As an aspiring researcher in Bayesian Neural Networks and molecular property prediction, I am currently engaged in PhD research at the Technical University of Denmark (DTU), focusing on uncertainty quantification in Graph Neural Networks. My background combines a solid foundation in Molecular Biomedicine with advanced skills in Machine Learning & Data Science, positioning me at the intersection of computational science and biomedicine.

Current Research

PhD in Applied Mathematics & Computer Science, Technical University of Denmark (2023–Present)

  • Project: Bayesian Neural Networks for Molecular Discovery
  • Supervisor: Assoc. Prof. Mikkel N. Schmidt
  • Focus: Developing methodologies to improve uncertainty quantification in Graph Neural Networks, addressing critical challenges in drug and material development through advanced computational models.
  • Teaching: Teaching assistant for the courses MLOps and Advanced Machine Learning.

Prior Research and Work Experience

Python Programmer, Natural History Museum of Denmark (2023)

  • Developed tools for digitizing museum samples, contributing to the DaSSCo project.

Bioinformatics Student Assistant, LEO Pharma A/S, Ballerup (2021–2023)

  • Developed a scalable backend in Python integrating high-dimensional gene expression datasets.

Research Assistant, Center for Medical Parasitology, University of Copenhagen (2020)

  • Contributed to SARS-CoV-2 vaccine development, resulting in a publication in Nature Communications.


BSc Machine Learning & Data Science, University of Copenhagen (2020–2023)

  • Engaged in advanced coursework and research, including machine learning theory and deep learning, culminating in a strong GPA of 11.3.

MSc Molecular Biomedicine, University of Copenhagen (2018–2020)

  • Conducted comparative studies on vaccine development, leading to published findings in MDPI Vaccines.

BSc Molecular Biomedicine, University of Copenhagen (2015–2018)

  • Early research project at the Department of Molecular Virology, Osaka University.


  • Programming Languages: Python, R, C++
  • Technologies and Frameworks: PyTorch, scikit-learn, Django, Docker, Git
  • Data Science & Machine Learning: Data visualization, wrangling, analysis; supervised and unsupervised deep learning models; Bayesian approaches
  • Biology: Molecular biology, biochemistry, immunology
  • Languages: Fluent in Danish, English, and Japanese (Business level, JLPT N2)


  1. Fougeroux C, Goksøyr L, […], Fredsgaard L, […]: Capsid-like particles decorated with the SARS-CoV-2 receptor-binding domain elicit strong virus neutralization activity. Nature Communications. 2021 link
  2. Fredsgaard L, […]: Head-to-Head Comparison of Modular Vaccines Developed Using Different Capsid Virus-Like Particle Backbones and Antigen Conjugation System. MDPI Vaccines. 2021 link


  • Network Administrator, Industrikollegiet, Copenhagen (2019–present): Managed network infrastructure, ensuring reliable access and support for the community.
  • Treasurer, Industrikollegiet, Copenhagen (2022–2023): Oversaw budget and finances, contributing to the well-being and engagement of the dormitory community.

In my journey from molecular biomedicine to applied mathematics and computer science, I have been driven by a passion for leveraging computational methods to solve complex problems in the life sciences. I am eager to continue this path, contributing to interdisciplinary teams and advancing the frontiers of research in my field.