© Maximilian Waidhas / b-it Through interdisciplinary collaboration and cutting-edge research methodologies, we strive to advance the state-of-the-art in data science and language technologies, with a focus on addressing real-world problems and societal challenges.
For more information about our ongoing projects, publications, and opportunities for collaboration, please explore the respective sections of our website or reach out to our team members directly. We welcome inquiries from researchers, students, and industry partners interested in joining us on our mission to push the boundaries of knowledge and innovation in these exciting research areas.
© Unsplash We study how foundation models represent people, values, perspectives, disagreement, and social context. We develop methods and benchmarks for human modeling, value-sensitive behavior, social intelligence, pluralism, and alignment stability under personalization and post-training.
© Unsplash We develop efficient, explainable methods for training, adapting, compressing, and evaluating foundation models. Our work spans LLM architectures, multilingual representations, tokenizers, model distillation and domain adaptation, reasoning approaches, and benchmarking.
© Unsplash We build reliable domain-specialized machine learning frameworks and LLM-powered tools for scientific discovery. Our work focuses on uncertainty-aware AI for particle physics, astrophysics, and biomedicine, scientific agents, robust RDM systems, simulation reliability, and statistically valid AI-assisted workflows.
Artificial Intelligence is achieving increasingly impressive results in drug discovery. Yet as these models become more powerful, they also raise a fundamental scientific question: when do they produce reliable scientific…
The past decade of AI was largely driven by one question: how to make large language models work at all. How to scale them, stabilize them, and push their capabilities…
A major success for the b-it: The European Research Council (ERC) is providing millions of euros for projects in the fields of computer science, economics, and evolutionary biology. Prof. Dr.…
In the field of Data Perspectivism, perspective has emerged as an umbrella term encompassing annotators’ points of view and culturally shaped worldviews. When modeling annotators, researchers have explored a variety…
The study of harms in NLP is a fast-evolving field of research, which in a few years has seen the need of considering the subjectivity that characterizes this phenomenon. In…
The emergence of large language models has transformed the landscape of conversational systems, but our understanding of how users interact with these systems and what they seek to accomplish remains…
Parkinson's disease (PD) is a progressive neurodegenerative disorder with a lengthy prodromal phase that remains difficult to capture using traditional clinical tools. Most monitoring begins only after diagnosis, limiting insight…
The increasing burden of mental health disorders-including depression, anxiety, OCD, and suicidal ideation-necessitates the development of advanced Al frameworks capable of interpreting complex emotional signals from language. Our research focuses…
Amid the recent uptake of Generative Al, sociotechnical scholars and critics have traced a multitude of resulting harms, with analyses largely focused on values and axiology (e.g., bias). While value-…
Adversarial text-carefully crafted inputs designed to mislead or degrade the performance of NLP systems-poses a growing challenge across a range of language technologies. In this talk, I will present my…
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