computer vision, particularly object segmentation and representation learning, as well as combining the visual and linguistic domain in multi-modal models for complex tasks such as visual question answering. He is also fascinated by the intersection of AI systems and human cognition in context of the information processing theory, including the computational modelling of human eye movements to gain insight into human decision-making processes. He aims to consolidate his knowledge through his studies and intends to pursue a PhD to contribute meaningful research to contemporary challenges in computer vision.