Bayesian Neural Networks for Molecular Discovery
Improving Uncertainty Quantification in Graph Neural Networks
Supervisor: Assoc. Prof. Mikkel N. Schmidt
Graph neural networks (GNNs) have revolutionized the analysis of molecular data and prediction of molecular properties, showcasing unparalleled predictive capabilities. However, one of the critical challenges with neural networks lies in their inability to provide reliable uncertainty estimates. This limitation becomes a significant hurdle in the domain of drug and material development, where uncertainty quantification is not just a measure of model confidence but a pivotal tool for guiding exploratory searches in vast chemical spaces.
Prior Research
Generative Models for Protein Sequence and Structure
Supervisor: Prof. Wouter Boomsma
In my research, I investigated the use of VAEs to capture the effects of mutations in protein sequences, inspired by the DeepSequence model, and also studied how the U-Net architecture can learn amino acid propensities from voxel representations of protein structures and “paint in” missing parts of the protein structure.
Next-Generation Vaccine Development Against SARS-CoV-2 (2020)
Contributed to developing a novel SARS-CoV-2 vaccine, leading to a publication in Nature Communications. The project’s success in clinical trials highlights the platform’s potential for rapid response to infectious diseases.
Enhancing Immunogenicity Through cVLP Vaccine Platforms (2018-2020)
Supervisor: Prof. Adam F. Sander
My master’s thesis explored the impact of cVLP backbones and conjugation systems on immune responses, demonstrating significant findings for vaccine design, published in Vaccines.