Research
Deep Learning and Machine learning methods for biological systems, with a focus on graph representation learning and physics-informed models.
Research Interests
- Graph representation learning
- Computational biology & systems biology
- Protein dynamics and structure–function relationships
- Knowledge graphs and ontology-aware embeddings
- Scientific machine learning / physics-informed neural networks
Current Research
Physics-Informed Graph Networks for Protein Dynamics
Proteins are naturally represented as contact graphs between residues. Combining this structure with quantum-inspired neural PDE operators, we can predict per-residue flexibility and normal modes directly bypassing the computational cost of molecular dynamics simulation for many use cases.
This work is implemented in the LGNM server.
Deep Learning for Biomarker Identification
I build knowledge graphs from Gene Ontology data and Gene Ontology annotations, then apply GNN's architectures with attention-based interpretability. The goal is not just prediction accuracy but the ability to surface which molecular subnetworks a model relies on a prerequisite for clinical translation.
This work is implemented in the CABIgoWEB server.
Open Questions I Think About
- How do we make ontological hierarchies (like the Gene Ontology DAG) truly differentiable, so gradients can flow through them meaningfully?
- How do we fuse biological networks with fundamentally different noise profiles to say, high-confidence physical PPIs and noisy co-expression?
- Oversmoothing in biological GNNs: at what depth does message-passing on PPI networks become uninformative, and what structural priors resist it?
- Physics-informed vs. purely data-driven losses: when does the physics prior help, and when does it constrain the model into worse solutions?
- Evaluation bias: how much of biomarker discovery performance is really just recovering well-annotated, well-studied genes?