Location: Freiburg
| Simon Ging is a PhD candidate at the University of Freiburg, working with Prof. Thomas Brox on vision–language models. His current focus is radiology: getting models to connect what a scan shows with what a radiologist writes about it. Two papers at MICCAI 2026 come out of this line of work: disease-aware pretraining for 3D CT, and a scalable recipe for spatially grounded 2D radiology models. Before medicine, he worked on general-purpose vision–language problems: COOT (NeurIPS 2020) introduced a hierarchical transformer for video-text representation learning; an ICLR 2024 spotlight repurposed classification datasets and their label hierarchies into an open-ended VQA benchmark; a GCPR 2025 oral used knowledge graphs to harvest better CLIP training data. He holds an M.Sc. from Freiburg and, before that, several years of experience as a software developer and SAP consultant. |
Elias Kempf is an ELLIS PhD student in interpretability and AI safety at the University of Freiburg and Oxford, jointly supervised by Thomas Brox and Fazl Barez. His research studies how learned model behaviors emerge, generalize, and transfer, with a focus on safety-relevant failure modes. Recent work spans domain and compositional generalization in vision-language models, hidden-bias transfer via subliminal learning, and black-box model diffing for comparing language model behaviors. His master’s thesis on CLIP’s domain and compositional generalization received the Förderpreis Künstliche Intelligenz and led to a Spotlight paper at ICML 2025. He has also been a MATS Research Fellow, mentored by Arthur Conmy (prev. Google DeepMind), and previously worked on deep learning for radiotherapy dosimetry at PTW Freiburg.
I am a Research Scientist at Prior Labs, where I develop the next generation of tabular foundation models. My work focuses on scalable pretraining, novel architectures, and scaling laws. I contribute to TabPFN-3, which scales tabular foundation models to datasets with up to one million training rows while substantially accelerating training and inference.
Previously, I was a Senior AI Researcher in Aleph Alpha Research’s Foundation Models team, working on efficient LLM pretraining, tokenizer-free architectures, and large-scale optimization. Before that, I was a Senior Expert at the Bosch Center for Artificial Intelligence, where my research centered on robust and reliable computer vision, neural architecture search, and synthetic data.
My broader interests span efficient and reliable machine learning, AutoML, and open-source software. I am an ELLIS member, an ELIZA Industrial Fellow, and an Area Chair for NeurIPS 2026. I have also contributed to scikit-learn, including its probability-calibration, kernel-ridge, and Gaussian-process modules.
I live in Böblingen and work in Freiburg. Outside research, I enjoy spending time with my wife and our three children. I am always happy to connect with people working on tabular foundation models, scalable pretraining, efficient or tokenizer-free architectures, and open-source machine learning.
I am a PhD candidate at the University of Freiburg working with Thomas Brox. I also hold a parallel position at Bosch Research in Renningen, Germany. My work deals with understanding representation learning in visual models through the lens of interpretability and explainability.
I did my Master’s at RIT, where I was an NSF AWARE-AI Fellow. I also worked at the RIT Center for Human Aware AI under the supervision of Prof. Andreas Savakis on unsupervised domain adaptation in large-scale foundation models.
I have had the pleasure of working alongside researchers and scientists from across the world. Prior to this, I have been associated with Microsoft Research, the Juno Mission at NASA JPL, IRIS-HEP at Princeton, the LIVIA Lab at ETS Montreal, ULR France, CamCann Smart Systems, and IIT Indore.
| I am a Master’s student in Computer Science with a specialization in Artificial Intelligence at the University of Freiburg. My research interests span Computer Vision and Natural Language Processing, with a particular focus on synthetic data generation and personally identifiable information (PII) dataset creation. Previously, I conducted research at the Max Planck Institute for Biological Intelligence, where I completed my bachelor’s thesis on multi-camera object detection and tracking. I have also contributed to medical image analysis and processing as a Research Assistant in Hungary. Through these experiences, I have developed a strong foundation in applied machine learning, bridging theoretical AI research with real-world computer vision and data-centric challenges. I remain actively interested in synthetic data generation and its implications for privacy-aware AI development. |
| Luiz Capistrano is an M.Sc. student in Embedded Systems Engineering (AI specialization) at the University of Freiburg. He is currently a Student Researcher at the Robot Learning Lab, working on Visuo-Tactile World Models for Contact-Rich Manipulation with Force-Array Tactile Sensing. His research focuses on tactile sensing, sim-to-real transfer, and model-based reinforcement learning for dexterous robotic manipulation. Prior to academia, he spent seven years as a credit and equity research analyst at Itaú BBA, Brazil’s leading investment bank, where he led research coverage across multiple sectors, developing strong quantitative and analytical foundations. He holds a B.Sc. in Engineering Physics from the University of São Paulo (2018). His long-term research vision centers on advancing autonomous robotic systems capable of generalizing across diverse tasks and environments — bridging tactile perception, world models, and model-based RL toward robots that can robustly interact with the physical world. |
| I am an AI Master’s student at the University of Freiburg and an ELIZA scholarship recipient. I previously earned a Bachelor’s degree from the National University of Sciences and Technology, where I developed DisasterWatch, an AI-driven web service that analyzes satellite imagery to assist first responders with real-time disaster damage assessments. After graduation, I applied machine learning to medical diagnostics, focusing on major depressive disorder detection, which resulted in my first research publication. These experiences in high-stakes domains highlighted the critical need for explainability and fairness in AI. Consequently, my current research interests lie in explainable AI, bias mitigation, and trustworthy machine learning, and I hope to contribute to building AI systems that are both equitable and understandable. |
| I’m an ELIZA fellow and Master’s student in Computer Science with a specialization in Artificial Intelligence at the University of Freiburg. My current research interests lie in the foundations of machine learning, optimization theory, and the application of machine learning in medicine. During a research internship at Fraunhofer ITWM, I worked on deepfake detection and published a first-author paper at the ECCV 2024 workshop, *“Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets”*, which investigates biases in current deepfake detection benchmarks. Alongside my academic work, I contributed to autonomous industrial crane systems at PSIORI and completed my Bachelor’s thesis on accelerating computer vision models for real-time applications. In 2025, I was awarded the “Best Bachelor Graduate” award by the EDEKA IT Foundation. |
I am a Master’s student in Computer Science specializing in Artificial Intelligence at the University of Freiburg. My foundational research experience began during my undergraduate studies, where I designed and developed an end-to-end patent retrieval system from natural language queries. This project included work in patent classification, resulting in a paper that I presented at the SIU 2025 conference.
Since starting my Master’s, my focus has shifted toward the theoretical and algorithmic foundations of machine learning. Through my graduate coursework and personal research interests, I am engaged with deep reinforcement learning, optimization algorithms, and foundation models. As an incoming ELIZA Scholar, I look forward to translating this theoretical groundwork into impactful research within the program.