Laurits Fredsgaard Larsen

About me

I’m a PhD candidate in Applied Mathematics and Computer Science at the Technical University of Denmark, specifically within the Cognitive Systems section. My research focuses on the development of Bayesian neural networks for molecular discovery.

I started my PhD, titled “Uncertainty Quantification for Graph Neural Networks”, in September 2023, and I’m supervised by Mikkel N. Schmidt (main supervisor) and Michael Riis Andersen (co-supervisor).

Research interests

My research focuses on uncertainty quantification for graph neural networks (GNNs) applied to molecular discovery. I aim to develop reliable machine learning models that can accelerate the development of new medicines. I am broadly interested in:

• Efficient Deep Ensembles: Deep ensembles are highly effective for uncertainty quantification, but their computational cost can be prohibitive. How can we develop more efficient ensemble methods?
• Scalable and Performant Bayesian Neural Networks: Bayesian neural networks offer a principled approach to uncertainty estimation, but scaling them to large datasets and complex models remains a challenge. How can we improve their scalability and performance?
• Uncertainty Quantification in Graph Neural Networks: GNNs excel at capturing complex relationships, but how can we effectively quantify their uncertainty? Can we leverage their unique architectural properties to develop novel uncertainty estimation techniques?
• Learning with Limited Labeled Data: Labeled molecule data is often scarce. How can we leverage unlabeled data, related modalities (e.g., omics, images, text), and prior knowledge to improve model performance?
• Integrating Machine Learning with Experimentation: How can we effectively integrate machine learning models into the experimental workflow (“lab-in-a-loop”) to accelerate the discovery process?

Background

While I’m now focused on machine learning, I have a diverse background spanning both hands-on experimental work and computational analysis. I hold degrees in both Molecular Biomedicine and Machine Learning and Data Science from the University of Copenhagen. I’ve gained experience in molecular biology, working with viruses, vaccines, and protein engineering.

Complementing my experimental skills, I worked as a bioinformatician at LEO Pharma during my studies, where I developed data analysis pipelines and built scalable systems for biological datasets, including experience with omics data and public APIs.