Dennis Fast

Dennis Fast

PhD Researcher

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EMAIL:

dfast@bht-berlin.de

PEOPLE

About

I work on transformer-based language models for biomedical and clinical text, with a focus on building robust, data-efficient systems that can be applied to real-world medical use cases. My background combines modern deep learning, signal processing and physics-based simulation, which helps me bridge traditional engineering methods and large-scale neural models.

My research aims to build clinical AI systems that do more than generate text - they provide transparent, verifiable and evidence-grounded explanations. I work on developing and evaluating language models and multimodal architectures that can extract clinical evidence, follow structured diagnostic reasoning, and provide clinicians with reliable, interpretable and domain-aware decision support.

I am open to collaborations on verifiable clinical reasoning, evaluation frameworks for medical LLMs, and multimodal clinical AI. If you work on related problems or would like to explore joint research in these areas, feel free to reach out.

Education

  • M.Sc. Data Science (BHT, Berlin): advanced statistics, machine learning methods and deep learning, specialisation in natural language processing (NLP).
  • B.Sc. Applied Mathematics & M.Sc. Computational Engineering (BHT, Berlin): advanced programming skills, numerical methods and scientific computing, specialisation in acoustic simulations.
  • Physical-Technical Assistant (Lise-Meitner-Schule, Berlin): programming skills, experimental methods and applied statistics in science.

Research Interests

  • Transformer models and representation learning for biomedical and clinical text
  • Clinical NLP: information extraction, summarisation and decision support
  • Evaluation and benchmarking of large language models in medical settings
  • Efficient and scalable training of domain-specific language models
  • Physics-based simulation and its intersection with data-driven modelling

Technical Skills & Methods

  • Machine learning and deep learning with PyTorch
  • Pretraining, domain adaptation and fine-tuning of large language models
  • Clinical text processing, terminology handling and outcome prediction
  • Design of benchmarks, evaluation protocols and error analyses for LLMs
  • Multimodal data handling (text, structured EHR data, imaging, audio)
  • Numerical simulation, signal processing and scientific computing

Teaching & Supervision

I teach core and advanced topics in NLP for Master’s students at BHT, covering the progression from classical representations to modern architectures. I also teach current advances in LLMs such as reasoning models, agentic systems, and collaborative model workflows.

I also supervise theses on clinical NLP, biomedical language models, clinical reasoning frameworks and applied ML for healthcare. My mentoring focuses on rigorous experimentation, reproducibility and clinically meaningful evaluation. If you have a concrete project idea for a bachalor or master thesis in this area, feel free to get in touch.

List of supervised theses:

  • Abdennaceur Mhadhbi. Clinical Reasoning in Large Language Models. Master Thesis, 2026.
  • Sebastian von Rohrscheidt. Evaluation reasoning capabilities of language model for clinical outcome prediction. Master Thesis, 2025.
  • Leif Bräg, Die unsichtbare Voreingenommenheit der KI-Prognose: Eine eingehende Untersuchung am Beispiel von CORe (The invisible bias of AI forecasting: An in-depth investigation based on the CORe model). Bachelor Thesis, 2024.

Current Research and Projects

Publications

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