Focus area: Foundations of ML: Natural Language Processing
| 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.
| Bhavyajeet Singh is a PhD student and doctoral researcher at the Ubiquitous Knowledge Processing Lab, Technical University of Darmstadt, advised by Prof. Iryna Gurevych. His research focuses on developing NLP methods for cybersecurity applications like penetration testing and secure code generation.
Previously, he was a Research Fellow at Microsoft Research India, where he worked on low-latency memory-augmented retrieval systems. This work has been published at ICML 2026 and was integrated into Bing Ads production pipelines. His broader research experience spans secure code generation, retrieval-augmented and memory-augmented NLP systems, and cross-lingual fact-to-text generation. One of his most influential earlier publications is XAlign: Cross-lingual Fact-to-Text Alignment and Generation for Low-Resource Languages, Tushar Abhishek, Shivprasad Sagare, Bhavyajeet Singh, Anubhav Sharma, Manish Gupta, and Vasudeva Varma, 2022. The work introduced a large-scale cross-lingual fact-to-text dataset with 0.45M pairs across eight low-resource languages, supporting research on grounded multilingual generation and cross-lingual alignment. The paper has gathered numerous citations, with the dataset being used my multiple downstream projects and models. |
I am a Professor of Computational Linguistics in the Department of Language Science and Technology at Saarland University. I am also a Professor by courtesy of Computer Science and Associate Faculty at the Max Planck Institute for Informatics.
I lead the Computational Linguistics group and am the speaker of the Research Training Group “Neuroexplicit Models of Language, Vision, and Action”. I am an ACL Fellow.
My PhD is in Computer Science from Saarland University; my advisors were Manfred Pinkal and Gert Smolka. Before coming back to Saarbrücken, I was a postdoc at Columbia University and the University of Edinburgh and a Professor of Theoretical Computational Linguistics at the University of Potsdam. I have spent sabbaticals at Meta AI in Paris and the Allen Institute for Artificial Intelligence (AI2) in Seattle.
I am a huge fan of Typst and try to do as much of my writing as possible in Typst rather than LaTeX. I maintain the tracl template for *ACL papers and the Pergamon bibliography package. I also maintain the Typstit tool for dragging Typst formulas into other apps. In my spare time, I am a certified coach for a cappella groups and was an internationally certified contest judge in the Musicality category from 2023-2026.
Volker Tresp is a professor at Ludwig Maximilian University of Munich (LMU). He received his Diploma degree in physics from the University of Göttingen in 1984 and M.Sc., M.Phil. and Ph.D. degrees from Yale University in 1986 and 1989, respectively. During his Ph.D., he worked in Yale’s Image Processing and Analysis Group (IPAG). In 1990, he joined Siemens where he has been heading various research teams in machine learning. In 1997, he became Siemens Inventor of the Year for his innovations in neural networks research and in 2018 became the first Siemens Distinguished Research Scientist. He revolutionized steel processing by pioneering a novel Bayesian neural network approach that cleverly integrated real-world data with simulated data from a prior solution.*** In 1994 he was a visiting scientist at the Massachusetts Institute of Technology in the Center for Biological and Computational Learning, working with the teams of Tomaso Poggio and Michael I. Jordan. He was co-editor of Advances in Neural Information Processing Systems 13. In 2011, he was appointed professor in informatics at the LMU, where he teaches a course on machine learning and where he is leading a second research team. He is known for his work on Bayesian machine learning, in particular the Bayesian Committee Machine and his work on hierarchical learning with Gaussian processes. The IHRM, the SRM, SUNS, and RESCAL are milestones in representation learning for multi-relational graphs. His team has been doing pioneering work on machine learning with knowledge graphs, temporal knowledge graphs, and scene graph analysis. The work on the Tensor Brain reflects his interest in mathematical models for cognition and neuroscience. In 2020, he became a Fellow of the European Laboratory for Learning and Intelligent Systems (ELLIS). As co-director (with Kristian Kersting and Paolo Frasconi), he leads the ELLIS program “Semantic, Symbolic and Interpretable Machine Learning”.
My research is at the intersection of Machine Learning, Natural Language Processing, and Neuroscience, with a focus on building computational models of language processing in the brain that can also improve natural language processing systems.
I am full professor and chair for AI and Computational Linguistics at LMU Munich, Head of the Munich AI and NLP (MaiNLP) lab, and co-director of the Center for Information and Language Processing (CIS). I am also a visiting full professor at the IT University of Copenhagen.
Dr. Kuldeep Singh is the CEO and Co-founder of Eka Labs AI, a deeptech startup building hyper-specialized Small Language Models (SLMs) for domain-specific intelligence.
Previously, he served as Director of AI Technology at Cerence AI, where he led the development and scaling of Generative AI and LLM-powered products from 0→1, enabling millions of user interactions across leading automotive OEMs, including Volkswagen, Audi, Toyota, Geely, JLR, and Ford, as well as Tier-1 suppliers. He also contributed to the development of Cerence’s automotive-grade LLM (CaLLM) and its deployment into premium infotainment systems, shaping next-generation voice-driven mobility experiences.
Dr. Singh holds a Ph.D. from the University of Bonn as a Marie Curie Fellow under Prof. Dr. Sören Auer. He later led Conversational AI research at Fraunhofer IAIS, Sankt Augustin in collaboration with Prof. Dr. Jens Lehmann. He has authored 50+ publications in top AI venues such as ICLR, AAAI, ACL, and The Web Conference, and holds two U.S. patents. His research focuses on knowledge distillation and Reinforcement learning for SLMs and graph representation learning, and he actively contributes in program committee of leading AI conferences.
Hinrich Schuetze is Professor for Computational Linguistics and co-director of the Center for Information and Language Processing at the University of Munich (LMU Munich). He received his PhD from Stanford in 1995 for his early research on embeddings (ACL Test-of-Time Paper Award). He then worked on natural language processing and information retrieval technology at Xerox PARC, at several Silicon Valley startups and at Google 1995-2004 and 2008/9. Hinrich is a coauthor of Foundations of Statistical Natural Language Processing (with Chris Manning) and Introduction to Information Retrieval (with Chris Manning and Prabhakar Raghavan). He was awarded a European Research Council Advanced Grant in 2017. Hinrich served as president of the Association for Computational Linguistics in 2020. He is a fellow of ACL, ELLIS and HessianAI.
Letitia Parcalabescu has an academic background in Physics and Computer Science, and holds a PhD in Computational Linguistics. Her doctoral research focused on benchmarking and interpreting the internal processes and explanations of multimodal AI models. Currently, she is an AI researcher at Aleph Alpha Research, working on training interpretable reasoning models by design, as well as curating and synthesizing data for large-scale pre-training. She created the “AI Coffee Break with Letitia” YouTube channel where she breaks down complex AI concepts. Topics range from newest research results in natural language processing, computer vision, to the broader societal impact of AI.